The Artificial Kidney: Physiological Modeling and Tissue Engineering (Tissue Engineering Intelligence Unit 3)

c-Myc Function in Neoplasia Chi V. Dang and Linda A. Lee, Johns Hopkins University

Functional Heterogeneity of Liver Tissue: From Cell Lineage Diversity to Sublobular Compartment-Specific Pathogenesis Fernando Vidal-Vanaclocha, Universidad del Pais Vasco

Endothelins David J. Webb and Gillian Gray, University of Edinburgh

Host Response to Intracellular Pathogens Stefan H.E. Kaufmann, Institute für Mikrobiologie und Immunologie der Universität Ulm

Cellular Inter-Relationships in the Pancreas: Implications for Islet Transplantation Lawrence Rosenberg and William P. Duguid, McGill University Anti-HIV Nucleosides: Past, Present and Future Hiroaki Mitsuya, National Cancer Institute Heat Shock Response and Organ Preservation George Perdrizet, University of Connecticut Glycoproteins and Human Disease Inka Brockhausen, Hospital for Sick Children—Toronto Exercise Immunology Bente Klarlund Pedersen, Rigshospitalet—Copenhagen Chromosomes and Genes in Acute Lymphoblastic Leukemia Lorna M. Secker-Walker, Royal Free Hospital-London Surfactant in Lung Injury and Lung Transplantation James F. Lewis, Lawson Research Institute Richard J. Novick, Roberts Research Institute Ruud A.W. Veldhuizen, Lawson Research Institute

Management of Post-Open Heart Bleeding Rephael Mohr, Jacob Lavee and Daniel A. Goor, The Chaim Sheba Medical Center

Premalignancy and Tumor Dormancy Eitan Yefenof, Hebrew University - Hadassah Medical School Richard H. Scheuerman, University of Texas Southwestern Myocardial Preconditioning Cherry L. Wainwright and James R. Parratt, University of Strathclyde Cytokines and Inflammatory Bowel Disease Claudio Fiocchi, Case Western Reserve Bone Metastasis F. William Orr and Gurmit Singh, University of Manitoba Cancer Cell Adhesion and Tumor Invasion Pnina Brodt, McGill University Cutaneous Leishmaniasis Felix J. Tapia, Instituto de Medicina-Caracas

Estrogen and Breast Cancer W.R. Miller, University of Edinburgh Molecular Mechanisms of Hypercoagulable States Andrew I. Schafer, University of Texas-Houston Organ Procurement and Preservation for Transplantation Luis Toledo-Pereyra, Michigan State University Liver Stem Cells Stewart Sell and Zoran Ilic, Albany Medical College

Cytokines in Reproduction: Molecular Mechanisms of Fetal Allograft Survival Gary W. Wood, University of Kansas

Skin Substitute Production by Tissue Engineering Mahmoud Rouabhia, Laval University

Computers in Clinical Medicine Eta Berner, University of Alabama

HIV and Membrane Receptors Dimitri Dimitrov, National Institutes of Health Christopher C. Broder, Uniformed Servives University of the Health Sciences

Immunology of Pregnancy Maintenance in the First Trimester Joseph Hill, Harvard University Peter Johnson, University of Liverpool

TEIU

The Artificial Kidney: Physiological Modeling and Tissue Engineering

3

Molecular Basis of Autoimmune Hepatitis Ian G. McFarlane and Roger Williams, King’s College Hospital

The Glycation Hypothesis of Atherosclerosis Camilo A.L.S. Colaco, Qandrant Research Institute

Cellular & Molecular Biology of Airway Chemoreceptors Ernest Cutz, University of Toronto

3

John K. Leypoldt

Hyperacute Xenograft Rejection Jeffrey Platt, Duke University Transplantation Tolerance J. Wesley Alexander, University of Cincinnati

TISSUE ENGINEERING I N T E L L I G E N C E U N I T

Interferon-Inducible Genes Ganes Sen and Richard Ransohoff, Case Western Reserve University

Inherited basement Membrane Disorders Karl Tryggvaso, Karolinska Institute

Artificial Neural Networks in Medicine Vanya Gant and R. Dybowski, St. Thomas Medical School— London

Cartoid Body Chemoreceptors Constancio Gonzalez, Universidad de Madrid

von Willebrand Factor Zaverio M. Ruggeri, Scripps Research Institute

Molecular Biology of Leukocyte Chemostasis Antal Rot, Sandoz Forschungsinstitut—Vienna

Immune Mechanisms in Atherogenesis Ming K. Heng, UCLA

Breast Cancer Screening Ismail Jatoi, Brook Army Medical Center

The Biology of Germinal Centers in Lymphoral Tissue G.J. Thorbecke and V.K. Tsiagbe, New York University

The Artificial Kidney: Physiological Modeling and Tissue Engineering

Gamma Interferon in Antiviral Defense Gunasegaran Karupiah, The John Curtin School of Medical Research—The Australian National University

p53 B Cells and Autoimmunity Christian Boitard, Hôpital Necker-Paris

LEYPOLDT

Myocardial Injury: Laboratory Diagnosis Johannes Mair and Bernd Puschendorf, Universität Innsbruck

R.G. LANDES

Genetic Mechanisms in Multiple Endocrine Neoplasia Type 2 Barry D. Nelkin, Johns Hopkins University

C O M PA N Y

MEDICAL INTELLIGENCE UNIT

R.G. LANDES C OM PA N Y

R.G. LANDES C OM PA N Y

TISSUE ENGINEERING INTELLIGENCE UNIT 3

The Artificial Kidney: Physiological Modeling and Tissue Engineering John K. Leypoldt, Ph.D. Research and Service Veteran Affairs Medical Center and Departments of Internal Medicine and Bioengineering University of Utah Salt Lake City, Utah, USA

R.G. LANDES COMPANY AUSTIN, TEXAS U.S.A.

TISSUE ENGINEERING INTELLIGENCE UNIT The Artificial Kidney: Physiological Modeling and Tissue Engineering R.G. LANDES COMPANY Austin, Texas, U.S.A. Copyright ©1999 R.G. Landes Company All rights reserved. No part of this book may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Printed in the U.S.A. Please address all inquiries to the Publishers: R.G. Landes Company, 810 South Church Street, Georgetown, Texas, U.S.A. 78626 Phone: 512/ 863 7762; FAX: 512/ 863 0081

ISBN: 1-57059-602-6

While the authors, editors and publisher believe that drug selection and dosage and the specifications and usage of equipment and devices, as set forth in this book, are in accord with current recommendations and practice at the time of publication, they make no warranty, expressed or implied, with respect to material described in this book. In view of the ongoing research, equipment development, changes in governmental regulations and the rapid accumulation of information relating to the biomedical sciences, the reader is urged to carefully review and evaluate the information provided herein.

Library of Congress Cataloging-in-Publication Data

The Artificial Kidney: Physiological Modeling and Tissue Engineering / John K. Leypoldt. p. cm. -- (Tissue engineering intelligence unit) Includes bibliographical references and index. ISBN 1-57059-602-6 (alk. paper) 1. Artificial kidney. 2. Hemodialysis. 3. Biomedical engineering. I Title. II. Series. [DNLM: 1. Hemodialysis--methods. 2. Biomedical Materials-therapeutic use. 3. Biomedical Engineering--methods. 4. Kidney, Artificial. 5. Models, Biological. 6. Tissue Culture--methods. WJ 378 L684p 1999] RC901.7.A7L49 1999 617.4'61059--dc21 99-33005 for Library of Congress CIP

PUBLISHER’S NOTE Landes Bioscience produces books in six Intelligence Unit series: Medical, Molecular Biology, Neuroscience, Tissue Engineering, Biotechnology and Environmental. The authors of our books are acknowledged leaders in their fields. Topics are unique; almost without exception, no similar books exist on these topics. Our goal is to publish books in important and rapidly changing areas of bioscience for sophisticated researchers and clinicians. To achieve this goal, we have accelerated our publishing program to conform to the fast pace at which information grows in bioscience. Most of our books are published within 90 to 120 days of receipt of the manuscript. We would like to thank our readers for their continuing interest and welcome any comments or suggestions they may have for future books. Michelle Wamsley Production Manager R.G. Landes Company

CONTENTS 1. Fluid Removal During Hemodialysis ....................................................... 1 John K. Leypoldt and Alfred K. Cheung Compartmentalization of Body Fluids and Ions ................................... 1 Determinants of Changes in Fluid Volumes During Hemodialysis .......................................................................... 4 Mathematical Modes of Sodium Kinetics and Fluid Distribution ........................................................................ 8 Modeling Changes in Blood Volume During Hemodialysis ............... 16 Conclusion ............................................................................................. 19 Notation ................................................................................................. 20 2. Urea Removal During Hemodialysis ..................................................... 25 Daniel Schneditz Urea Removal During Hemodialysis .................................................... 25 Transport and Elimination of Urea ...................................................... 25 Compartment Modeling ....................................................................... 34 The Inverse Problem ............................................................................. 45 Conclusion ............................................................................................. 51 Notation ................................................................................................. 54 3. Transport Kinetics During Peritoneal Dialysis ..................................... 59 Michael F. Flessner Peritoneal Anatomy ............................................................................... 60 Physiology of the Dialysis Transport Process ...................................... 60 Mathematical Models of Peritoneal Transport .................................... 76 Notation ................................................................................................. 86 4. The Bioartificial Renal Tubule ............................................................... 89 H. David Humes, Sherrill MacKay and Janeta Nikolovski In Vitro Development and Characterization of the RAD ................... 90 Ex Vivo Performance of the RAD ......................................................... 93 Conclusion ............................................................................................. 96 5. Percutaneous Access for Peritoneal Dialysis: A Tissue Engineering Approach ............................................................................ 99 Jennifer A. LaIuppa and Clifford J. Holmes PD Catheters ........................................................................................ 100 Tissue Response to Current PD Catheters ......................................... 100 Complications Associated with PD Catheters ................................... 102 Previous Attempts to Solve Access Complications ............................ 103 Tissue Engineering Approach to Reduce PD Catheter Complications ............................................................ 104 Surface Architecture ............................................................................ 105 Surface Treatments .............................................................................. 109 Growth Factors .................................................................................... 114 Conclusion ........................................................................................... 116 Notation ............................................................................................... 116

6. Tissue Engineering in the Peritoneal Cavity: Genetic Modification of the Peritoneal Membrane .......................................... 123 Catherine M. Hoff and Ty R. Shockley Gene Therapy ....................................................................................... 124 Peritoneal Membrane .......................................................................... 126 Genetic Modification of the Peritoneal Membrane ........................... 132 Potential for Improving Peritoneal Dialysis Through Genetic Modification .................................................................................... 136 Potential Limitations of Gene Therapy for Peritoneal Dialysis ..................................................................... 140 Formulation of a Successful Mesothelial Cell-Mediated Gene Therapy Strategy .................................................................... 141 Conclusion ........................................................................................... 141 Notation ............................................................................................... 141

EDITORS John K. Leypoldt, Ph.D. Research and Service Veteran Affairs Medical Center and Departments of Internal Medicine and Bioengineering University of Utah Salt Lake City, Utah, USA Chapter 1

CONTRIBUTORS Alfred K. Cheung, M.D. Medical Service Veteran Affairs Medical Center and Department of Internal Medicine University of Utah Salt Lake City, Utah, USA Chapter 1 Michael F. Flessner, M.D., Ph.D. Nephrology Unit University of Rochester Rochester, New York, USA Chapter 3 Catherine M. Hoff, Ph.D. Baxter Healthcare Corporation Renal Division, Scientific Affairs McGaw Park, Illinois, USA Chapter 6 Clifford J. Holmes, Ph.D. Baxter Healthcare Corporation Renal Division, Scientific Affairs McGaw Park, Illinois, USA Chapter 5 H. David Humes, M.D. Department of Internal Medicine University of Michigan Ann Arbor, Michigan, USA Chapter 4

Jennifer A. LaIuppa, Ph.D. Baxter Healthcare Corporation Renal Division, Scientific Affairs McGaw Park, Illinois, USA Chapter 5 Sherrill MacKay, B.S. Department of Internal Medicine University of Michigan Ann Arbor, Michigan, USA Chapter 4 Janeta Nikolovski, M.S. Department of Internal Medicine University of Michigan Ann Arbor, Michigan, USA Chapter 4 Daniel Schneditz, Ph.D. Karl-Franzens University Graz, Austria Chapter 2 Ty R. Shockley, Sc.D. Baxter Healthcare Corporation Renal Division, Scientific Affairs McGaw Park, Illinois, USA Chapter 6

PREFACE

P

rofessional engineering activities towards improving the artificial kidney continue to evolve and expand. Early research focused on machine and hemodialyzer development, with emphasis on producing dialysis delivery systems that were simple and practical, and on improving hemodialyzer efficacy for fluid and solute removal. These efforts were very successful; indeed, fluid and uremic toxins can now be removed during hemodialysis treatments at rates that are so rapid that the primary limitation to the removal process is the patient, not the artificial kidney. Biomedical engineers or physiologists (and nephrologists) with an engineering or mathematical bent are well-positioned to play a significant role in understanding these physiological limitations to fluid and solute removal. Mathematical modeling of fluid and solute removal during treatment by the artificial kidney has long been an important area of research. Early models led to a better understanding of the removal of uremic toxins by the dialyzer or the dialysis membrane; this endeavor could be called kinetic modeling of the dialysis membrane. Recent models have instead emphasized limitations to fluid and solute removal within the patient, an effort that can be called physiological modeling. The first three Chapters of this book detail the importance of understanding the physiological limitations to fluid and solute removal during treatment by the artificial kidney. The first Chapter describes fluid removal during hemodialysis and the importance of sodium kinetics and fluid distribution within body compartments to this removal process. Fluid removal remains a significant limitation to shortening hemodialysis treatment time. Too rapid fluid removal can lead to hypotension and other intradialytic symptoms that are uncomfortable to the patient. On the other hand, insufficient fluid and sodium removal may lead to chronic hypertension in these patients. The optimal fluid removal method for treating chronic hemodialysis patients remains to be described. Chapter 2 is an introduction to and a thorough description of the most recent concepts regarding the use of urea kinetic modeling during hemodialysis. The author of this Chapter, Daniel Schneditz, has recently made several meaningful contributions to this research area and has also made them practical for the clinical nephrologist. The importance of urea kinetic modeling to the practice of clinical dialysis cannot be underestimated. Today, all dialysis staff and patients know the meaning of the urea removal index, Kt/V, and its importance to the outcome of hemodialysis and peritoneal dialysis patients. It should be emphasized that urea Kt/V is derived directly from the engineering concept of a dimensionless parameter. Peritoneal dialysis is an alternative therapy for treating end stage renal failure patients, and Chapter 3 describes the physiological basis for solute removal during peritoneal dialysis. Over the past two decades, Michael F. Flessner and his colleagues have continued to remind the dialysis community that the peritoneum is not simply a hemodialysis membrane placed inside the patient. Indeed, it is a physiological transport barrier that has unique characteristics. It is

worth mentioning that the physiological modeling approach employed in both Chapters 2 and 3 was inspired by the early pioneering research of Robert L. Dedrick and Kenneth B. Bischoff. Their seminal paper (ref. 39 of Chapter 2) first introduced the concept of physiological modeling to the dialysis community and was the inspiration for the first part of this volume’s title. Chapters 4-6 describe the application of tissue engineering principles to further improve artificial kidney therapy. Chapter 4 describes the construction and initial testing of a novel device, a bioartificial tubule, that mimics the reabsorptive and metabolic functions of the native kidney. The ultimate goal of this research is to develop a bioengineered kidney consisting of both a bioartificial glomerulus and the described bioartificial tubule. In a sense, this work can be considered an extension of work by Lee W. Henderson, Clark K. Colton and others in the 1970s who developed a membrane device that mimicked glomerular ultrafiltration of blood, an artificial glomerulus. This work is in its infancy, but these investigators are creating a unique and potentially more natural way to remove uremic toxins. Chapters 5 and 6 describe two research programs that apply tissue engineering principles to improve peritoneal dialysis therapy. In contrast to the other Chapters in this book, these last two Chapters describe truly pioneering efforts into uncharted territory. Chapter 5 reviews the potential for developing improved peritoneal catheters that can modify the tissue response and promote normal tissue attachment to this implanted device. Such developments are crucial to further advance peritoneal dialysis as a long term renal replacement therapy. Chapter 6 describes the potential for genetically altering cells within the peritoneal cavity, specifically peritoneal mesothelial cells, to enhance peritoneal membrane function and extend its longevity as a dialyzing membrane. This area is particularly exciting and the potential for this research in the near future appears to be limited only by the imagination. This book was designed for use by bioengineering graduate students or professional bioengineers interested in undertaking research related to the artificial kidney. It is hoped that nephrologists will also find the topics and information in this book helpful to their interests. John K. Leypoldt January 1999

CHAPTER 1

Fluid Removal During Hemodialysis John K. Leypoldt and Alfred K. Cheung

T

he total volume and distribution of body fluids are highly regulated in normal individuals because of precise excretion of water and ions by the kidney.1,2 In end stage renal disease, control of fluid volume becomes progressively abnormal; at a certain stage, water and ions must be removed by dialysis therapy to maintain normal physiologic functions of the body, especially those of the heart and lungs. While the importance of fluid removal during dialysis therapy has long been recognized,3-5 we are entering a new era because of recent technical developments that permit more accurate evaluation of volume status in hemodialysis patients.6,7 This chapter reviews current understanding of the changes in body fluid distribution that occur during maintenance hemodialysis therapy. Emphasis is placed on the physiology of fluid distribution in chronic hemodialysis patients and on physiological models that describe fluid and sodium kinetics during hemodialysis. Physiological models can be defined as mathematical models that attempt to capture the underlying transport physiology and therefore aim to understand transport mechanisms; the extent of detail needed for a given model depends on the study goals or objectives.8 Kinetic modeling of body fluid distribution in hemodialysis patients focuses on describing the time dependence of body fluid compartment volumes and certain plasma solute concentrations, since many of these variables can be experimentally measured, are directly related to transport processes that limit fluid and solute removal and directly affect patient outcome. Two fundamental physiological transport processes that can limit fluid and solute removal are slow intercompartmental diffusion and blood flow. The current chapter will consider physiological limitations to fluid and sodium removal using models without flow limitations, since body fluids and ions are known to be highly compartmentalized into distinct anatomical entities.1,2 In contrast, the next chapter will emphasize compartmental, flow-limited models to describe urea removal during hemodialysis.

Compartmentalization of Body Fluids and Ions Body Fluids In the normal individual approximately 60% of body weight is water. Two-thirds of body water is intracellular, and one-third is extracellular. The extracellular compartment can be further subdivided into the interstitial and plasma compartments. The interstitial fluid volume is approximately three-fourths and plasma volume is approximately one-fourth of extracellular fluid volume. These relationships are illustrated schematically in Figure 1.1. There is significant interindividual variability in body composition even among normal individuals; the amount of total body water has been previously shown to depend on age,

The Artificial Kidney: Physiological Modeling and Tissue Engineering , edited by John K. Leypoldt. ©1999 R.G. Landes Company.

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The Artificial Kidney: Physiological Modeling and Tissue Engineering

Fig.1.1. Approximate distribution of fluid within the human body. The extracellular compartment is the sum of the intersitital and plasma compartments.

gender and body fat content. Altered physiological states, such as end stage renal disease, can also alter body fluid composition.9 Routine determination of body fluid composition is difficult. The volume of total body water, the extracellular compartment and plasma can be measured by determining the dilution of marker (usually RADiolabeled) solutes that are confined to the respective spaces. The accuracy of determinations of total body water by these methods is approximately ± 3% or ± 1.5 L. Extracellular fluid volume cannot be determined as precisely as total body water, because there does not exist an ideal marker solute that is rapidly distributed throughout this volume but completely excluded from cells. For example, estimates of extracellular fluid volume in normal male individuals as a percentage of body weight range from approximately 16% (using inulin or mannitol as marker) to 28% (using bromide as marker).9 Assuming total body water is 60% of body weight, these estimates of extracellular fluid volume correspond to 27-47% of total body water. Estimates of extracellular fluid volume using bioelectrical impedance analysis (Model 4000B, Xitron Laboratories, San Diego, CA) closely approximate the distribution volume of bromide10,11 and are therefore at the high end of these estimates. Intracellular fluid volume can only be determined by subtraction of extracellular fluid volume from total body water volume. The determination of total body water and extracellular fluid volumes in chronic hemodialysis patients may become increasingly routine with the improved development of techniques based on bioelectrical impedance analysis; however, these techniques are only currently employed in research settings. Most of current knowledge about body fluid distribution derives from studies on normal individuals or in patients without renal disease. In one of the few studies on renal failure patients, Bauer and Brooks12 found that body fluid composition of 10 well-nourished patients maintained on chronic hemodialysis therapy had blood, extracellular and total body water volumes that were similar to those of normal individuals. The only difference

Fluid Removal During Hemodialysis

3

observed was a high plasma volume during renal failure, presumably because of a change reciprocal to that of red cell mass in order to maintain normal blood volume. It is unclear from these data, however, if these fluid volumes are optimal for chronic hemodialysis patients.

Ions

The ionic composition of intracellular and extracellular fluids differs markedly.1,2 The intracellular compartment contains predominantly potassium as the cationic constituent and proteins and organic phosphates as anion constituents. The extracellular compartment contains predominantly sodium as the cationic constituent and chloride as the anionic constituent. Plasma differs from interstitial fluid in that it contains substantially more protein. These compositional differences are maintained by barriers or membranes, and the characteristics of the membrane separating the intracellular and interstitial compartments are materially different from that separating the interstitial and plasma compartments. The former barrier is the cell membrane and can be considered readily permeable to water but impermeable to all ions. The latter barrier is the capillary wall and can be considered readily permeable to water and small solutes but almost completely impermeable to large proteins such as albumin. For the purposes of this chapter, we will consider the cell membrane to be a perfect osmometer such that it is impermeable to all ions. In reality, these barriers are considerably more complex. The ionic composition of body fluid compartments is a major determinant of the volume of each compartment since these volumes are determined passively by maintaining osmotic equilibrium between the compartments. The volume of the extracellular compartment is determined largely by its sodium content (and its attendant anions). In the normal individual the kidney, largely under the influence of circulating antidiuretic hormone or vasopressin, maintains serum or plasma sodium concentration in a narrow range, 138-142 mEq/L,13 despite great variations in sodium and fluid intake. By regulating sodium and water excretion, the kidney maintains a constant extracellular fluid volume. Small differences in ionic concentration can be critical in ion and fluid kinetics during hemodialysis; therefore, the methods for measuring ionic concentrations, and a detailed understanding of the factors that influence these concentrations, are important.14-16 Methods for measuring ionic concentration have changed over the past decade. Previously, flame photometry was commonly used to measure ionic concentrations in routine clinical practice; this method measures the total concentration of an ion in a given volume. Most laboratories currently use ion selective electrodes for measuring concentration; this method measures ionic activity within the water content of the solution. Solute or ionic activity is a complex parameter; it generally assesses the ability of the ion to take part in electrochemical or physicochemical processes. For example, the difference in ionic activity across a barrier or membrane determines its diffusion rate or its osmotic effectiveness. Changes in ionic activity (as determined electrochemically) during hemodialysis may not, however, accurately reflect total ion removal.14 These issues can be illustrated by considering concentrations of sodium in plasma and dialysate as measured by flame photometry and ion sensitive electrodes.15 These alternative techniques give very similar values (within 1%) for sodium concentration in plasma because of two different counteracting factors. First, the concentration of sodium in plasma water is approximately 6% higher than that in plasma because of the excluded volume occupied by plasma proteins. Accounting for this factor alone, one might expect ion selective electrodes to yield values significantly higher than those determined by flame photometry. On the other hand, sodium is complexed with proteins and other small anions by approximately 5%; this lowers sodium activity by this same factor. Thus, these

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The Artificial Kidney: Physiological Modeling and Tissue Engineering

two factors approximately cancel each other in plasma. In dialysate, however, the absence of proteins requires that the dialysate activity of sodium, measured by ion selective electrodes, is 3-5% lower than that measured by flame photometry. In this review, we will neglect any further distinction between concentration and activity except in the numerical simulations (described below).

Determinants of Changes in Fluid Volumes During Hemodialysis In the end stage renal failure patient, the normal mechanisms for regulatory body fluid volumes and ionic composition are no longer present. In the absence of residual renal function, body fluids are largely removed only intermittently during hemodialysis therapy. One of the primary goals of hemodialysis therapy is therefore to alter patient volume status and body fluid distribution; the target is the so-called dry weight, which can be defined as the postdialysis body weight where the patient is in a state of normohydration. Because the dry weight of chronic hemodialysis patients cannot be directly evaluated, however, it is often defined simply to minimize the occurrence of clinical signs and symptoms indicating either overhydration or underhydration. It should be emphasized that rigorous evaluation of dry weight by these clinical criteria is difficult; nevertheless, we will assume in this chapter that this practice truly achieves a state of overall normohydration. In the ideal case, therefore, maintenance of dry weight during hemodialysis controls the volume of total body water. Fluid gain during the interdialytic interval is distributed into the intracellular and extracellular compartments based on the ratio of dietary sodium to fluid intake.17 For example, if fluid ingested during the intRADialytic interval contains sodium at the same concentration as the postdialysis extracellular fluid compartment, then the ingested fluid will remain entirely within the extracellular compartment. In this case the plasma sodium concentration will remain constant. If the fluid ingested between treatments has a sodium content higher than that contained within the postdialytic extracellular fluid compartment, then the ingested fluid will remain extracellular and will be accompanied by the movement of intracellular water into the extracellular compartment to dilute the excess sodium. If the fluid ingested between dialysis treatments has a low sodium content (the most typical case), then the ingested fluid will be distributed between both the intracellular and extracellular compartments. In the latter case, the plasma sodium concentration decreases during the interdialytic interval; for a fixed sodium intake, the decrease is greater the larger the amount of fluid ingested.17 Another goal of dialysis therapy is therefore to remove sufficient sodium to maintain a volume of extracellular fluid that normalizes blood pressure. Because sodium concentrations in plasma and dialysate are very similar, it is often difficult to determine sodium removal during routine therapy. In practice, the sodium concentration in dialysate is usually fixed to a value that is approximately the desired concentration in plasma after therapy, since equilibrium between plasma and dialysate sodium concentrations is assumed to be achieved by the end of the dialysis treatment. Simply removing a quantity of sodium equal to that ingested between dialysis treatments does not, however, guarantee maintenance of extracellular volume at normal values (see below).

Changes in Intracellular and Extracellular Fluid Volumes

Van Stone et al18,19 determined total body water volume, extracellular fluid volume and plasma volume using RADiolabeled isotopes of H2O, sulfate, and albumin and demonstrated that volume changes in these compartments during hemodialysis were a function of the dialysate sodium concentration. These investigators studied chronic hemodialysis patients at three different dialysate concentrations of sodium: one dialysate sodium concen-

Fluid Removal During Hemodialysis

5

tration was equal to that in the patient’s plasma, one was 7% less than that in patient plasma and one was 7% more than that in patient plasma. When patients were dialyzed at an ultrafiltration rate of 0.5 L/h using a dialysate sodium concentration equal to that in plasma, fluid was removed during hemodialysis predominantly from the extracellular compartment. When the dialysate sodium concentration was lower than that in patient plasma, fluid was also removed largely from the extracellular compartment; in addition, there was a significant shift of fluid from the extracellular to the intracellular compartment. When the dialysate sodium concentration was higher than that in patient plasma, fluid was removed from both the extracellular and intracellular compartments. This study also demonstrated that decreases in plasma volume due to either a high ultrafiltration rate or a slow vascular refilling rate were lessened when using a high dialysate sodium concentration. Since the use of a high dialysate sodium concentration had been previously shown to reduce hypotension and other symptoms during hemodialysis,20-22 these observations were consistent with the concept that these symptomatic events are volume-related. Subsequent studies showed that these observations are consistent with those predicted by mathematical models of sodium and fluid kinetics during hemodialysis. Kimura et al23 developed a model assuming that sodium and its accompanying anions were the only important osmotic substances in extracellular fluids and that the absolute amount of intracellular osmotic solutes remained constant throughout hemodialysis (see below for more details). This model accurately predicted changes in plasma sodium concentration during hemodialysis when patients were treated with low, normal and high dialysate sodium concentrations.23 Changes in intracellular and extracellular fluid volumes were also estimated by this model with reasonable accuracy, although the measured fluid shifts induced by low dialysate sodium concentrations were significantly smaller than predicted. While the above studies confirm that changes in intracellular and extracellular fluid volumes can be induced by altering dialysate sodium concentration, the conditions used in these experiments do not necessarily apply during maintenance hemodialysis where the dialysate sodium concentration for a given patient is often fixed. The body compartments from which fluid is removed under these latter conditions is instead determined by fluid and sodium intake during the interdialytic interval. This can be explained by assuming that the plasma sodium concentration is always reduced to approximately the same value at the end of each treatment regardless of the predialysis plasma sodium concentration. Consider first the patient who ingests fluid with a sodium concentration equal to that in the extracellular compartment. All ingested fluid will be confined to the extracellular compartment and the plasma sodium concentration will be equal to the dialysate sodium concentration. In this case, fluid will be removed only from the extracellular compartment; this case was experimentally verified by Bauer and Brooks.12 Consider next the same patient who ingests fluids with a sodium concentration less than that of the extracellular compartment. This fluid will be distributed in both the intracellular and extracellular compartments and the predialysis plasma sodium concentration will be less than the dialysate sodium concentration.17 Since the dialysate sodium concentration will be higher than that in plasma, fluid will be removed from both intracellular and extracellular compartments during each hemodialysis treatment. A corresponding scenario applies when this patient ingests fluid with a sodium concentration higher than that in the extracellular compartment. During routine hemodialysis, therefore, the compartment from which fluid is removed is determined by the composition of the fluid the patient ingests in the interdialytic interval.

Changes in Plasma Volume The amount of fluid removed during hemodialysis therapy is roughly equal to the total volume of plasma. If there were no mechanisms for refilling the plasma compartment, plasma

6

The Artificial Kidney: Physiological Modeling and Tissue Engineering

and blood volume during hemodialysis would drop to fatal levels. The rate of plasma or vascular refilling has been measured in a few studies and was reported to be between 0.25 and 0.56 L/h,24-26 values substantially less than the typical rate of extracorporeal fluid removal during hemodialysis. Therefore, plasma volume can decrease to a variable extent during hemodialysis depending on the vascular refilling rate. The factors that affect the plasma refilling rate are only partially understood. Keshaviah et al27 determined the decrease in plasma volume (calculated from increases in total plasma protein concentration) in uremic dogs and chronic hemodialysis patients undergoing isolated ultrafiltration. For a given ultrafiltration volume, the decrease in plasma volume was greater for higher rates of ultrafiltration. In chronic hemodialysis patients, there was considerable interpatient variability in the degree of intravascular volume depletion at a given ultrafiltration rate. These observations were further analyzed by a mathematical model based on Starling’s hypothesis of transcapillary fluid exchange where the rate of vascular refilling is governed by perturbations of Starling forces induced by the ultrafiltration process. The capillary ultrafiltration coefficient (Kv) was estimated by fitting the observed changes in total plasma protein concentration with predictions from the mathematical model. The Kv value calculated in 14 hemodialysis patients was 4.3 ± 0.7 (SEM) ml/min/mm Hg, a value comparable to that previously reported in the physiology literature. Low values of Kv indicated low vascular refilling rates and were associated with a greater degree of hypovolemia. Schneditz et al28 subsequently described a similar mathematical model, but included a small but finite concentration of protein in the fluid refilling the vascular compartment. These investigators estimated two separate parameters, the initial blood volume and Kv, by fitting their model predictions with observed changes in blood volume during hemodialysis after a short pulse of rapid ultrafiltration. The value of Kv estimated in the 13 patients was 5.3 ± 2.2 (SD) ml/min/mm Hg when normalized to a lean body mass of 50 kg, a value similar to that reported by Keshaviah et al. These investigators also showed that the estimated initial blood volume was 21% higher on average than that predicted from an anthropometric formula, suggesting the presence of fluid overload at the beginning of hemodialysis treatment. Furthermore, they showed that hypovolemia was less for patients with high values of Kv and for patients who were overhydrated. This latter result is consistent with observations by others29,30 that vascular refilling is enhanced in states of overhydration. The extracorporeal ultrafiltration rates employed in the studies by Keshaviah et al and Schneditz et al were greater (>1.2 L/hr) and the duration of ultrafiltration (30-120 min) was shorter than those typically observed during routine hemodialysis. These studies showed an exponential increase in vascular refilling during ultrafiltration because of the rapid increase in plasma oncotic pressure. Changes in blood or plasma volume during routine hemodialysis31-35 do not, however, follow the pattern expected from these experiments. The decrease in plasma volume during maintenance hemodialysis can be linear is some patients; in others, there is no significant decrease in plasma volume for the first 1 or 2 hours of treatment, after which plasma volume can decrease precipitously. The disparity between changes in plasma or blood volume during routine hemodialysis and those predicted by the models of Keshaviah et al and Schneditz et al are illustrated in Figure 1.2. This disparity between model predictions and measured changes in plasma volume during routine hemodialysis was described quantitatively by Tabei et al36 using a mathematical model based on Starling’s hypothesis. These investigators reported that Kv estimated in 14 patients was not constant during the treatment despite a constant extracorporeal ultrafiltration rate of 0.71 ± 0.05 (SEM) L/hr. Instead, Kv decreased dramatically, by a factor of approximately four, during the last three hours of the dialysis session. After one hour of hemodialysis Kv was 6.8 ± 1.3 (SEM) ml/min/mm Hg, and at the end of the session Kv was only 1.6 ± 0.2 (SEM) ml/min/mm Hg; these values bracket those reported by Keshaviah et

Fluid Removal During Hemodialysis

7

Fig. 1.2. A schematic illustration of intRADialytic changes in blood volume. Shown in the solid line with squares is the profile expected if intRADialytic increases in plasma oncotic pressure largely govern changes in blood volume as suggested by the studies of Keshaviah et al27 and Schneditz et al.28 Shown in the solid line and in the long and short dashed lines are three typical profiles of intRADialytic changes in blood volume observed in chronic hemodialysis patients.35

al and Schneditz et al. A significant concern with these mathematical models is that quantitative values for several assumed parameters cannot be directly measured in chronic hemodialysis patients. Sensitivity analysis of the assumed parameters by Tabei et al36 showed, however, that the decrease in Kv with time during the treatment was unlikely due to invalid estimates of the initial blood volume or arterial, venous or capillary blood pressure. In a separate preliminary report,37 these investigators showed that the decrease in Kv was not correlated with norepinephrine concentrations since continuos norepinephine infusions during hemodialysis only resulted in a relatively small decrease in Kv. Further analysis of these same data38 showed that the decrease in Kv closely correlated with plasma concentrations of both atrial natriuretic peptide (ANP) and cyclic guanosine 3',5'-monophosphate (cGMP). This observation led these investigators to postulate that the decrease in Kv during hemodialysis was due to a decrease in the water permeability of the capillary wall by ANP. An alternative explanation relates to the relationship between plasma concentrations of both ANP and cGMP and patient volume status or the hydration status of interstitial fluids.39-42 The intRADialytic decrease in ANP and cGMP likely reflects a reduction in hydration status of the interstitial compartment, a factor that has been previously shown to influence the vascular refilling rate.29,30 In these mathematical models such mechanisms would be observed as a decrease in calculated Kv values, since none of the above mathematical models accounts for changes in the hydration status or pressure in the interstitial space. Indeed, more complex mathematical models43 have reported that the capacitance of interstitial fluids can play an important role in altering intRADialytic changes in plasma volume. Alternatively, Lopot and Kotyk44 have argued that Kv only minimally influences intRADialytic

8

The Artificial Kidney: Physiological Modeling and Tissue Engineering

changes in plasma volume; instead, they suggest that the compliance of the cardiovascular system is the main determinant of intRADialytic changes in plasma volume. Nevertheless, these changes in vascular compliance must act by altering Starling forces to effect vascular refilling and intRADialytic changes in plasma volume. The above mathematical models have also neglected the effect of simultaneous changes in intracellular and extracellular volumes on intRADialytic changes in plasma volume. Since the plasma is part of the extracellular compartment, one should expect there to be a relationship, albeit complex, between intRADialytic changes in these compartmental volumes. For example, Van Stone et al19 measured changes in plasma, extracellular and total body water volumes using RADioisotopes in six patients during hemodialysis in the absence of ultrafiltration using three different dialysate sodium concentrations. Extracellular and plasma volumes decreased when using the low sodium dialysate (131 ± 1 (SEM) mEq/L); they remained relatively constant when using the normal sodium (141 ± 2 (SEM) mEq/L); and they increased when using the high sodium dialysate (153 ± 2 (SEM) mEq/L). The intRADialytic decrease in plasma volume when using the low sodium dialysate concentration in the absence of ultrafiltration was greater than 6%. These results show that changes in extracellular and intracellular fluid volumes may significantly impact on intRADialytic changes in plasma volume, especially if the latter are less than 10%.

Mathematical Modes of Sodium Kinetics and Fluid Distribution The experiments and models describing changes in fluid volumes during hemodialysis described in the above section have had a major impact on the treatment of chronic hemodialysis patients. Dialysate sodium concentrations have been increased over the past two decades to reduce intRADialytic hypotension and other volume-related symptoms; however, the importance of increasing dialysate sodium concentration on the volume of the extracellular fluid compartment has not been emphasized. We now examine this relationship using a mathematical model.

Models During Hemodialysis Mathematical models of sodium and fluid removal during hemodialysis were originally developed to determine whether changes in dialysate sodium concentration would increase the retention of sodium during hemodialysis therapy. Gotch et al45,46 first proposed a model of sodium and fluid removal during hemodialysis; this model is fundamental and is shown schematically in Figure 1.3. Body fluids are assumed to be distributed between the intracellular and extracellular compartments based on the concentrations of two different osmotic solutes. The extracellular osmotic solute is assumed to be sodium, and sodium is assumed to be distributed uniformly throughout and confined exclusively to the extracellular compartment. Another solute (e.g., potassium) is assumed to be the intracellular osmotic solute; this solute is assumed to be impermeable to cell membranes and therefore confined exclusively to the intracellular compartment. Fluid and solute balance equations for the intracellular and extracellular compartments are the following:45,46

dVi = K f (Ci − Ce ) dt

[Eq. 1.1]

Fluid Removal During Hemodialysis

9

Fig. 1.3. A schematic model of fluid and sodium kinetics during hemodialysis. Fluid is distributed between the intracellular and extracellular compartments with volumes Vi and Ve, respectively. The intracellular osmotic solute is confined to the intracellular compartment with a concentration Ci, and sodium is confined to the extracellular compartment with a concentration Ce. The extracorporeal ultrafiltration rate is denoted by Qf, Qs denotes the sodium removal rate and Kf denotes the whole body ultrafiltration coefficient.

dV e = −Q f − K f (Ci − Ce ) dt

[Eq. 1.2]

d(ViCi ) =0 dt

[Eq. 1.3]

d(V eCe ) = −Qs dt

[Eq. 1.4]

where: Vi and Ve = intracellular and extracellular volumes; Ci and Ce = intracellular osmotic solute and plasma sodium concentrations, respectively; Kf = the whole body (or cell membrane) ultrafiltration coefficient, a product of the permeability of cell membranes to water and the effective cell membrane surface area; Qf and Qs = the extracorporeal ultrafiltration rate and the extracorporeal sodium removal rate, respectively. The latter parameter can be calculated from the dialysance of sodium (D) by the following equation:45,46

The Artificial Kidney: Physiological Modeling and Tissue Engineering

10

Qs = [D(1− Q f / Qb ) + Q f ]Ce − D(1− Q f / Qb )Cd

[Eq. 1.5]

where: Qb and Cd denote the blood water flow rate and dialysate concentration of sodium. Eqs. [1.1]-[1.5] can only be solved numerically when appropriate initial conditions are specified. Certain algebraic modifications or simplifications to these equations were made by Gotch et al.45 First, Eqs. [1.1] and [1.2] can be added together to yield

dV e dVi dV T + = = −Q f dt dt dt

[Eq. 1.6]

where: VT denotes the sum of intracellular and extracellular volumes or total body water volume. This equation indicates that changes in total body water volume resulting from hemodialysis can be calculated as the amount of total ultrafiltered water or the change in body weight during the treatment. The second simplification made by Gotch et al was to replace extracellular volume with total body water volume in Eq. [1.4]. This was assumed valid because measurements of the apparent or osmotic volume of distribution for sodium approximate total body water, not extracellular volume.47 With this assumption, Eq. [1.4] becomes

d(V TCe ) = −Qs dt

[Eq. 1.7]

The two compartment model for sodium and fluid kinetics was therefore transformed into an apparent single compartment model. Eqs. [1.5]-[1.7] were then solved analytically to yield the following equation describing the dependence of plasma sodium concentration on time during hemodialysis:

(C (t) − C ) / (C (0) − C ) = [V e

d

e

d

D(1/ Q f −1/ Q b )

T

(t) / V T (0)]

[Eq. 1.8]

This equation was shown to accurately predict the postdialysis plasma sodium concentration from the initial plasma sodium concentration, the dialysate sodium concentration, the initial volume of total body water (assumed to be equal to the urea distribution volume), the hemodialysis operating conditions and the change in body weight

Fluid Removal During Hemodialysis

11

during hemodialysis. Petitclerc et al48 derived Eq. [1.8] using a similar approach; however, these investigators assumed that VT could be substituted for V e in Eq. [1.4] because the intracellular osmotic solute was impermeable to the cell membrane. While this latter approach simplifies the model, it does not identify the key assumption in the derivation of Eq. [1.8]. Kimura et al23 derived a two compartment sodium kinetic model in finite difference, instead of differential, form that is essentially equivalent to that derived by Gotch et al. Instead of assuming that the apparent sodium distribution volume could be substituted for the true extracellular distribution volume for sodium, these investigators assumed that osmotic equilibrium was maintained at all times between intracellular and extracellular fluids. These investigators then solved the finite difference equations using a computer and showed that the change in plasma sodium concentration during hemodialysis using low, normal and high dialysate sodium concentrations could also be well predicted by their model. This result is not very surprising since the models of Gotch et al, Petitclerc et al and Kimura et al are all equivalent, as we now show. The solution to Eqs. [1.1]-[1.5] depends significantly on the parameter ε, which is defined as

ε = D / K fCd

[Eq. 1.9]

This parameter is a ratio of the time scale for osmotic equilibration between the intracellular and extracellular compartments (τoe = VT(0)/KfCd) and the time scale for sodium diffusional equilibrium within the extracellular compartment (τde = VT(0)/D) during hemodialysis. When ε is small, then osmotic equilibrium is rapid, as assumed by Kimura et al. We have shown elsewhere using perturbation analysis49 that when ε is very small or zero, Eq. [1.1] can be approximated by

Ci = Ce

[Eq. 1.10]

Substituting Eq. [1.10] into [1.3] and adding together Eqs. [1.3] and [1.4], the result is Eq. [1.7]. This analysis shows that the assumption that the apparent distribution volume for sodium can be used to evaluate changes in plasma sodium concentration45 is equivalent to that assuming rapid osmotic equilibration between the intracellular and extracellular compartments.23 The amount of sodium removal during a treatment session can be simply calculated using the above model from the change in plasma sodium concentration and the change in total body water (i.e., the apparent sodium distribution volume).50 It should be noted, however, that this model cannot be used to determine total body content of sodium, VeCe, since it does not provide a unique value for extracellular fluid volume. The inability of the above model to uniquely predict intracellular and extracellular fluid volumes in absolute terms limits, to some extent, its routine application. More complex models of sodium kinetics that attempt to more accurately determine changes in plasma sodium concentration during hemodialysis have been recently formulated. Heineken et al51 measured changes in plasma sodium concentration during hemodialysis when the dialysate sodium concentration was abruptly changed to very high (170 mEq/L) and to very low (110 mEq/L) values. They showed that the changes in plasma sodium concentration could not be explained using a single compartment sodium kinetic model assuming rapid osmotic equilibrium between the intracellular and extracellular fluid compartments. To fit the experimental data, these investigators developed a more complex

12

The Artificial Kidney: Physiological Modeling and Tissue Engineering

model assuming that both sodium and urea were osmotically active across the cell membrane and reported excellent agreement between model predictions and experimental measurements of plasma sodium concentration. A surprising result was that the estimated value of the whole body ultrafiltration coefficient or Kf was exceedingly low (0.22 ml/min/ mm Hg); indeed, this value is even less than previous estimates of the capillary ultrafiltration coefficient of approximately 5 ml/min/mm Hg (see above). Calculation of such a low value was likely due to their assumption that urea is equivalent to sodium as an osmotic solute across cell membranes. This is not a valid assumption, since urea is highly permeable across cell membranes and its osmotic reflection coefficient across the cell membrane is much less than one, even though integral membrane transport proteins are usually required for movement of urea across cell membranes. On the other hand, the osmotic reflection coefficient for sodium should be approximately equal to one. It should be emphasized that direct experimental evidence for a significant osmotic effect of urea on body fluid distribution during maintenance hemodialysis is lacking. Indeed, Van Stone et al19 found virtually no change in extracellular volume when chronic renal failure patients were dialyzed (causing a substantial and rapid decrease in plasma urea concentration) using an approximately isonatremic dialysis solution in the absence of ultrafiltration. While urea may play a significant osmotic role when its concentration is very high, as in the disequilibrium syndrome,52 the osmotic role of urea is likely small during routine hemodialysis. Ursino et al53 have used a similar kinetic model to describe sodium kinetics during hemodialysis using either a constant or variable dialysate sodium concentration, except they have assumed osmotic equilibrium across the cell membrane. A similar concern over this model exists regarding the validity of assuming that urea is an effective osmotic solute across cell membranes. Ahrenholz et al54 observed that intRADialytic changes in plasma volume were dependent on the dialysate sodium concentration. These investigators proposed a two compartment sodium kinetic model comprising the plasma and the rest of total body water that provided a good fit between theoretical predictions and their experimental observations. Their model assumed that differences in sodium concentration across the capillary wall induced an osmotic flow into plasma. This assumption is physiologically untenable, however, since the osmotic reflection coefficient for sodium across the capillary wall (except perhaps in the brain) is very small.55 Further experimental and modeling efforts are needed to clarify additional factors which alter fluid exchange between the intracellular and extracellular compartments during hemodialysis.

Models in the Interdialytic Interval In order to produce a model for illustrating the role of important dialysis and dietary parameters on plasma sodium concentration and body fluid distribution, a mathematical model of sodium and fluid kinetics in the interdialytic interval is necessary. Eqs. [1.1]-[1.4] and [1.6]-[1.7] remain valid during the interdialytic interval except that fluid intake, denoted by Nf, and sodium intake, denoted by Ns, replace and are opposite in sign to Qf and Qs, respectively. We also assume that Nf and Ns are constants and that changes in body sodium and fluid from sweat, stool and urinary losses are negligible. With these assumptions, changes in plasma sodium concentration and extracellular volume during the interdialytic interval can be described by the following equations:

Fluid Removal During Hemodialysis

13

Ce (θ ) =

V T (0)Ce (0) + Nsθ V T (0) + Nfθ

[Eq. 1.11]

V e (θ ) =

V e (0)Ce (0) + Nsθ Ce (θ )

[Eq. 1.12]

The variables in Eqs. [1.11] and [1.12] are functions of time during the interdialytic interval (θ). To calculate a time-averaged value of Ve, we have used the arithmetic mean of the postdialysis and predialysis values. While an analytical expression for time-averaged Ve can be derived by integrating Eq. [1.12] over the interdialytic interval, the result is algebraically complex, and we have not found that the algebraic expression differs by more than a small fraction of that calculated by simply averaging the predialysis and postdialysis values. This general approach for modeling sodium and fluid kinetics during the interdialytic interval is similar to that first described by Kimura and Gotch.17

Model of a Chronic Hemodialysis Patient A mathematical model describing sodium and fluid kinetics for a hemodialysis patient with constant sodium and fluid intake can be constructed by combining the above models during the intRADialytic and interdialytic intervals. For illustrative purposes, we assume that the patient was always dialyzed to the same postdialysis total body water volume of 42 L and that conventional hemodialysis was performed for 4 hours, 3 times per week. The blood water flow rate was assumed to be 350 ml/min, and the dialyzer and the dialysate flow rate were such that the sodium dialysance was 270 ml/min. These values are characteristic of a typical hemodialysis session using a dialyzer containing a membrane with surface area of 1.5-2.0 m2. In these simulations the plasma sodium concentration was not altered, but the dialysate sodium concentration was divided by 1.0323 to account for differences between sodium activity and concentration (see above). These treatment sessions were assumed to be symmetrically positioned throughout the week with equal interdialytic intervals, and the patient was simulated to steady state conditions over 15 consecutive treatment sessions using the above equations. Eq. [1.12] shows that this model is not truly a single compartment model, since the initial or target postdialysis extracellular fluid volume and plasma sodium concentration must be assumed. We have assumed these values as 14 L and 140 mEq/L, respectively. Because of these required assumptions, the calculated values of extracellular volume are not absolute in magnitude. Nevertheless, relative differences in extracellular volume effectively represent the effect of altering dialysis and dietary parameters on plasma sodium concentration and body fluid distribution. Several general conclusions regarding sodium and fluid kinetics can be made from experience with this model. First, sodium and fluid removal during hemodialysis equals sodium and fluid intake during the interdialytic interval. This relationship must hold because the patient is assumed to be at steady state. This does not imply, however, that the patient has a fixed postdialysis extracellular volume equal to the target value. Second, the compartment from which fluid is removed during hemodialysis depends on the relationship between the dialysate sodium concentration and the sodium concentration of fluids ingested during the interdialytic interval. If the sodium concentration of fluid ingested during the interdialytic interval is hypotonic compared with the dialysate sodium concentration, then fluid removal during hemodialysis will be derived from both the intracellular and extracellular compartments. If, however, the sodium concentration of fluid

14

The Artificial Kidney: Physiological Modeling and Tissue Engineering

ingested during the interdialytic interval is hypertonic compared with the dialysate sodium concentration, then fluid removal during hemodialysis will be derived exclusively from the extracellular compartment and there will be a concurrent flux of fluid from the extracellular to the intracellular compartment. These results concur with predictions stated above from general principles. Third, the value of the assumed urea removal index, Kt/V (see chapter 2), has only a minor effect on the calculated results. Figures 1.4-1.7 show examples of results of these simulations for fluid intake rates of 1-3 L/day, sodium intake rates of 2-8 g/day and dialysate sodium concentrations of 135-145 mEq/L. Figure 1.4 shows the intRADialytic decrease in extracellular volume predicted for a constant fluid intake of 2 L/day for several sodium intake levels and dialysate sodium concentrations. The intRADialytic decrease in extracellular volume is greater for higher sodium intake and lower dialysate sodium concentrations. This effect of dialysate sodium concentration on the intRADialytic decrease in extracellular volume is identical to that shown empirically by Van Stone et al. 18,19 Interestingly, the level of sodium intake has only a moderate influence on the intRADialytic decrease in extracellular volume until high levels (i.e., 8 g/day) are reached. Figure 1.5 shows the effect of these same dietary and dialysis parameters on time-averaged values of extracellular volume. Time-averaged extracellular volume is greater with a higher sodium intake and a higher dialysate sodium concentration. This example suggests that the effect of an increase in sodium intake by 3 g/day on timeaveraged extracellular volume is approximately equal to that for an increase in the dialysate sodium concentration by 5 mEq/L. Figures 1.6 and 1.7 show the effect of changing sodium and fluid intake on the intRADialytic decrease in and time-averaged extracellular volume at a fixed dialysate sodium concentration of 140 mEq/L. Figure 1.6 shows that the intRADialytic decrease in extracellular volume is greater for both higher sodium and higher fluid intake. These results are not unexpected for a steady state patient, since sodium and fluid removal rates increase in parallel with increases in sodium and fluid intake. The effect of fluid intake on timeaveraged extracellular volume shown in Figure 1.7 is minimal; actually, time-averaged extracellular volume decreases with higher fluid intake in this example. This is because excess body fluid remains predominantly intracellular and the higher fluid removal rate results in enhanced sodium removal. Similar results have been observed using other parameter combinations. These simulations show that treatment by hemodialysis leads to a decrease in extracellular volume and that this decrease is a complex function of sodium intake, fluid intake, and the dialysate sodium concentration. Although not demonstrated here, the intRADialytic decrease in extracellular volume is also a strong function of the extracorporeal ultrafiltration rate. These conclusions are consistent with clinical experience in the dialysis unit and reflect the necessity to remove the sodium and fluid ingested between hemodialysis sessions. The importance of low levels of sodium and fluid intake in ameliorating some of the adverse effects of the treatment, including large shifts in fluid volume, have long been advocated. The effect of these dietary and dialysis parameters on time-averaged extracellular volume contrast with those used to treat the routine hemodialysis patient. It is generally considered that the dialysate sodium concentration should be set to the target postdialysis plasma sodium concentration. If equilibration between the dialysate and plasma sodium concentrations and the prescribed postdialysis body weight were always achieved, then each and every dialysis treatment would lower extracellular volume to a fixed value that would be independent of sodium and fluid intake. Our simulations show, however, that true equilibration between the plasma and dialysate sodium concentrations is never truly achieved, even though the initial difference in concentration is never very large. Since plasma sodium concentration approaches the dialysate sodium concentration similarly to the manner

Fluid Removal During Hemodialysis

15

in which plasma urea concentration approaches zero (compare Eq. [1.8] with Eq. [2.25]), it is apparent that equilibration is only 60-70% complete. Further, a small difference between plasma and dialysate sodium concentrations can produce a significant change in extracellular volume. Because of this disequilibration between plasma and dialysate sodium concentrations at the end of treatment, this analysis shows that both sodium and fluid intake rates can influence the volume of extracellular fluids immediately postdialysis and during the interdialytic interval, even though a constant postdialysis body weight is achieved.

Control of Extracellular Volume and Blood Pressure The above simulations show that time-averaged extracellular volume is a complex function of sodium intake, fluid intake, dialysate sodium concentration and postdialysis body weight, yet the importance of these fundamental relationships is not well appreciated. This is rather surprising because of the known relationship between time-averaged extracellular volume and blood pressure in hemodialysis patients.56 Although the studies by Van Stone et al18,19 showed that the use of high dialysate sodium concentration resulted in increased extracellular fluid volume at the end of hemodialysis, the importance of such a high postdialysis extracellular volume in contributing to hypertension is generally not acknowledged. Indeed, the high dialysate sodium concentrations routinely used to maintain hemodynamic stability during hemodialysis treatments can lead to increased thirst57 and higher blood pressure, especially in hypertensive patients.58 The latter observation is likely a direct result of the increase in time-averaged extracellular volume when increasing dialysate sodium concentration as shown in the above simulations (Fig. 1.5). Further evidence for such a relationship has recently been reported in studies reporting an effect of dialysate sodium concentration on the blood pressure of chronic hemodialysis patients.59,60 It is important to note that fluid intake per se has little effect on time-averaged extracellular volume. Although fluid and sodium intake rates are often correlated in chronic hemodialysis patients, this model shows that sodium intake, not fluid intake, largely determines time-averaged extracellular volume and presumably blood pressure. Recognizing that rigorous control of extracellular volume is a mainstay of hemodialysis therapy, the above simulations suggest that dialysate sodium concentrations should ideally be individualized for each patient, depending on sodium intake and fluid intake. One approach for individualizing dialysate sodium concentration would be to achieve a target postdialysis plasma sodium concentration, perhaps by noninvasive monitoring of plasma water conductivity.61,62 This approach would only produce a target postdialysis extracellular fluid volume, not a time-averaged value. It would be possible using a mathematical model to extend this approach to target time-averaged extracellular fluid volume if sodium intake could be routinely determined by measuring predialysis and postdialysis plasma sodium concentrations.50 A difficulty with any such approach is that the target extracellular fluid volume for each patient would not be known a priori and would need to be determined by incrementally decreasing the dialysate sodium concentration. A decrease in dialysate sodium concentration to lower extracellular volume may, however, lead to poor hemodynamic stability during treatment; thus, lowering dialysate sodium concentration would have to be performed cautiously. Further, a strategy for decreasing dialysate sodium concentration would need to be empirical, perhaps until a practical mathematical model for predicting intRADialytic changes in blood volume can be developed. An alternative guide for lowering dialysate sodium concentration is direct measurement of extracellular fluid volume using multifrequency bioimpedance spectroscopy in these patients. This latter approach for optimizing dry weight (or more precisely, extracellular volume) determinations is currently being evaluated.63

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The Artificial Kidney: Physiological Modeling and Tissue Engineering

Fig. 1.4. IntRADialytic decrease in extracellular volume (ECV) for a hypothetical patient with a postdialysis total body water volume of 42 L after a 4 hour hemodialysis treatment using a dialyzer and dialysate flow rate such that sodium dialysance was 270 ml/min and the blood water flow rate was 350 ml/min. The patient was at steady state, and fluid intake was assumed to be 2 L/day. Results are shown for various dialysate sodium concentrations and for sodium intakes of 2 (black bars), 5 (white bars), and 8 (gray bars) g/day.

Modeling Changes in Blood Volume During Hemodialysis Mathematical modeling of changes in blood volume during hemodialysis is of high interest because this parameter is related to symptomatic hypotension and other intRADialytic morbid events7 and because of recent technical developments that permit continuous, real-time measurement of intRADialytic changes in blood or plasma volume.64 As discussed above, previous mathematical models do not accurately describe intRADialytic changes in blood volume during hemodialysis because they only account for changes in Starling forces in plasma during the treatment. Besides the Starling forces in plasma, changes in intracellular and extracellular fluid compartment volumes and changes in Starling forces in interstitial tissues are also important. Several additional factors described below have also been proposed as important in evaluating intRADialytic changes in blood volume. The details of modeling intRADialytic changes in blood volume are beyond the scope of this review. The most advanced mathematical models to date have not completely accounted for all of the above factors;43,65,66 this is an active and fruitful area for future research. To determine intRADialytic changes in blood volume from continuous measurements of hematocrit or plasma protein concentration, it must be assumed that there is no loss from or addition of red blood cells or plasma proteins within the circulation. For example, Yu et al67 showed that intRADialytic hypovolemia induced a release of red blood cells from the splanchnic, and perhaps the splenic, vascular beds that increased the total number of circulating red blood cells during treatment. This concern might be heightened in hemodialysis patients who are performing exercise during treatments, since exercise in normal individuals has been shown to induce a significant splenic release of red blood cells.68 A corresponding concern exists when monitoring plasma protein concentrations since it is

Fluid Removal During Hemodialysis

17

Fig. 1.5. Time-averaged extracellular volume (ECV) during the interdialytic interval for the patient and treatment schedule described in the legend to Figure 1.4.

known that plasma proteins, in particular albumin, are partially permeable across the capillary wall. Indeed, Schneditz et al28 suggested that there is a net increase in protein content within the circulation as the result of extracorporeal ultrafiltration due to a small, but finite, protein content in the fluid entering the circulation during vascular refilling. Incorporation of these effects into a mathematical model is difficult. A second, and perhaps more significant, concern when modeling intRADialytic changes in blood volume is the assumption that circulating red blood cells are evenly distributed throughout the vasculature. This assumption is clearly false, but the extent to which changes in blood flow distribution during hemodialysis alter hematocrit in different parts of the body is unknown. It has long been known that the hematocrit in the microcirculation is lower than that in large blood vessels and that the measured hematocrit varies among different organs.69 Any change in blood flow distribution, therefore, could produce a change in hematocrit in a peripheral blood vessel without any change in total blood volume. For example, Lundvall and Lindgren70 have recently shown that changes in hematocrit in a peripheral artery upon standing from the prone position can be analyzed as if the body behaves as a two compartment system. Changes in arterial hematocrit did not reflect overall changes in plasma volume since dependent tissues have higher blood flows than other tissues; thus, the true change in plasma volume was underestimated when calculated from the change in arterial hematocrit. While previous work has suggested that blood flow distribution to various organs is altered during hemodialysis,71 the effect of this phenomenon on intRADialytic changes in hematocrit measured in a peripheral blood vessel is unknown. Evidence suggesting these effects to be unimportant has recently been reported in preliminary form,72 where intRADialytic changes in blood volume measured from changes in total plasma protein concentration using ultrasound technology were not different from those measured from changes in hematocrit.

18

The Artificial Kidney: Physiological Modeling and Tissue Engineering

Fig. 1.6. IntRADialytic decrease in extracellular volume (ECV) for the patient and treatment schedule described in the legend to Figure 1.4. In this case, the dialysate sodium concentration was fixed at 140 mEq/L. Results are shown for various rates of fluid intake and for sodium intakes of 2 (black bars), 5 (white bars), and 8 (gray bars) g/day.

Despite the theoretical possibility, evidence to date does not support an important role for the addition of red blood cells or protein to the circulation nor for the importance of changes in blood flow distribution in governing intRADialytic changes in hematocrit or plasma protein concentration. As recently stated by Lundvall and Lindgren,70 “this problem (the validity of hemogloblin or hematocrit as a marker of changes in plasma volume) has resisted a solution for almost a century.” It appears that further discussion of this issue without additional data would be nonproductive. The present discussion suggests that two features are essential in the development of a future mathematical model of blood volume changes during hemodialysis. First, changes in Starling forces, both intravascularly and extravascularly, need to be included. Inclusion of the latter phenomena have only been incorporated into such models recently43,66 and need further development. Second, changes in extracellular volume need to be taken into account. The models of Kimura et al65 and Ursino et al53 attempt to account for this phenomenon, but the latter model likely does not accurately describe intRADialytic changes in extracellular volume for reasons described above. Only after these factors are added to the existing models of Keshaviah et al27 and Schneditz et al28 can the development of more meaningful and practical mathematical models occur.

Conclusion One of the main indications for hemodialysis therapy is the removal of excess body fluid and the accompanying extracellular sodium ions; yet, optimal methods for removing fluid and sodium during routine hemodialysis remain elusive. Mathematical models of fluid removal are coupled to those of sodium kinetics and require knowledge of the distinct compartmentalization of total body fluid and sodium content. This review shows that

Fluid Removal During Hemodialysis

19

Fig. 1.7. Time-averaged extracellular volume (ECV) during the interdialytic interval for the patient and treatment schedule described in the legend to Figure 1.6.

routine use of high dialysate sodium concentrations during maintenance hemodialysis is a double-edged sword. While high dialysate sodium concentrations improve hemodynamic stability during the treatment, they can also increase time-averaged extracellular volume and presumably blood pressure. This important role of dialysate sodium concentration in regulating time-averaged extracellular volume, however, is largely ignored in algorithms that attempt to optimize intRADialytic changes in blood volume.73,74 Further developments in modeling fluid removal and sodium kinetics are needed to show how to individualize dialysate sodium concentration and optimize intRADialytic hemodynamic stability without increasing time-averaged extracellular fluid volume.

Notation Cd Ce Ci D Kf Kv Qb Qf Qs SD SEM t Ve Vi

sodium concentration in dialysate sodium concentration in extracellular fluid and plasma concentration of the intracellular osmotic solute dialysance of sodium whole body ultrafiltration coefficient governing fluid movement between the intracellular and the extracellular compartments capillary ultrafiltration coefficient blood water flow rate in the hemodialysis circuit ultrafiltration rate in the hemodialysis circuit solute removal rate in the hemodialysis circuit standard deviation standard error of the mean time during the dialysis treatment extracellular fluid volume intracellular fluid volume

The Artificial Kidney: Physiological Modeling and Tissue Engineering

20

VT total body water volume Greek: ε dimensionless parameter defined in Eq. [1.9] θ time during the interdialytic interval τoe time scale for osmotic equilibrium between intracellular and extracellular compartments τde time scale for sodium diffusional equilibrium within the extracellular compartment

References 1. Pitts RF. Physiology of the Kidney and Body Fluids. 3rd ed. Chicago: Year Book Medical, 1974. 2. Valtin H, Schafer JA. Renal Function. 3rd ed. Boston: Little Brown and Company, 1995. 3. Hegstrom RM, Murray JS, Pendras JP et al. Two year’s experience with periodic hemodialysis in the treatment of chronic uremia. Trans Am Soc Artif Intern Organs 1962; 8:266-280. 4. Thomson GE, Waterhouse K, McDonald HP Jr et al. Hemodialysis for chronic renal failure. Arch Int Med 1967; 120:153-167. 5. Vertes V, Cangiano JL, Berman LB et al. Hypertension in end-stage renal disease. N Engl J Med 1969; 280:978-981. 6. Cheung AK. Stages of future technological developments in haemodialysis. Nephrol Dial Transplant 1996; 11[Suppl 8]:52-58. 7. Leypoldt JK, Cheung AK. Evaluating volume status in hemodialysis patients. Adv Ren Repl Ther 1998; 5:64-74. 8. Aris R. Mathematical Modelling Techniques. London: Pitman, 1978. 9. Fannestil DD. Compartmentation of body water. In: Narins RG, ed. Clinical disorders of fluid and electrolyte metabolism. 5th ed. New York: McGraw-Hill, 1994:3-20. 10. van Marken Lichtenbelt WD, Snel YEM et al. Deuterium and bromide dilution, and bioimpedance spectrometry independently show that growth hormone-deficient adults have an enlarged extracellular water compartment related to intracellular water. J Clin Endcrinol Metab 1997; 82:907-911. 11. de Lorenzo A, Andreoli A, Matthie J et al. Predicting body cell mass with bioimpedance by using theoretical methods: A technological review. J Appl Physiol 1997; 82:1542-1558. 12. Bauer JH, Brooks CS. Body fluid composition in chronic renal failure. Clin Nephrol 1981; 16:114-118. 13. Kumar S, Berl T. Sodium. Lancet 1998; 352:220-228. 14. Gotch FA, Evans MC, Keen ML. Measurement of the effective dialyzer Na diffusion gRADient in vitro and in vivo. Trans Am Soc Artif Intern Organs 1985; 31:354-357. 15. Locatelli F, Ponti R, Pedrini L et al. sodium kinetics and dialysis performances. Contrib Nephrol 1989; 70:260-266. 16. Flannigan MJ. Sodium flux and dialysate sodium in hemodialysis. Semin Dial 1998; 11:298-304. 17. Kimura G, Gotch FA. Serum sodium concentration and body fluid distribution during interdialysis: Importance of sodium to fluid intake ratio in hemodialysis patients. Int J Artif Organs 1984; 7:331-336. 18. Van Stone JC, Bauer J, Carey J. The effect of dialysate sodium concentration on body fluid distribution during hemodialysis. Trans Am Soc Artif Intern Organs 1980; 26:383-386. 19. Van Stone JC, Bauer J, Carey J. The effect of dialysate sodium concentration on body fluid compartment volume, plasma renin activity and plasma aldosterone concentration in chronic hemodialysis patients. Am J Kidney Dis 1982; 2:58-64. 20. Stewart WK, Fleming LW, Manuel MA. Muscle cramps during maintenance haemodialysis. Lancet 1972; i:1049-1051. 21. Port FK, Johnson WJ, Klass DW. Prevention of dialysis disequilibrium syndrome by use of high sodium concentration in the dialysate. Kidney Int 1973; 3:327-333.

Fluid Removal During Hemodialysis

21

22. Wehle B, Asaba H, Castenfors J et al. The influence of dialysis fluid composition on the blood pressure response during dialysis. Clin Nephrol 1978; 10:62-66. 23. Kimura G, Van Stone JC, Bauer JH et al. A simulation study on transcellular fluid shifts induced by hemodialysis. Kidney Int 1983; 24:542-548. 24. Kim KE, Neff M, Cohen B et al. Blood volume changes and hypotension during hemodialysis. Trans Am Soc Artif Intern Organs 1970; 16:508-514. 25. Rouby JJ, Rottembourg J, Durande JP et al. Importance of plasma refilling rate in the genesis of hypovolaemic hypotension during regular dialysis and controlled sequential ultrafiltration-hemodialysis. Proc Eur Dial Transplant Assoc 1978; 15:239-244. 26. Swartz RD, Somermeyer MG, Hsu CH. Preservation of plasma volume during hemodialysis depends on dialysate osmolality. Am J Nephrol 1982; 2:189-194. 27. Keshaviah PR, Ilstrup KM, Shapiro FL. Dynamics of vascular refilling. In: Atsumi K, Maekawa M, Ota K, eds. Progress in Artificial Organs-1983. Cleveland: ISAO Press, 1984:506-510. 28. Schneditz D, Roob J, Oswald M et al. Nature and rate of vascular refilling during hemodiaysis and ultrafiltration. Kidney Int 1992; 42:1425-1433. 29. Koomans HA, Geers AB, Mees EJD. Plasma volume recovery after ultrafiltration in patients with chronic renal failure. Kidney Int 1984; 26:848-854. 30. Wizemann V, Leibinger A, Mueller K et al. Influence of hydration state on plasma volume changes during ultrafiltration. Artif Organs 1995; 19:416-419. 31. de Vries J-PPM, Olthof CG, Visser V et al. Continuous measuremnt of blood volume during hemodialysis by an optical method. ASAIO J 1992; 38:M181-M185. 32. de Vries JPPM, Donker AJM, de Vries PMJM. Prevention of hypovolemia-induced hypotension during hemodialysis by means of an optical reflection method. Int J Artif Organs 1994; 17:209-214. 33. Bogaard HJ, de Vries JPPM, de Vries PMJM. Assessment of refill and hypovolaemia by continuous surveillance of blood volume and extracellular fluid volume. Nephrol Dial Transplant 1994; 9:1283-1287. 34. Leypoldt JK, Cheung AK, Steuer RR et al. Determination of circulating blood volume by continuously monitoring hematocrit during hemodialysis. J Am Soc Nephrol 1995; 6:214-219. 35. Lopot F, Kotyk P, Bláha J et al. Use of continuous blood volume monitoring to detect inadequately high dry weight. Int J Artif Organs 1996; 19:411-414. 36. Tabei K, Nagashima H, Imura O et al. An index of plasma refilling in hemodialysis patients. Nephron 1996; 74:266-274. 37. Tabei K, Sakurai T, Iimura O et al. Effect of noRADrenaline (NA) on water permeability coefficient (Lpp) in hemodialysis (HD) patients. [Abstract]. J Am Soc Nephrol 1994; 5:529. 38. Iimura O, Tabei K, Nagashima H et al. A study of regulating factors on plasma refilling during hemodialysis. Nephron 1996; 74:19-25. 39. Saxenhofer H, Gnädinger MP, Weidmann P et al. Plasma levels and dialysance of atrial natriuretic peptide in terminal renal failure. Kidney Int 1987; 32:554-561. 40. Lauster F, Gerzer R, Weil J et al. Assessment of dry body-weight in haemodialysis patients by the biochemical marker cGMP. Nephrol Dial Transplant 1990; 5:356-361. 41. Lauster F, Fülle H-J, Gerzer R et al. The postdialytic plasma cyclic guanosine 3':5'-monophosphate level as a measure of fluid overload in chronic hemodialysis. J Am Soc Nephrol 1992; 2:1451-1454. 42. Fishbane S, Natke E, Maesaka JK. Role of volume overload in dialysis-refractory hypertension. Am J Kidney Dis 1996; 28:257-261. 43. Ursino M, Innocenti M. Mathematical investigation of some physiological factors involved in hemodialysis hypotension. Artif Organs 1997; 21:891-902. 44. Lopot F, Kotyk P. Computational analysis of blood volume dynamics during hemodialysis. Int J Artif Organs 1997; 20:91-95. 45. Gotch FA, Lam MA, Prowitt M et al. Preliminary clinical results with a sodium-volume modeling of hemodialysis therapy. Proc Dial Transplant Forum 1980; 10:12-16.

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The Artificial Kidney: Physiological Modeling and Tissue Engineering

46. Sargent JA, Gotch FA. Principles and biophysics of dialysis. In: Jacobs C, Kjellstrand CM, Koch KM et al, eds. Replacement of Renal Function by Dialysis. 4th ed. Dordrecht: Kluwer Academic, 1996:34-102. 47. Wolf AV, McDowell ME. Apparent and osmotic volumes of distribution of sodium, chloride, sulfate and urea. Am J Physiol 1954; 176:207-212. 48. Petitclerc T, Man N-K, Funck-Brentano J-L. Sodium modeling during hemodialysis: A new approach. Artif Organs 1984; 8:418-422. 49. Leypoldt JK, Cheung AK. Extracellular volume in nocturnal hemodialysis. Semin Dia 1999; 12[Suppl 1]:S50-S54. 50. Kimura G, Van Stone JC, Bauer JH. The amount of sodium removed by hemodialysis. Am J Kidney Dis 1986; 8:253-256. 51. Heineken FG, Evans MC, Keen ML et al. Intercompartmental fluid shifts in hemodialysis patients. Biotechnol Prog 1987; 3:69-73. 52. Ross EA, Barri YMH. Hemodialysis. In: Tisher CC, Wilcox CS, eds. Nephrology. 3rd ed. Baltimore: Williams & Wilkins, 1995:228-240. 53. Ursino M, Coli L, La Manna G et al. A simple mathematical model of intRADialytic sodium kinetics: “In vivo” validation during hemodialysis with constant or variable sodium. Int J Artif Organs 1996; 19:393-403. 54. Ahrenholz P, Falkenhagen D, Hähling D et al. Measurements of plasma colloid osmotic pressure, total protein and sodium concentration during haemodialysis: Can single-pool sodium modelling explain the results? Blood Purif 1990; 8:199-207. 55. Chrone C, Levitt DG. Capillary permeability to small solutes. In: Renkin EM, Michel CC, eds. Handbook of Physiology, Section 2: The Cardiovascular System, Volume IV. Microcirculation, part 1. Bethesda: American Physiology Society, 1984:411-466. 56. Charra B, Bergström J, Scribner BH. Blood pressure control in dialysis patients: Importance of the lag phenomenon. Am J Kidney Dis 1998; 32:720-724. 57. Henrich WL, Woodard TD, McPhaul JJ Jr. The chronic efficacy and safety of high sodium dialysate: Double-blind, crossover study. Am J Kidney Dis 1982; 2:349-353. 58. Cybulsky AVE, Matni A, Hollomby DJ. Effects of high sodium dialysate during maintenance hemodialysis. Nephron 1985; 41:57-61. 59. Flanigan MJ, Khairullah QT, Lim VS. Dialysate sodium delivery can alter chronic blood pressure management. Am J Kidney Dis 1997; 29:383-391. 60. Sang GLS, Kovithavongs C, Ulan R et al. Sodium ramping in hemodialysis: A study of beneficial and adverse effects. Am J Kidney Dis 1997; 29:669-677. 61. Pedrini LA, Ponti R, Faranna P, et al. Sodium modeling in hemodiafiltration. Kidney Int 1991; 40:525-532. 62. Locatelli F, Di Filippo S, Manzoni C et al. Monitoring sodium removal and delivered dialysis by conductivity. Int J Artif Organs 1995; 18:716-721. 63. Katzarski KS, Divino-Filho JC, Bergstrom J. Importance of the removal of fluid excess on blood pressure control in hemodialysis patients. [Abstract]. J Am Soc Nephrol 1998; 9:254A. 64. Schneditz D, Levin NW. Non-invasive blood volume montoring during hemodialysis: Technical and physiological aspects. Semin Dial 1997; 10:166-169. 65. Kimura G, Van Stone JC, Bauer JH. Model prediction of plasma volume change induced by hemodialysis. J Lab Clin Med 1984; 104:932-938. 66. Ursino M, Innocenti M. Modeling arterial hypotension during hemodialysis. Artif Organs 1997; 21:873-890. 67. Yu AW, Nawab ZM, Barnes WE et al. Splanchnic erythrocyte content decreases during hemodialysis: A new compensatory mechanism for hypovolemia. Kidney Int 1997; 51:1986-1990. 68. Laub M, Hvid-Jacobsen K, Hovind P et al. Splen emptying and venous hematocrit in humans during exercise. J Appl Physiol 1993; 74:1024-1026. 69. Albert SN, Jain SC, Shibuya J et al. The Hematocrit in Clinical Practice. Springfield: Charles A. Thomas, 1965. 70. Lundvall J, Lindgren P. F-cell shift and protein loss strongly affect validity of PV reductions indicated by Hb/Hct and plasma proteins. J Appl Physiol 1998; 84:822-829.

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23

71. Chaignon M, Chen WT, Tarazi RC et al. Effect of hemodialysis on blood volume distribution and cardiac output. Hypertension 1981; 3:327-332. 72. Schneditz D, Chamney PW, Greenwood RN et al. Relative blood volume changes during hemodialysis (HD) measured by optical and ultrasonic techniques. [Abstract]. J Am Soc Nephrol 1997; 8:172A. 73. Bonomini V, Coli L, Scolari MP. Profiling dialysis: A new approach to dialysis intolerance. Nephron 1997; 75:1-6. 74. Santoro A, Mancini E, Paolini F et al. Blood volume regulation during hemodialysis. Am J Kidney Dis 1998; 32:739-748.

CHAPTER 2

Urea Removal During Hemodialysis Daniel Schneditz

U

rea evolved as the carrier for the excretion of nitrogen in most mammals, including man, probably because it is relatively inert, highly soluble in water, and highly permeable across membranes. These features are important for transport in biological systems. Urea appears in body water in significant amounts as a result of the synthesis and degRADation of proteins and enzymes which represent the dominant structural and functional entities of the organism. Urea is relatively stable and easily analyzed by different laboratory techniques.1 These characteristics make urea a unique marker for hemodialysis. In order to monitor hemodialysis using urea as a marker, the effects of hemodialysis on urea concentration in the blood, in the tissues, and in the whole body have to be known, and they will be discussed in this chapter. Solute removal from the body during dialysis is determined by flow and diffusion. The term dialysis implies that diffusion is the dominant mode of solute transport across the membrane within the artificial kidney. However, the transport of solutes from the tissues into dialysate, crossing a characteristic distance of meters, is governed largely by forced convection. In a multistep process, the overall rate of the process is limited by the slowest step in the sequence. Thus, in high efficiency dialysis the rate of small solute elimination is controlled by convective transport. The common cause of flow limitation is recirculation, which is extensively discussed in this chapter. The transport of substances added to the body, their distribution, transformation, and elimination are studied by pharmacokinetics. The tools to study and to quantitate elimination of substances such as urea during hemodialysis are the same as those used in pharmacokinetic modeling. Modeling of urea elimination is based on physical principles such as the conservation of mass and physical relations such as the law of diffusion. Analysis of the dynamic problem results in ordinary differential equations with initial conditions, usually in first-order form, but with associated algebraic equations. An initial discussion of physical relations and equations involved in compartment modeling is followed by a presentation of compartment models and their evolution, leading to current concepts of how to determine the amount of urea removal and the dose of delivered dialysis in everyday practice.

Transport and Elimination of Urea Basic Mechanisms Flow and transport of mass is a feature of life. The study of uptake and elimination of a wide range of substances lies at the very heart of physiology. The concept of transport is also linked to containment and selective transport either to obstruct or to facilitate transport of specific substances. It is important to recognize the scale of distances to be covered in a complex organism.2 The length scale ranges from nanometers to meters, a linear ratio of The Artificial Kidney: Physiological Modeling and Tissue Engineering, edited by John K. Leypoldt. ©1999 R.G. Landes Company.

The Artificial Kidney: Physiological Modeling and Tissue Engineering

26

109, and a volume ratio of 1027. The large range of dimensions involved in transport translates into a range of time constants which serve as estimates of time required for a given transient process to be effectively complete. Once characteristic time scales have been established, one can restrict attention to individual processes with response times of the same order. Those an order of magnitude faster can be treated as instantaneous, and those ten times longer can be assumed not to happen at all. Effective transport from the molecular to the cellular level, and from one organ to the other, requires a combination of transport mechanisms. Transport on the small scale is determined by diffusion.3,4 Diffusion The Brownian motion of molecules and particles in solution, which results from intermolecular collisions with the surrounding fluid, is the basis of transport at the molecular scale. This motion does not take any predictable direction and the movement is random, but particles under observation tend to move farther from their origin with increasing time and with increasing coefficient of diffusion (D, in cm2/s). If the molecules in solution are uniformly distributed, the net movement is zero. But, if the solute is not evenly distributed in the solution, there is a net movement of substance from regions of higher concentration to regions of lower concentration. Since diffusion is based on random movements of solutes, it is more probable in a statistical sense that solutes diffuse from regions of higher concentration to regions of lower concentration than in the opposite direction. The same process will be observed if a region of high solute concentration adjacent to a region of low solute concentration is separated by a membrane permeable to the solute. For a thin membrane (∆x), the diffusive flow (Jd, in g/s) of uncharged molecules across this membrane is determined by:

Jd = −

DA ⋅ ∆c ∆x

[Eq. 2.1]

where: ∆c = the difference in concentration of the solute on both sides of the membrane; A = the membrane surface area; D = the diffusion coefficient of the solute in the membrane. Convection As diffusion becomes exceedingly inefficient with increasing distance, nature has developed other mechanisms such as fluid flow for the transport of solutes to places where they are needed and where diffusion can take over the process of distribution.5 The treatment with the artificial kidney takes advantage of both diffusion and convection. Blood flow is used to move solutes from the microcirculation of the tissues to central parts of the circulation, to the extracorporeal circulation, and to the artificial kidney. Dialysate flow removes the solutes from the artificial kidney. Intracorporeal blood flow and blood flow distribution is controlled by physiologic mechanisms, whereas extracorporeal blood and dialysate flow is controlled by the dialysis machine. The distinction is important because a major limitation to increasing hemodialysis efficiency is determined by physiologic blood flow regulation and blood flow limitation. Even though the profile of particle velocities at steady flow in a rigid tube such as the extracorporeal blood line is inhomogeneous, changes in input concentration tend to even out because of lateral and longitudinal diffusion at points farther downstream from the

Urea Removal During Hemodialysis

27

inflow. Turbulence and pulsatility will increase cross-stream mixing so that the concentration sampled from the bloodstream at sufficient distance from the inflow can be assumed to be homogeneous. The convective solute flux (Jv, in g/s) can be approximated by

Jv = Qc

[Eq. 2.2]

where: c = solute concentration; Q = volume flow. Transport by convection not only occurs with blood flow but is also important when fluid is filtered through a membrane and solutes are carried with the fluid by solvent drag.

Aspects in Dialysis Extraction and Clearance Blood serves as a carrier for solutes to be extracted from the body by the artificial kidney (Fig. 2.1). As blood is brought into close contact with the tissues in the microcirculation and with dialysate in the artificial kidney by convection, solutes such as urea will tend to equilibrate across permeable membranes by diffusion. If urea concentrations are high in the tissues and low in the dialysate, there is net convective and diffusive flow of urea from the tissues to the dialysate. Blood is saturated in the microcirculation and cleared in the artificial kidney. The recirculating closed loop nature of convective transport between locations with diffusive transport is a characteristic for cardiovascular transport in general, and for hemodialysis in particular. The extraction (E) of solute from blood in the dialyzer is defined as the fraction of solute removed from the dialyzer relative to the amount of solute delivered to the dialyzer6

E=

Qin cin − Qout cout Qin cin

[Eq. 2.3]

where: cin and cout = urea concentrations; Qin and Qout = blood flows entering and leaving the system, respectively. The subscripts (in, out) refer to a view from the dialyzer. It follows from mass balance that extraction into the dialyzer (Ed) is equal to negative extraction from the tissue (Etis). A major component of the hemodialysis treatment refers to fluid removal from the overhydrated patient. Body fluid accumulating in the interdialysis interval must be removed from the patient during hemodialysis by ultrafiltration. The problems associated with fluid overload and fluid removal are discussed in chapter 1 of this book. Since the dialyzer membrane is permeable both to solute and to solvent, a certain amount of fluid will be filtered through the membrane when a given pressure gRADient is applied across the membrane. Dialysis membranes are manufactured to retain cellular components and large molecules such as proteins, and to allow the passage of small molecules such as urea. Except for components larger than the cutoff, the composition of ultrafiltrate is comparable to the composition of blood. A significant amount of solute is removed from the body by ultrafiltration without changes in solute concentration. The solute flux by ultrafiltration is a convective flux. Ultrafiltration is an integral part of hemodialysis. The difference between dialyzer blood inflow (Qin) and outflow (Qout) is given by the ultrafiltration rate (UFR):

The Artificial Kidney: Physiological Modeling and Tissue Engineering

28

Fig. 2.1. Extraction, diffusion, and convection during hemodialysis.

Qin − Qout = UFR

[Eq. 2.4]

and Eqn [2.4] can be rewritten as a sum of two expressions

E=

cin − cout UFR cout + ⋅ cin Qin cin

[Eq. 2.5]

where the first expression on the right side of Eq. [2.5] refers to extraction without ultrafiltration; the second expression refers to extraction due to ultrafiltration. Extraction of solute without ultrafiltration is often referred to as diffusive extraction, even though blood and dialysate flows determine overall solute transport over a wide range of dialyzer performance. Also, in different sections of the hollow fiber membrane, solute is transported with filtration and backfiltration because of changing hydrostatic and colloid osmotic pressure gRADients between the blood and the dialyzer compartment.7 Extraction by ultrafiltration is referred to as convective extraction. The contribution of convective to overall extraction is small, especially with high blood flows, but significant in quantitating the amount of solute removed. With constant ultrafiltration, UFR rarely exceeds 30 ml/min and with high efficiency hemodialysis where high ultrafiltration rates are accompanied by high blood flows, the filtration fraction UFR/Qb will be less than 10% in most cases. The extracted flow is defined as clearance (Cl):

Urea Removal During Hemodialysis

29

[Eq. 2.6]

Cl = Qin ⋅ E

Clearance is the equivalent flow of a reference fluid from which the solute is completely extracted. The choice of reference fluid is arbitrary, but may be important for the interpretation of clearance. In applications with the artificial kidney, the reference fluid may be whole blood, plasma, plasma water or blood water. However, if urea clearance is used to determine the urea distribution volume in a hemodialysis patient, the reference fluid must be blood water. Therefore, it is good practice to transform plasma urea concentrations to plasma water concentrations (correct for the plasma protein content) and to relate clearances to blood water flow.8 It follows from Eq. [2.5] and Eq. [2.6] that clearance consists of a diffusive and a convective component according to:

Cl = Qin

cin − cout c + UFR out cin cin

[Eq. 2.7]

Tissue The flow of solute between tissue and blood in the microcirculation depends on both convection and diffusion of solute. The system of flowing blood equilibrating with stationary tissue was studied by Renkin:9,10

Cl = Qb ⋅

cart − cven = Qb ⋅ 1 − e − PS Qb cart

(

)

[Eq. 2.8]

where: Qb = the blood (or blood water) flow; cart and cven = arterial and venous concentrations, respectively; Cl = the diffusive clearance according to Eq. [2.7]. The clearance in Eq. [2.8] can be interpreted as the hypothetical volume which comes into complete equilibration with the tissue fluid compartment per unit time. The dimension of the permeability ⫻ surface area product (PS) is that of a flow and represents the maximum capillary clearance possible for a given substance in a capillary bed of given permeability and surface area at infinite blood flow. At finite blood flows, a clearance smaller than this maximum will be realized (Fig. 2.2). Renkin’s formula was derived from experiments using 42K+ as a tracer added to the bloodstream and the tissue concentration (ctis) could be assumed as negligible in this special case. However, most of the time tissue concentration is not negligible (ctis ≠ 0) and the original relationship developed by Kety is a more general description of solute flow between the blood and the tissue:5,11

Qb ⋅

cart − cven = Qb ⋅ 1 − e − PS Qb cart − ctis

(

)

[Eq. 2.9]

30

The Artificial Kidney: Physiological Modeling and Tissue Engineering

Fig. 2.2. Tissue and dialyzer clearance. Larger increase in tissue clearance (Cl, Eq. [2.8]) than dialyzer clearance (Kd, Eq. [2.10]) with increasing blood flow at the same permeability ⫻ surface area product (PS = K0A = 100, 300, or 700 ml/min, Qd=1.5 ⫻ Qb).

Renkin’s formula represents a particular solution of Kety’s formula when ctis = 0. Eq [2.9] is helpful to understand the relationship between arteriovenous concentration difference (cart - cven) and the tissue to blood concentration difference (cart - ctis). Venous outflow concentration readily equilibrates with tissue concentration with small blood flows and high PS. In this case, transport of solute is flow-controlled. When blood flow increases relative to PS, the arteriovenous difference decreases and the transport of solute is diffusion-controlled. Artificial Kidney Compared to the microcirculation, where the stationary tissue is continuously exposed to a moving phase, both blood and dialysate are continuously replaced by fresh phase in the artificial kidney. Blood flow (Qb) and dialysate flow (Qd) continuously deliver and remove solutes from the blood and dialysate compartments separated by the dialyzer membrane in order to maintain a high concentration gRADient (∆c/∆x) to drive the diffusive solute flux. The constant exchange considerably increases solute flux across the membrane. If diffusion is fast compared to blood flow, solutes will equilibrate and the process becomes flowcontrolled. If diffusion is slow compared to blood flow, the process is diffusion-controlled (Fig. 2.2).

Urea Removal During Hemodialysis

31

Solute transport is determined by flow and permeability, with the special effect that both compartments are continuously exchanged in modern dialyzers. With countercurrent flow, dialyzer clearance (K) is given as:

K = Qb

1 − e W (1− Z ) Z − e W (1− Z )

[Eq. 2.10]

where: Z = Qb/Qd; W = K0A/Qb.12 Some common abbreviations have been adapted for hemodialysis where clearance and permeability ⫻ surface area product are usually abbreviated by K and K0A, respectively. The K0A defines the maximum clearance to be attained at infinite blood and dialysate flows. A comparison of Eq. [2.8] and Eq. [2.10] shows that with the same permeability ⫻ surface area product (K0A = PS), Renkin’s formula yields higher values than the clearance calculated for dialyzers at finite dialysate flows (Fig. 2.2). The difference can be explained by Renkin’s assumption of a zero tissue concentration (ctis=0), and of a finite solute concentration in the dialysate compartment in the derivation of Eq. [2.10]. The solute concentration in the dialysate compartment reduces the concentration gRADient between the blood and the dialysate and reduces clearance. Only when Qd is much greater than Qb, solute concentration in the dialysate compartment becomes very small, and Eq. [2.10] reduces to Renkin’s formula. The permeability ⫻ surface area product for urea is available for most commercial dialyzers, so that actual urea clearance can be calculated from blood water and dialysate flows.13 Typically, K0A values range from 350-1100 ml/min. Dialyzers with K0A values greater than 700 ml/min are considered high efficiency dialyzers. Listed K0A usually refer to tests done with aqueous solutions and in vivo blood water clearances tend to be lower than calculated from manufacturer data using Eq. [2.10]. Recirculation Recirculation is basic to hemodialysis. It is intrinsic to the process insofar as only a fraction of the volume is removed from the body per unit of time, cleared from urea, and returned to the body. If all the volume (V) were cleared in a single pass using a blood flow (Qb) and a clearance (K) the process would be complete at t = V/Qb. The extraction of solute from the volume using a single pass process (Es) is given by the extraction of the dialyzer (E, Eq. [2.3]):

Es = E =

K ⋅t V

[Eq. 2.11]

However, if blood flow is returned to the volume and perfect mixing is assumed, the extraction obtained with recirculating blood flow (Er) for the same duration is given as:

Er = 1 − e − Es

[Eq. 2.12]

The Artificial Kidney: Physiological Modeling and Tissue Engineering

32

The difference (Es - Er) increases with increasing dialyzer extraction (E). Even with a perfect dialyzer (Es = 1) the extraction of the mixed compartment (Er) only reaches 63% if the process is maintained for the same time t = V/Qb. Access and Systemic Clearance Access clearance (Kac) is the equivalent flow passing the access that is cleared from solute. Under normal conditions and without access recirculation (Rac) the concentration of solute such as urea is the same in blood entering the access (cac,in) and the dialyzer (cd,in), and access clearance is equal to dialyzer clearance. In the simplest analysis, access recirculation occurs when access blood flow is insufficient to meet the demands of the blood pump (Fig. 2.3). With local access recirculation, access clearance becomes smaller than dialyzer clearance, because a fraction of cleared blood returns to the inlet of the arterial line and dilutes the concentration of solute entering the extracorporeal circulation. A flow dependent concentration gRADient (fac) develops between the blood entering the access (cac,in) and blood entering the dialyzer (cd,in). It follows from mass balance that dialyzer clearance has to be corrected for the concentration gRADient between the access and the arterial line inflow which develops because of access recirculation. Access clearance is defined as a fraction of dialyzer clearance determined by the intra-to-extracorporeal solute gRADient (fac):

Kac = fac Kd

[Eq. 2.13]

The intra-to-extracorporeal solute gRADient (fac) depends on the degree of access recirculation (Rac), dialyzer clearance (Kd) and extracorporeal blood flow (Qb) according to:14

fac =

cd , in 1 − Rac = cac, in 1 − Rac ⋅ (1 − Kd Qb )

[Eq. 2.14]

Without access recirculation, fac = 1 and cd,in = cac,in. With a given recirculation (Rac) and high efficiency dialyzers, fac approaches values of (1 - Rac). A recirculation of 30% is not uncommon with reversed position of blood lines, and dialyzer inflow concentrations may only reach 70% of access inflow concentrations in this case.15 An important practical consequence derived from Eq. [2.14] relates to the effect of blood flow on access clearance in the presence of access recirculation. An increase in blood flow will increase dialyzer clearance, but may decrease access clearance in the presence of access recirculation.16 Systemic clearance (Ksys) is the equivalent flow cleared from systemic tissue compartments. The concentration of solute in blood entering the access (cac,in) is equal to arterial concentration (cart) in the peripheral and to mixed venous concentration (cven,mix) in the central venous access, respectively. Arteriovenous Access The peripheral arteriovenous access is established for chronic hemodialysis. However, access blood flow bypasses systemic tissue compartments, which causes a small but significant reduction in extracorporeal clearance. A fraction of access flow returns to the peripheral access without systemic equilibration because of compartment recirculation

Urea Removal During Hemodialysis

33

Fig. 2.3. Access and compartment recirculation. Concentration of solute entering the dialyzer is smaller than concentration entering the access with access recirculation (cd,in
(CPR).17 Venous blood returning from systemic tissue compartments is diluted by cleared access blood because of CPR. A flow dependent concentration gRADient (fCPR) is established between mixed venous blood (cven,mix) leaving systemic tissue compartments and arterial blood (cart) entering the access circulation. This dilution systematically reduces the solute flow from systemic tissues to the dialyzer. In analogy to the relation between dialyzer and access clearance (Eq. [2.13]), systemic clearance is defined as a fraction of access clearance determined by the arteriovenous solute gRADient (fCPR):

Ksys = fCPR Kac

[Eq. 2.15]

The arteriovenous solute gRADient (fCPR) depends on the ratio of access clearance to systemic blood flow (Qsys = CO - Qac):

fCPR =

cart cven, mix

=

Qsys + UFR Qsys + Kac

[Eq. 2.16]

34

The Artificial Kidney: Physiological Modeling and Tissue Engineering

With high efficiency hemodialysis where access clearance can be assumed in the range of 0.35 L/min, and with a systemic blood flow of 4 L/min, arterial solute concentrations will reach only 92% of mixed venous solute concentrations. Venovenous Access The venovenous access is used for acute treatment and when complications prevent the use of the more common peripheral vascular access. With the tip of the central venous catheter in the right atrium and without local recirculation, mixed venous blood from the different tissues entering the extracorporeal circulation is delivered to the dialyzer. Therefore, the concentration in dialyzer inflow blood (cd,in) equals the concentration in mixed venous blood (cven,mix) and there is no concentration gRADient between the intra- and extracorporeal system. Cleared blood returned to the cardiovascular system mixes with venous blood, and with the assumption that the concentration is not changed on its passage through the heart and the lung because of the small mass of these organs, the relationship between arterial and mixed venous blood is determined by access clearance and cardiac output:

K cart = cven, mix ⋅ 1 − ac   CO 

[Eq. 2.17]

It is this gRADient between arterial and venous blood which drives solute to flow from the tissue to the blood in the microcirculation.

Compartment Modeling The method of modeling used in the study of mass transport in the body is based on compartments.18 A compartment represents a homogeneous, uniform entity with variable in- and outflow. For example, the mass of urea contained in the body can be considered a compartment with regard to the uniform chemical characteristics of urea. When transport of mass is studied in the body, it is convenient to introduce one of the customary measures of mass concentration (c) such as the ratio of mass (m) to volume (V). Then the compartment is assumed to be perfectly mixed where the concentration takes the same value throughout the compartment volume without a delay. Still, compartment volumes do not necessarily correspond to anatomical spaces. Mathematically, compartments and flows are represented by integral and differential operators, respectively.

The Forward Problem Two problems, i.e., the forward and the inverse problem, have to be solved by mathematical modeling. The forward problem deals with finding the solution of differential equations chosen to describe the dynamic aspects of the system. With initial conditions and with given parameters, the state of the system can be modeled for any given instant. System parameters can be obtained from literature or from scaling.2 The differential equations involved in current urea kinetic modeling have been solved analytically, which considerably reduces the complexity of the problem.19-22 The forward problem resembles the prescription of dialysis when the physician is confronted with initial patient conditions such as excess body weight and uremia, and when treatment parameters and the duration of dialysis have to be chosen to achieve a prescribed dose of dialysis.

Urea Removal During Hemodialysis

35

Single Compartment Model Urea kinetic models attempt to predict concentrations in samples which are easily obtained during hemodialysis, such as in extracorporeal line blood and in the dialysate. The single compartment model for urea transport during hemodialysis, neglecting the small effects of urea generation and residual clearance, is given as:

−

dm = Qin cin − Qout cout dt

[Eq. 2.18]

where the decrease in urea mass (negative) is equal to the sum of urea inflow into the dialyzer (Qincin, positive) and urea outflow from the dialyzer (Qoutcout, negative) back to the compartment. Notice that urea transport is convective. Since urea is dissolved in body water and if this volume (V) is well mixed, the mean urea concentration (c) is given as c = m/V. Thus, Eq. [2.18] can be rewritten in terms of volume and concentration changes. With Eqs. [2.6] and [2.7], the decrease in urea mass is determined by dialyzer clearance (Kd) and the concentration of dialyzer solute inflow (cd,in) according to:

V

dc dV +c = − Kd cd , in dt dt

[Eq. 2.19]

The concentration in blood entering the dialyzer (cd,in) is not the same as mean compartment concentration (c) because of access and compartment recirculation, which lead to characteristic intra-to-extracorporeal and arteriovenous concentration gRADients (cven ≠ cd,in, Fig. 2.3). However, these gRADients can be considered by factors (fac, fCPR) to correct for reduced clearance. Thus Eq. [2.19] simplifies to:

Vdc + cdV = − Kcdt

[Eq. 2.20]

where K is systemic clearance as described above (Eq. [2.15]). If the volume is constant, cdV = 0. Integration of Eq. [2.20] within the limits (t = 0) and (t) leads to an exponential function relating the constant volume concentration ct at any given time t to the initial concentration c0, and to the ratio of K to V times t, the so-called “K-t-over-V”:

ct = c0 ⋅ e − Kt V

[Eq. 2.21]

However, hemodialysis is characterized by significant changes in total body water because of ultrafiltration, and therefore cdV ≠ 0. The change of volume (-dV/dt) is usually constant with current treatment modes and determined by ultrafiltration rate (UFR). Since V is no longer constant, it is convenient to introduce the relative volume (v) as a new variable:

The Artificial Kidney: Physiological Modeling and Tissue Engineering

36

v=

Vt V0 − UFR ⋅ t = V0 V0

[Eq. 2.22]

and to define its derivative (dv/dt = -UFR/V0). Substitution of (Vdc) in Eq. [2.20] by (-vdcUFRdt/dv) leads to:

dc dv =β c v

[Eq. 2.23]

where:

β=

K −1 UFR

[Eq. 2.24]

Integration within the limits (t = 0) and (t) leads to a power function relating the variable volume concentration ct at any given time t to the initial concentration c0, the ultrafiltration rate (UFR) and the clearance (K):

[Eq. 2.25]

where the relative volume (v) at time t is given by Eq. [2.22]. With isolated ultrafiltration, K = UFR so that v0 = 1, and ct = c0. Exponential and power functions represent the natural solutions for the constant and for the variable compartment volume problem, respectively. The relative volume vt varies between 1 at t = 0 and approximately 0.9 at the end of hemodialysis. For v ≈ 1, the difference between variable and constant volume ct is very small and the constant volume approach can be used to describe the variation in ct (Fig. 2.4). This is easier for many purposes. In general, the accuracy of any urea kinetic model must be compared to typical experimental errors, which are not limited to the biochemical measurement of blood urea concentration but which also depend on the sampling technique. The typical accuracy of enzyme-based assays is in the range of 1-3% and a similar error must be assumed to arise during sampling. Thus, the effects of ultrafiltration remain relatively small when compared to experimental errors. Overall solute removal, however, is significantly affected by ultrafiltration and a considerable error is introduced if the constant volume model is used to quantify the amount of urea removed during hemodialysis.

Interpretation of Kt/V The expression Kt/V appears as an exponent in the single compartment constant volume urea kinetic model. It expresses the quantity of clearance normalized to distribution volume received by a patient. Gotch and Sargent first recognized that this index provides a measure for hemodialysis received by a patient.23,24 Based upon a retrospective analysis of various treatment modes it was initially suggested that a Kt/V of greater than 1.0 represented an adequate amount of hemodialysis. The minimum Kt/V recently has been increased to 1.2.25 However, the expression Kt/V does not appear in other, more realistic models such as

Urea Removal During Hemodialysis

37

Fig. 2.4. Single compartment, constant and variable volume model. Model calculations for the single pool assumption (V0 = 40L, UFV = 4L, Qb = 400 ml/min, Kd = 300 ml/min, convective and diffusive K = 306 ml/min, t = 180 min) show a similar variation in compartment concentration (ct) for the constant and variable volume model, but a 7% difference in Kt/V calculated from Eq. [2.21] or Eq. [2.26]. The drop in compartment concentration is attenuated without equilibration of venous outflow (PS/Qsys = 0.7, fCPR = 0.947, fPS = 0.95).

the variable volume and two compartment models, and the interpretation of V in Kt/V is less clear in these models. If both sides of Eq. [2.21] are multiplied by constant volume (V), the same exponential relation applies to the variation of solute mass between t = 0 and t:

mt = m0 e − Kt V

[Eq. 2.26]

This relation can be rearranged to obtain an expression for Kt/V based on mass balance rather than on a clearance to volume ratio:

 m  Kt / V = − ln1 − d  m0  

[Eq. 2.27]

where md refers to the amount of solute removed from the compartment by the artificial kidney. In the constant volume model without urea generation rate, md is given by the

The Artificial Kidney: Physiological Modeling and Tissue Engineering

38

difference between m0 and mt, only. Kt/V, defined as a fractional change of solute mass, is independent of volume and volume changes. If clearance, volume, and the change in volume are known, the equivalent Kt/V for the variable volume problem is obtained by integration of the Kt/V function: t

Kt V =

K dt V t =0

∫

[Eq. 2.28]

Since:

Vt = V0 − UFR ⋅ t

[Eq. 2.29]

substitution of (dt) by (–dv/UFR) and assuming a constant clearance (K) integration between the limits t = 0 and t gives:

Kt / V = −

K V ln t UFR V0

[Eq. 2.30]

Systemic, single compartment Kt/V is obtained if dialyzer clearance is corrected for access and compartment recirculation.

Perfusion Model with Unequilibrated Outflow In the single compartment model it is assumed that tissue outflow is equilibrated. A different approach has to be followed if the outflow from the single compartment is not equilibrated because of limited membrane permeability.22 Introducing the approach developed by Kety5,11 to urea kinetic modeling and combining Eq. [2.9] and Eq. [2.18] leads to:

Vdctis + ctis dV = (cart − ctis ) ⋅ (1 − e − W ) ⋅ Qsys dt − cvenUFR

[Eq. 2.31]

where: W = PS/Qsys; Qsys and ctis = the perfusion and the concentration of the tissue compartment; cart and cven = the concentrations of arterial and venous blood entering and leaving the tissue compartment, respectively. During hemodialysis, arterial concentrations are smaller than venous concentrations because of extracorporeal clearance. The magnitude of the arteriovenous gRADient is given by the ratio between the cart and cven (Eq. [2.16]). The magnitude of the gRADient between the tissue and venous outflow concentration (fPS) is described by the ratio of the cven to ctis and can be obtained from combining Eqs. [2.9] and [2.16]:

fPS =

cven 1 − e−W = ctis 1 + fCPR ⋅ e − W

[Eq. 2.32]

Urea Removal During Hemodialysis

39

If PS is much larger than Qsys, such as for urea membrane permeability, exp(-W)→0 and venous outflow concentration (cven) will be equilibrated with tissue concentration (ctis). In this case fPS→1. Otherwise, fPS is smaller than unity and venous outflow concentration is lower than tissue concentration. Insertion of Eq. [2.32] into Eq. [2.31] and manipulation of Eq. [2.31] leads to an equation comparable to Eq. [2.23] with a new constant β’:

β ′ = ( fPS − 1) − ( fCPR fPS − 1) ⋅ (1 − e − W ) ⋅

Qsys UFR

[Eq. 2.33]

For large W, fPS→1, and β’→β (Eq. [2.23]).

Two Compartment Models In the constant volume single compartment model, urea concentration varies as a single exponential. In the variable volume model, urea concentration varies as a single power function. The difference in urea variation between these two models is small if volume changes remain in the range of 10%. However, experimental urea concentrations significantly deviate from a single exponential during applications with the artificial kidney.26 The deviation is observed at the beginning of dialysis but it is most obvious after the end of dialysis, where urea concentrations rapidly increase during postdialysis urea rebound (PDUR) (Fig. 2.5). Mathematical analysis of intRADialysis concentration changes reveal a double exponential variation in urea concentrations.27 The natural correlate for the double exponential variation is the two compartment model. The standard two compartment pharmacokinetic model is structured with a distribution of solute between a central and a peripheral compartment and with central elimination.28 Parameter identification using experimental data leads to a volume ratio close to two-thirds to one-third for central and peripheral compartments, respectively, which is comparable to the ratio of intra-to-extracellular volumes.29 Thus the compartments of the two compartment urea kinetic model were originally identified as extra- and intracellular volumes, and limited mass transfer between compartments was explained by cell membrane permeability. This interpretation is known as the cell diffusion model. If urea is removed from the central volume with limited intercompartmental urea transfer, central urea concentrations will drop at a faster rate than peripheral urea concentrations. At the end of dialysis, central urea concentration will be artificially low. The interest in two compartment kinetics, postdialysis urea rebound, and intercompartmental urea transfer coefficients is determined by the necessity to quantitate solute removal from pre- and postdialysis urea concentrations.13 The dose of dialysis is significantly overestimated from immediate postdialysis urea concentrations. The correct dose of dialysis (equilibrated Kt/V, KtVeq) is determined from equilibrated concentrations. But it takes 30 to 60 minutes for concentration gRADients to dissipate and for concentrations to equilibrate during the postdialysis rebound. Recent research focused on the quantification of rebound to predict equilibrated postdialysis concentration from model and treatment parameters. In early models, the concentration of solute in the central compartment was assumed to be identical to the concentration of solute entering the dialyzer. The assumption only applies for a central venous access and for situations where recirculation is absent. Otherwise, rebound may be very variable. When two compartment models are used to estimate intercompartmental urea transfer from intra- or postdialysis urea profiles without correcting for access and compartment recirculation, calculated transfer coefficients show a more than tenfold variability and range from 0.08 to 2 L/min.30,31

40

The Artificial Kidney: Physiological Modeling and Tissue Engineering

Fig. 2.5. Two compartment model. Variation of arterial and mixed venous concentration for a simulated 2 hour treatment. At the end of the treatment, concentrations increase during the postdialysis urea rebound (PDUR). The increase is smaller for mixed venous than for arterial concentration. Adapted from Schneditz,17 with permission from the International Society of Nephrology.

Modified Cell-Diffusion Model The classic two compartment model has been adapted to correct for differences between mixed venous, arterial, and dialyzer inflow concentrations which develop because of access and compartment recirculation (Fig. 2.6).21,32 The mathematical description of the serial two compartment model is given by the second order dynamic system:

d (Ve ce ) dV = − Kc (ce − ci ) − Kd cd , in − Kr ce − i ci + G dt dt

[Eq. 2.34]

d (Vc dV i i) = Kc (ce − ci ) + i ci dt dt

[Eq. 2.35]

Urea Removal During Hemodialysis

41

where: Vi, Ve, ci, and ce = the water volumes and the urea concentrations in the two compartments; Kc = the intercompartmental urea clearance; Kr = the residual clearance; Kd = the dialyzer clearance; G = the urea generation rate. The model is based on the following assumptions. The elimination process is characterized by dialyzer clearance (-Kdcd,in) and residual clearance (-Krce). It is assumed that urea is added to the central compartment (+G) at a constant rate and that volume is removed from both compartments. The volume and solute flow from the peripheral to the central compartment is assumed as isotonic (-dVi/dtci). In the approach by Schneditz et al it was assumed that the total distribution volume (V = Ve + Vi) varies linearly both during dialysis and between dialyses.32 Therefore, if the fraction Ve/V is constant, Eqs. [2.34] and [2.35] and can be transformed into a pair of linear inhomogeneous differential equations with constant coefficients (a11, a12, a21, a22): •

→

→

v ⋅ c = A ⋅ c + f (v)

[Eq. 2.36]

where:

 dce  •  ce  →  dv   a11 , A= c =  , c =  dci   a21  ci     dv 

→

 G ⋅ (Ve + Vi )  a12   , f (v) =  UFR ⋅ Ve  a22    0   [Eq. 2.37]

and where v is given by Eq. [2.22]. The vector Eq. [2.36] can be treated as an eigenvalue problem and solved analytically.32 If central and peripheral compartments are identified as extra- and intracellular volumes, the assumption that volume is removed from both compartments in proportionate amounts is unlikely to be true in practice. During hemodialysis, excess body water is predominantly removed from the extracellular compartment. However, the actual contribution of each compartment depends on electrolyte and osmolyte gRADients which develop throughout the body during hemodialysis and which are determined by the composition of the dialysate. If the ratio Ve/V is not constant, Eqs. [2.34] and [2.35] must be treated in a different way. An analytical solution using perturbation analysis was obtained by Smye.21 Regional Blood Flow Model The variability in the estimated intercompartmental urea transfer coefficient is greatly reduced if concepts of access and compartment recirculation are considered, such as in the modified cell diffusion two compartment model.33 But how do hemodynamic factors such as blood pressure and hematocrit, and metabolic factors such as patient temperature, relate to variable urea transfer between compartments?34-36 The distribution of tracers administered to the body by the blood stream is related to blood flow, diffusion through the extravascular space, and partition between tissue compartments such as fat and water. Even for highly diffusive substances such as water, distribution in total body water seems to be flow limited.37 A bolus of D2O or T2O added to the blood stream requires more than one hour to equilibrate in humans and larger animals. An explanation, consistent with both the rapid diffusion of solutes and a marked delay between injection and equilibration, assumes that the peripheral circulation is heterogeneous

42

The Artificial Kidney: Physiological Modeling and Tissue Engineering

Fig. 2.6. Modified cell-diffusion model. Adapted from Schneditz,32 with permission from the American Society of Nephrology.

rather than homogeneous. Other highly diffusible administered solutes, such as urea, are also supposed to rapidly distribute in the intracellular volume of well perfused organs and to slowly mix with intracellular and interstitial water of poorly perfused organs.38 The removal of urea is likely to follow the same principles. The importance of flow limitation and of heterogeneous flow distribution in the removal of solutes by the artificial kidney was first recognized by Dedrick, who applied concepts of physiologic pharmacokinetic modeling developed by Teorell and Bischoff to applications with the artificial kidney.39-41 While unappreciated at the time, physiologic modeling plays an important role in the current understanding of urea kinetics. The regional blood flow model introduced by Schneditz et al represents the application of flow and diffusion controlled urea transport mechanisms described in this chapter to a two compartment model (Fig. 2.7).22,42 Based on physiologic data on organ perfusion and organ water content, organ systems can be allocated to either a high (index H) or a low (index L) flow system (Fig. 2.8). Organ systems with a high blood flow to volume ratio such as the kidneys, the small organs, heart, brain, portal system, lungs, and the blood contain approximately 20% of total body water but are perfused by approximately 80% of the cardiac output. The organs of the locomotor system, the skin and the fat represent the low flow system which contains approximately 80% of total body water, but it is only perfused by 20% of the cardiac output. Although urea distribution and clearance is obviously flow controlled, a more general description including both diffusive and convective transport processes is given by:

[Eq. 2.38]

Urea Removal During Hemodialysis

d (VL cL ) dV = QL (cart − cven, L ) + L cven, L dt dt

43

[Eq. 2.39]

where: VH, VL, cH, and cL = water volumes and urea concentrations in the two compartments; QH and QL = the blood flows of the two compartments; Kr = the residual clearance; G = the urea generation rate. cven,H and cven,L are the solute concentrations in venous outflows from the two compartments, which are not necessarily equilibrated with tissue concentration (cH, cL). The model is based on the following assumptions. The elimination process is characterized by the arteriovenous concentration gRADient times compartment perfusion and residual clearance (-Krcart). The degree of venous outflow equilibration is determined by the ratio of permeability ⫻ surface area product to compartment perfusion as described in Eq. [2.9]. It is assumed that urea is introduced into the system through the high flow compartment (+G) at a constant rate and that volume is removed from both compartments in proportionate amounts. It is further assumed that the total distribution volume (V = VH + VL) varies linearly both during dialysis and between dialyses. Therefore, if the fraction VH/V is constant, Eqs. [2.38] and [2.39] can be transformed into a pair of linear inhomogeneous differential equations with constant coefficients comparable to Eqs. [2.36] and [2.37] and which can be solved by analytical techniques.22 Comparison of Two Compartment Models The similarity of equations developed for the serial cell diffusion and the parallel regional blood flow model suggests that the two models are comparable, if not equivalent, from a modeling and pharmacokinetic point of view. This rationale is acceptable for the purpose of prescribing and estimating the dose of dialysis for average patients and treatment modes. However, a physiologically based model has the potential of being extendable to a spectrum of physiologic stimuli and responses during hemodialysis. In both descriptions the model output is the concentration in central and peripheral compartments. Urea concentration in the central compartment is easily accessible if extracorporeal concentration is corrected for effects caused by access and compartment recirculation. The concentration in the peripheral compartment is not easily accessible. Intracellular concentration in red blood cells has been shown to equilibrate rapidly, but the red blood cell may not be a good representative of overall cell membrane permeability. However, it has been shown by Metry that urea concentrations sampled from the interstitial space of skin, i.e., the extracellular space, significantly lag behind the concentration measured in blood.43 The results of these studies support the regional blood flow model. In the regional blood flow model, QL refers to the perfusion of the low flow muscleskin-bone compartment. In the cell diffusion model Kc refers to the intercompartmental urea clearance. These parameters control the flow of solute from the peripheral to the central compartment during hemodialysis. When both models are used to identify QL and Kc, respectively, a strong linear relationship is obtained between these system parameters.32 The relationship between QL and Kc is helpful to understand the variability of Kc with different patients and with different treatment modes. Effects that reduce peripheral perfusion, such as ultrafiltration-induced hypvolemia should lead to enhanced postdialyis urea rebound and reduced effective clearance.44,45 Conditions that enhance peripheral perfusion, such as exercise, should lead to opposite effects. It was shown experimentally that intRADialysis exercise reduced rebound.46 Theoretical analysis of the regional blood flow model predicts that

44

The Artificial Kidney: Physiological Modeling and Tissue Engineering

Figure 2.7. Regional blood flow model. Adapted from Schneditz,42 with permission from the American Society for Artificial Internal Organs.

urea rebound can be abolished if QL is increased by 5 L/min by 30 minutes of exercise before the end of hemodialysis.47 In the cell diffusion model, the ratio of central to total body water volume is one-third compared to one-fifth in the regional blood flow model. In the regional blood flow model, urea generation rate is allocated to the central compartment and to the system which includes the splanchnic circulation and the liver. During hemodialysis most of excess body fluid is removed from the extracellular space. In the regional blood flow model the distinction between compartments does not refer to intra- and extracellular spaces. Both the high and low flow systems contain organ systems with corresponding amounts of intra- and extracellular volumes. In the regional blood flow model it is justified to assume that fluid is removed from both compartments in proportionate amounts.

The Inverse Problem Once the model structure has been specified, experimental data can be used to identify the model and relevant model parameters. For more information on this topic see recent reviews by Cobelli18 and Marmarelis.48 Both authors have published extensively in this area. The question arises whether experimental data contain enough information to estimate all the unknown parameters of the postulated model. It may turn out that the postulated model structure is too complex for the data available, and there is no way that the parameter can be estimated. For example, even the single compartment model is nonidentifiable if both clearance (K) and volume (V) are to be modeled from pre- and postdialysis urea concentrations. However, the ratio of clearance to volume (K/V) and Kt/V are easily obtained from these data (Eq. [2.21]). The experiment usually provides a limited set of real data, and parameter estimates are usually obtained by least squares techniques. The accuracy of the parameter is a result of

Urea Removal During Hemodialysis

45

Fig. 2.8. Specific perfusion of tissues. Tissues are listed according to their specific perfusion (Q/ V) and their water volume relative to total body water volume. Blood volume is not perfused but represents a significant water fraction and is allocated to the high flow system. Adapted from Schneditz,42 with permission from the American Society for Artificial Internal Organs.

various sources of error such as measurement error and error in model structure. The large variability in Kc modeled from postdialysis urea rebound is an example of model structure error. When model structure includes effects caused by access and compartment recirculation, the accuracy of modeled Kc increases.32,33

Model Parameters The sensitivity of each parameter with respect to its relative variation can be interpreted as the variation in model output in response to a defined change of this parameter. A high sensitivity relates to a large effect in model output, in this case urea concentration. On the other hand, if parameters such as G or Kc are estimated from urea concentrations, a large sensitivity relates to a higher accuracy in the estimation of the model parameter. For example, analysis of the cell diffusion model by Grandi for intra- and interdialysis phases shows that the sensitivity of G and V is larger than the sensitivity of Kc and Vi/Ve by one order of magnitude.49 While G and V can be identified with reasonable accuracy using appropriate sampling, accurate identification is difficult for Kc and Vi/Ve. In addition, the effect of a relative decrease in Vi on model output is comparable to an increase in Kc. The effects compensate. The accuracy of parameter estimates is maximized if urea concentrations are measured near maximum values of the sensitivity curve. For G and V maximum sensitivity is observed at the end of dialysis and at the beginning of the next dialysis treatment,

46

The Artificial Kidney: Physiological Modeling and Tissue Engineering

respectively. Therefore, samples should be taken pre- and postdialysis and before the beginning of the next treatment. According to the model used by Grandi, which is a simplification of the cell diffusion model presented here, a fourth sample taken 15 minutes into dialysis close to the maximum of the Vi/Ve curve provides the best estimate for the volume ratio of the two compartment system if Kc is assumed constant. When urea concentration is measured with a 2% error, V is estimated with an accuracy of approximately 5%, G with an accuracy of 4%, and the ratio Vi/Ve with an accuracy of 34%. The accuracy in the estimation of each parameter can be improved if more samples are taken. But since sampling is limited to the intRADialysis period, the accuracy in the estimation of G does not really improve. In the single pool model, three samples are required to estimate G, and V using numerical techniques.8 Under certain assumptions, two samples are sufficient. As the model gains in complexity, more parameters have to be identified, which is impossible without obtaining more information. Continuous sampling techniques are of special interest in this context.

Postdialysis Urea Rebound The postdialysis urea rebound (PDUR) has attracted considerable interest because blood urea concentration is artificially low in blood drawn from the arterial line of the extracorporeal circulation immediately following the end of dialysis. Thus, the dose of dialysis delivered will be overestimated if calculated from an unequilibrated blood sample. Solute concentrations increase when dialysis is stopped, because of the dissipation of solute gRADients which develop during the course of dialysis between different body compartments and, to a lesser degree, because of continuing urea generation. In the analysis by Pedrini, rebound was modeled as the dissipation of intra-to-extracellular urea gRADients which were assumed to develop during dialysis because of limited cell membrane permeability.50 It is also possible that rebound is related to the dissipation of flow dependent gRADients which are caused by access recirculation, compartment recirculation and sequestration of urea in peripheral compartments, largely because of differences in regional perfusion. The PDUR consists of three components which differ in magnitude and in time. As soon as dialysis is stopped, the intra-to-extracorporeal gRADient starts to dissipate. It takes not more than 20 seconds for this process to complete. Access outflow concentrations increase a few seconds after stopping dialysis, which will lead to a subsequent increase in arterial access inflow concentrations. It takes approximately 2 minutes for arteriovenous gRADients to dissipate. Arterial concentrations increase within a minute after stopping dialysis, which reduce the tissue to blood gRADient in the low flow system and reverse the tissue to blood gRADient in the high flow system. During this phase, which completes within approximately 30 minutes, solutes equilibrate between organ systems flowing from organs with low, to organs with high, perfusion. Not all three components of PDUR occur with every treatment. The PDUR related to access and compartment recirculation depends on access function and access type. The magnitude of these gRADients can be determined from recirculation measurements (Eqs. [2.14], [2.16]). Access recirculation is rare, but if present it is likely to be the largest contribution to PDUR. The contribution of compartment recirculation is small but it is consistent with peripheral arteriovenous accesses. However, these components can easily be measured, since gRADients caused by access and compartment recirculation dissipate within 2 minutes. The third phase of PDUR which relates to dissipation of solute disequilibrium between organ systems with different specific perfusion takes approximately 30 minutes. Ideally, one would want to wait until all dialysis-induced gRADients have dissipated and to sample an equilibrated postdialysis concentration (ceq) 30 to 60 minutes after the end of

Urea Removal During Hemodialysis

47

dialysis. This is difficult not only because of patient compliance but also because of organization in a busy dialysis unit. The rebound has been measured as a function of the intRADialysis fall in solute concentration (PDUR f) or as a function of the postdialysis solute concentration (PDURp):

PDURp =

ceq − c post c post

[Eq. 2.40]

A comparison of both measures reveals a characteristic of the two compartment system (Fig. 2.9). Rebound measured as a function of the intRADialysis decrease in solute concentration (cpre - cpost) continuously decreases with increased dialysis duration. In terms of solute removal, the error caused by neglecting rebound is more important with short than with long dialysis. However, rebound measured as a function of postdialysis solute concentration is largely independent of the duration of hemodialysis. Rearranging Eq. [2.40] and applying the basic constant volume, single compartment model (without G or Kr) gives an expression for the overestimation in dose of dialysis calculated as Kt/V from postdialysis and equilibrated concentrations:

(

)

∆Kt / V = Kt / Veq − Kt / Vpost = − ln PDURp + 1

[Eq. 2.41]

Thus, in a given treatment the overestimation is independent of the duration of the treatment. The implications of PDUR would be minor if the extent of rebound were predictable for each patient. Fortunately, the third phase of rebound is much more consistent than the first two phases of rebound. There are currently three techniques to predict the PDUR and to account for the overestimation of Kt/V based on a 20 s or 2 min postdialysis urea concentration. All techniques are derived from the intRADialysis drop in urea concentration and from characteristics of two compartment urea kinetics. Rate of Elimination Phase The approach to estimate equilibrated postdialysis concentration from intRADialysis solute profiles is based on the observation that blood urea concentration during the later stages of hemodialysis can be approximated by a single exponential function with a constant rate. This is a characteristic of the two compartment kinetic model, but its value for dialysis was first recognized by Smye.51 The typical variation of solute concentration in the two compartment model is described by an early distribution and a late elimination phase. Late concentration changes in one compartment are followed by parallel changes in the other compartment. The process is governed by elimination. Exactly the same consideration applies for dialysis. Once the solute flux between compartments has stabilized, the variation in solute concentration in both compartments is governed by elimination, i.e., by dialysis efficiency (K/V). The process of distribution is usually completed within one hour of dialysis so that the process of elimination can be measured during the later stages of the treatment (Fig. 2.10). If the rate constant of elimination (k) is measured in the peripheral compartment, and if a valid reference concentration is known for the central compartment, the concentration in the central compartment can be estimated for any time of the elimination process (Fig. 2.11). Similar considerations apply for the equilibrated compartment.

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The Artificial Kidney: Physiological Modeling and Tissue Engineering

Fig. 2.9. Postdialysis urea rebound. (Top) Variation in dialyzer inflow (cd,in) and mean body concentration (cmean). (Bottom) Hypothetical rebound (PDURp, PDURf) to be observed if dialysis were stopped at any time during the treatment. A, B, and C in ratios refer to differences indicated in top panel.

Predialysis solute concentration is a valid reference value for equilibrated concentration. Therefore, if this concentration is used as a reference value, the concentration estimated from the rate constant (k) for the end of dialysis (t) provides an estimate close to equilibrated postdialysis concentration (cest):

cest = c pree − k ⋅t

[Eq. 2.42]

Urea Removal During Hemodialysis

49

Fig. 2.10. Systemic and compartmental clearance. Simulated variation in systemic (Ksys) and compartmental clearance (KH, KL) using a constant dialyzer clearance (Kd = 300 ml/min). compartment recirculation causes an initial stepdown in systemic clearance. The relative contribution of high and low flow compartment clearance (KH, KL) changes during the early distribution phase and assumes a steady state during the late elimination phase of hemodialysis.

Equilibrated postdialysis concentration can then be used to calculate equilibrated Kt/V. In a two compartment variable volume model with urea generation, cest is not exactly equal to ceq because changes in volume and urea generation affect the slope of the late elimination phase (see also Fig. 2.10). Patient Clearance Time In a different approach, Tattersall observed that the time separating arterial urea concentration from the same equilibrated concentration was constant and independent of the rate and duration of dialysis (Fig. 2.11).52 This time required to clear all parts of the body with infinite dialyzer clearance to reach an equilibrated Kt/V equal to 1.0 is called patient clearance time (tp). The time can be interpreted as the sum of characteristic perfusion times (τ = Vn/Qn) of each compartment weighted for volume fraction (Vn/V):

tp =

Vn Vn

∑Q ⋅V

n = L, H

n

[Eq. 2.43]

The characteristic time of a compartment refers to the inverse of its specific perfusion and identifies the time required for cumulative perfusion to equal the compartment volume (Fig. 2.8). Assuming standard values for V, Vn, and Qn, tp is identified as approximately

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50

Fig. 2.11. Rate of late elimination phase and patient clearance time. Logarithmic plot of arterial blood concentration during hemodialysis and postdialysis rebound. The rate constant (k) of the late elimination phase determined from an intRADialysis (cintra) and from the postdialysis concentration (cpost) can be used to calculate an estimated postdialysis concentration (cest, Eq. [2.42]) close to the equilibrated postdialysis concentration (ceq). The patient clearance time (tp) is required for postdialysis concentrations to equilibrate.

36 minutes. The patient clearance time tp can be used to estimate equilibrated postdialysis urea concentration (cest) from post- and predialysis solute concentrations:

c  cest = c pre  post   c pre 

(

t t +t p

)

[Eq. 2.44]

and to correct single compartment Kt/V determined from the immediate postdialysis sample using the following formula:

Kt / Veq = Kt / Vsp ⋅

t t + tp

[Eq. 2.45]

Urea Removal During Hemodialysis

51

Rate Equation In the third approach, Daugirdas and Schneditz showed that the overestimation of Kt/V calculated from immediate postdialysis and equilibrated urea concentration (∆Kt/V) was a function of dialysis efficiency (K/V).53 Simulation of treatments with different dialysis intensities in patients with different urea generation rate, urea content, distribution volume, and volume removal shows that PDURp varies with dialysis efficiency (Fig. 2.12). A strong linear relationship is only observed when the transfer rate between compartments is scaled to compartment size. A corresponding relationship between the overestimation of Kt/V and PDURp can be expected because of the association described in Eq. [2.41]. A practical formula is obtained if the overestimation of dialysis dose is expressed as a function of single pool dialysis efficiency:

∆Kt / V = − a ⋅

Kt V sp t

+b

[Eq. 2.46]

where a and b are constants which depend on whether unequilibrated Kt/V is determined from an arterial (a = 36 min, b = 0.03) or from a venous (a = 28 min, b = 0.02) blood sample drawn from a peripheral or from a central venous access. Unequilibrated Kt/V corrected for effects of ultrafiltration and urea generation can be estimated from pre- and postdialysis urea concentration, the ultrafiltration volume (UFV), body weight (BW) and the dialysis duration (t) can be obtained by one of the single pool formulas, such as the one suggested by Daugirdas:54

[Eq. 2.47] Comparison of Estimates The techniques to identify equilibrated Kt/V from two or three blood samples drawn at the beginning, at the end, and during dialysis are based on the assumption that treatment and patient parameters remain constant throughout dialysis. This is not the case when treatments are interrupted by stopping blood pumps, when dialysate bypasses the dialyzer during machine alarms and automatic machine calibration routines, or when blood flows and ultrafiltration rates are reduced because of patient complications. In the approaches by Tattersall and Daugirdas and Schneditz, it is also assumed that the two compartment effect is scaled to a specific perfusion pattern. The patient clearance time (tp) and the slope (a) of the rate equation depend on a specific perfusion pattern of different organ systems. Systematic differences in these parameters are expected for hypotensive or exercising dialysis patients. The approach by Smye, on the contrary, is not sensitive to individual differences in specific perfusion. The slope of the elimination phase (k) is actually estimated in the individual treatment. But the accuracy of the approach depends on whether a representative estimate of k can be obtained, both because of sampling errors and because of variation in the elimination process. In a recent evaluation of the three techniques, the rate equation was the most accurate.55 The addition of one or more intRADialysis samples did not increase the accuracy of predicting equilibrated Kt/V.

Conclusion It is well understood that urea only serves as a surrogate for all the substances which accumulate in uremia and which have to be cleared from the body by hemodialysis.

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Fig. 2.12. Rebound, Kt/V, and rate of hemodialysis. (Top) Simulated increase of postdialysis urea rebound (PDUR) with increase in dialysis efficiency (K/V). (Bottom) Simulated decrease in dose of delivered dialysis (Kt/Veq - Kt/Vsp) with increase in dialysis efficiency (K/V). Adapted from Daugirdas,53 with permission from the American Society for Artificial Internal Organs.

Urea Removal During Hemodialysis

53

However, the concept of a minimum fractional urea removal measured as a Kt/V of 1.2 has found widespread clinical acceptance and represents the current norm in hemodialysis therapy.25 It took three stages to reach this point. Early models derived from physiology confirmed the validity of proposed mechanisms but were too complex to be used in clinical practice. A simplified approach based on the single compartment model provided reasonable accuracy and could be applied and tested by many users. In spite of the benefits for hemodialysis quantification, the limitations of the single compartment approach called for reevaluation of the model structure. Eventually, complexity was reintroduced into a technique which had already found clinical acceptance.

Improved Measurement The future of hemodialysis is likely to see automatic measurement of urea removal with every treatment. At present urea removal is measured by manual techniques. Manual techniques require accurate blood sampling, staff time, and are subject to all the errors involved in sample and data handling. Blood sampling significantly contributes to treatment-related protein and iron losses. These factors limit the frequency of manual urea measurements. Urea removal is usually measured from two or at most three blood samples taken on the occasion of the monthly bloodwork. These samples are considered representative for hemodialysis delivered over the course of one month. The treatment done on the modeling day is more likely to be done in compliance with the prescription, so that the result is biased when compared to actual dialysis dose delivered during the rest of the month. Also, reimbursement is likely to be linked to the actual amount of hemodialysis delivered with each treatment. This situation calls for automatic and continuous techniques. Three different approaches are under investigation but all of them need to be studied and refined. Theoretically there is no need for taking blood samples during hemodialysis since blood and material cleared from the body comes into close contact with the dialysis machine. The measurement in whole blood would be of most advantage. However, no technique is currently available which allows for simple, stable, specific, non-toxic, and non-destructive detection of urea levels in whole blood. The next best approach to measure urea levels during hemodialysis is the measurement of ultrafiltrate.56 Ultrafiltrate which has the same urea concentration as blood water can be obtained from the extracorporeal circulation at reasonable rates and in reasonable amounts. The sample does not have to be returned to the body, so that technical requirements are considerably simplified. The continuous measurement of ultrafiltrate is realized in the Urea Monitoring System (UMS, Bellco-Sorin, Mirandola, Italy) which requires a small ultrafiltration filter to be incorporated into the arterial blood line. Urea in the ultrafiltrate is measured by the increase in conductivity caused by enzymatic hydrolysis of urea into ammonium and bicarbonate ions.1 There is a short delay between measured and actual blood concentrations. The device detects all factors involved in convective urea transport to the dialyzer. The total amount of urea removed from the patient by hemodialysis is obtained from continuous measurement of urea concentration in spent dialysate volume. Urea concentrations are much smaller in dialysate because urea extraction is less than 1.0 and because dialysate flow is usually much higher than blood flow. One approach is realized in the Biostat 1000 (Baxter Healthcare Corporation, McGaw Park, Illinois) where spent dialysate is sampled from dialyzer outflow and analyzed for urea concentration.57 Calculation of Kt/V requires a predialysis blood water urea concentration, which can be obtained if the treatment is started by a short period of isolated ultrafiltration. A different approach is followed using automatic clearance measurements.58 On-line clearance techniques measure the effective performance of the dialyzer in the extracorporeal circulation and correct for effects caused by access recirculation. Measurements take a

The Artificial Kidney: Physiological Modeling and Tissue Engineering

54

few minutes and can be repeated several times during one treatment. Except for some changes in the dialysis machine, these techniques need no additional equipment. On-line clearance provides an automatic measure for access clearance, which otherwise is calculated from K0A, blood flow, and dialysate flow according to Eq. [2.10], and from access recirculation according to Eqs. [2.13] and [2.14]. The calculation of dialysis dose requires an estimate of urea distribution volume which can be obtained from a pre- and postdialysis blood water urea concentration measured with the monthly bloodwork. Therefore, a combination of automatic clearance and concentration measurements would cover significant aspects of dialysis dose measurement. Measurement of urea removal and system identification with every treatment would increase the accuracy of model parameters such as total body water. An improved accuracy of this parameter would be helpful in the evaluation of body hydration and body composition.

Improved Treatment Modes The future of hemodialysis is likely to see a larger variety of treatment modes. Currently, most hemodialysis treatments are done three times a week for a duration of three to four hours. This schedule emerged as a consequence of maximizing efficiency (K/V) based on urea removal, and also minimizing treatment time and treatment frequency, because of economic factors. Hemodialysis certainly represents the most expensive example of current substitutive medicine. Yet, hemodialysis is far from optimal. The treatment is non-physiologic and causes large perturbations in the composition and distribution of body fluids, placing the patient under significant compensatory stress because of its intense and intermittent nature. With the improvement of technical equipment such as the development of sensors to measure and to control treatment-related outcome and safety parameters, new treatment modes are likely to gain popularity. Other combinations of treatment frequency, time, and intensity are under discussion, such as slow nocturnal hemodialysis (low intensity, long, frequent) and daily home dialysis (high intensity, short, frequent). It is likely that current concepts of urea removal can be extended to other forms of treatment; however, it remains to be seen whether the removal of urea alone remains the strong marker for adequate hemodialysis treatment.

Notation A A a, b a11, a12, a21, a22 BW β, β’ c

r cr c˙

Cl CO D E fac fCPR fPS G Jd, Jv

matrix of constant coefficients (Eqs. [2.36], [2.37]) surface area constants (Eq. [2.46]) elements of the matrix of coefficients (Eq. [2.37]) body weight constants (Eqs. [2.24], [2.33]) concentration row vector of concentration (Eqs. [2.36], [2.37]) r derivative of c with respect to v (Eqs. [2.36], [2.37]) clearance (Eqs. [2.6], [2.7], [2.8]) cardiac output coefficient of diffusion extraction (Eqs. [2.3], [2.5]) intra-to-extracorporeal gRADient (Eq. [2.14]) arteriovenous gRADient (Eq. [2.16]) tissue to venous blood gRADient (Eq. [2.32]) urea generation rate (Eqs. [2.34], [2.38]) diffusive, convective solute flow

Urea Removal During Hemodialysis

k K Kc Kr K0A m PDUR PDURf PDURp PS Q Qb Qd R t τ tp UFR UFV V v W x Z Subscripts: 0 ac art b CPR d e eq est H i in intra L mean mix out post pre PS sp sys t tis ven

rate constant of elimination phase (Eq. [2.42]) dialyzer clearance (Eq. [2.10]) intercompartmental urea clearance residual urea clearance dialyzer membrane transfer coefficient mass postdialysis urea rebound PDUR as function of intRADialysis solute fall PDUR as function of cpost (Eq. [2.40]) permeability ⫻ surface area product volume flow extracorporeal blood flow dialysate flow recirculation time characteristic perfusion time patient clearance time (Eq. [2.43]) ultrafiltration rate (Eq. [2.4]) ultrafiltration volume volume relative volume (Eq. [2.22]) ratio of permeability to flow (Eqs. [2.10], [2.32], [2.33]) thickness of membrane (Eq. [2.1]) ratio of blood flow to dialysate flow (Eq. [2.10]) related to time t = 0 related to access related to arterial blood related to blood related to compartment recirculation related to dialyzer related to extracellular related to equilibrated related to estimated related to high flow system related to intracellular related to inflow related to intRADialysis related to low flow system related to mean concentration related to mixed blood related to outflow related to end of hemodialysis, postdialysis related to beginning of dialysis, predialysis related to permeability related to single pool model related to systemic circulation related to time = t related to tissue related to venous blood

55

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References 1. Taylor AJ, Vadgama P. Analytical reviews in clinical biochemistry: he estimation of urea. Ann Clin Biochem 1992; 29:245-264. 2. Schmidt-Nielsen K. Scaling: Why is Animal Size so Important? Cambridge: Cambridge University Press, 1984. 3. Lightfoot EN. The roles of mass transfer in tissue function. In: Bronzino JD. ed. The Biomedical Engineering Handbook. Boca Raton: CRC Press, 1995:1656-1670. 4. Sperelakis N. Diffusion and permeability. In: Sperelakis N, ed. Cell Physiology Source Book. San Diego: Academic Press, 1995:61-66. 5. Kety SS. Physiological and physical factors governing the initial stages of drug distribution. In: Teorell T, Dedrick RL, Condliffe PG, eds. Pharmacology and Pharmacokinetics. New York: Plenum Press, 1974:233-240. 6. Lassen NA, Henriksen O, Sejrsen P. Indicator methods for measurement of organ and tissue blood flow. In: Shepherd JT, Abboud FM, eds. Handbook of Physiology. Section 2: The Cardiovascular System. Volume 3. Bethesda: American Physiological Society, 1983:21-63. 7. Stiller S, Mann H, Brunner H. Backfiltration in hemodialysis with highly permeable membranes. Contrib Nephrol 1985; 46:23-32. 8. Depner TA. Prescribing Hemodialysis: A Guide to Urea Modeling. In: Boston/ Dordrecht/London: Kluwer Academic Publishers, 1991. 9. Renkin EM. Effects of blood flow on diffusion kinetics in isolated, perfused hindlegs of cats. Am J Physiol 1955; 183:125-136 10. Renkin EM. Transport of potassium-42 from blood to tissue in isolated mammalian skeletal muscle. Am J Physiol 1959; 6:1205-1210. 11. Kety SS. Theory and applications of the exchange of inert gas at the lungs and tissues. Pharmacol Rev 1951; 3:1-41. 12. Galletti PM, Colton CK, Lysaght MJ. Artificial kidney. In: Bronzino JD, ed. The Biomedical Engineering Handbook. Boca Raton: CRC Press, 1995:1898-1922. 13. Daugirdas JT, Ing TS. Handbook of Dialysis. 2nd ed. Boston: Little, Brown and Company, 1994. 14. Gotch FA. Models to predict recirculation and its effect on treatment time in single-needle dialysis. In: Ringoir S, Vanholder R, Ivanovich P, eds. First International Symposium on Single-Needle Dialysis. Cleveland: ISAO Press, 1984:305. 15. Sherman RA. Recirculation in the hemodialysis access. In: Henrich WL, ed. Principles and Practice of Dialysis. Baltimore: Williams & Wilkins, 1994:38-46. 16. Collins AJ, Hanson G, Berkseth R et al. Recirculation and effective clearances. Kidney Int 1988; 33:219. (abst.) 17. Schneditz D, Kaufman AM, Polaschegg HD, et al. compartment recirculation during hemodiaysis. Kidney Int 1992; 42:1450-1456. 18. Cobelli C, Saccomani MP. compartment models of physiologic systems. In: Bronzino JD. ed. The Biomedical Engineering Handbook. Boca Raton: CRC Press, 1995:2375-2385. 19. Cappello A, Grandi F, Lamberti C et al. Comparative evaluation of different methods to estimate urea distribution volum and generation rate. Int J Artif Organs 1994; 17:322-330. 20. Grandi F, Avanzolini G, Cappello A. Analytic solution of the variable-volume double-pool urea kinetics model applied to parameter estimation in hemodialysis. Comput Biol Med 1995; 25:505-518. 21. Smye SW, Will EJ. A mathematical analysis of a two-compartment model of urea kinetics. Phys Med Biol 1995; 40:2005-2014. 22. Schneditz D, Daugirdas JT. Formal analytical solution to a regional blood flow and diffusion based urea kinetic model. ASAIO J 1994; 40:M667-M673. 23. SargentJA. Control of dialysis by a single-pool urea model: The national cooperative dialysis study. Kidney Int 1983; 23[Suppl 13]:S19-S25. 24. Sargent JA, Gotch FA. Principles and biophysics of dialysis. In: Jacobs C, Kjellstrand CM, Koch KM et al, eds. Replacement of Renal Function by Dialysis. 4th ed. Dordrecht: Kluwer Academic Publishers, 1996:35-102.

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25. Owen W Jr, Roberts J. Hemodialysis adequacy. In: National Kidney Foundation Inc, ed. Clinical Practice Guidelines. Executive Summaries. New York: National Kidney Foundation, Inc, 1997:16. 26. Ilstrup K, Hanson G, Shapiro W et al. Examining the foundations of urea kinetics. ASAIO Trans 1985; 31:164-168. 27. Pearson P, Lew S, Abramson F et al. Measurement of kinetic parameters for urea in end-stage renal disease patients using a two-compartment model. J Am Soc Nephrol 1994; 4:1869-1873. 28. Liberati D, Biasioli S, Foroni R et al. New compartmental model approach to dialysis. Med Biol Eng Comput 1993; 31:171-179. 29. Halperin ML, Goldstein MB. Fluid, Electrolyte, and Acid-Base Physiology. 3rd ed. Philadelphia: WB Saunders, 1999:230-237. 30. Star RA, Hootkins RE, Thompson JR et al. Variability and stability of two pool urea mass transfer coefficient. J Am Soc Nephrol 1992; 3:395. (abst.) 31. Star RA, Hogarth L, Garcia E, et al. Two pool urea mass transfer coefficient (MTC) by urea infusion. J Am Soc Nephrol 1993; 4:388. (abst.) 32. Schneditz D, Fariyike B, Osheroff R et al. Is intercompartmental urea clearance during hemodialysis a perfusion term? A comparison of two pool urea kinetic models. J Am Soc Nephrol 1995; 6:1360-1370. 33. Pflederer BR, Torrey C, Priester-Coary A et al. Estimating equilibrated Kt/V from an intRADialytic sample: Effects of access and compartment recirculations. Kidney Int 1995; 48:832-837. 34. Kjellstrand CM, Skroer R, Cederlof IO, et al. Patient related factors leading to slow urea transfer in the body during dialysis. ASAIO J 1994; 40:164-170. 35. Keshaviah PR, Hanson G, Collins AJ. Erythropoietin and cell membrane permeability. Kidney Int 1990; 37:304. (abst.) 36. Depner TA, Rizwan S, Cheer AY et al. Peripheral urea disequilibrium (PUD) during hemodialysis is temperature-dependent. J Am Soc Nephrol 1991; 2:321. (abst.) 37. Coleman TG, Manning RD Jr, Norman RA Jr, et al. Dynamics of water-isotope distribution. Am J Physiol 1972; 223:1371-1375. 38. Matthews DE, Downey RS. Measurement of urea kinetics in humans: A validation of stable isotope tracer methods. Am J Physiol 194; 246:E519-E527. 39. Dedrick RL, Bischoff KB. Pharmacokinetics in applications of the artificial kidney. Chem Eng Prog Symp Ser 1968; 64:32-44. 40. Teorell T. Kinetics of distribution of substances administered to the body. Arch Int Pharmacodyn Therap 1937; 57:205-240. 41. Bischoff KB, Brown RG. Drug distribution in mammals. Chem Eng Prog Symp Ser 1966; 62:33-45. 42. Schneditz D, Van Stone JC, Daugirdas JT. A regional blood circulation alternative to in-series two compartment urea kinetic modeling. ASAIO J 1993; 39:M573-M577. 43. Metry GS, Attman PO, Lonnroth P et al. Urea kinetics during hemodialysis measured by microdialysis—a novel technique. Kidney Int 1993; 44:622-629. 44. Ronco C, Brendolan A, Crepaldi C et al. Ultrafiltrations-Rate und Dialyse-Hypotension. Dialyse J 1992; 40:8-15. 45. Schneditz D, Zaluska WT, Morris AT et al. Effect of ultrafiltration (UF) on peripheral urea sequestration in hemodialysis (HD) paients. J Am Soc Nephrol 1996; 7:1525. (abst.) 46. Ronco C, Crepaldi C, Brendolan A et al. InRADialytic exercise increases effective dialysis efficiency and reduces rebound. J Am Soc Nephrol 1996; 6:612. (abst.) 47. Smye SW, Lindley EJ, Will EJ. Simulating the effect of exercise on urea clearance in hemodialyss. J Am Soc Nephrol 1998; 9:128-132. 48. Marmarelis VZ. Methods and tools for identification of physiologic systems. In: Bronzino JD, ed. The Biomedical Engineering Handbook. Boca Raton: CRC Press., 1995:2432-2446. 49. Grandi F, Avanzolini G, Cappello A. Sensitivity analysis for estimating urea kinetics parameters during hemodialysis. Med Eng Phys 1995; 17:177-181.

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50. Pedrini LA, Zereik S, Rasmy S. Causes, kinetics and clinical implications of posthemodialysis urea rebound. Kidney Int 1988; 34:817-824. 51. Smye SW, Dunderdale E, Brownridge G et al. Estimation of treatment dose in highefficiency haemodialysis. Nephron 1994; 67:24-29. 52. Tattrsall JE, DeTakats D, Chamney P et al. The post-hemodialysis rebound: Predicting and quantifying its effect on Kt/V. Kidney Int 1996; 0:2094-2102. 53. Daugirdas JT, Schneditz D. Overestimation of hemodialysis dose depends on dialysis efficiency by regional blood flow but not by conventional two pool urea kinetic analysis. ASAIO J 1995; 41:M719-M724. 54. Daugirdas JT. Second geneation logarithmic estimates of single-pool variable volume Kt/V: An analysis of error. J Am Soc Nephrol 1993; 4:1205-1213. 55. Daugirdas JT, Depner A, Gotch FA et al. Comparison of methods to predict equilibrated Kt/V in the HEMO pilot study. Kidney Int 1997; 52:1395-1405. 56. Canaud B, Bosc JY, Leblanc M et al. A simple and accurate method to determine equilibrated post-dialysis urea concentration. Kidney Int 1997; 51:2000-2005. 57. Depner TA, Keshaviah PR, Ebben JP et al. Multicenter clinical validation of an on-line monitor of dialysis adequacy. J Am Soc Nephrol 1996; 7:464-471. 58. Polaschegg HD. Automatic, noninvasive intRADialytic clearance measurement. Int J Artif Organs 1993; 16:185-191.

CHAPTER 3

Transport Kinetics During Peritoneal Dialysis Michael F. Flessner

W

hile hemodialysis is the principal renal replacement therapy in the world, an alternative therapy for patients with kidney failure is peritoneal dialysis. Under normal conditions, the peritoneal cavity is a potential space which surrounds the viscera of the abdomen and pelvis. In patients with renal failure, a plastic tube or catheter is surgically inserted through the abdominal wall near the umbilicus into the cavity, and the external portion of the catheter is typically tunneled to one side of the abdomen under the skin. Two to four weeks of healing seals the catheter in its tunnel, and the catheter is ready for use. In adult humans, the cavity is filled with two to three liters of dialysis solution, which is allowed to dwell in the cavity for a specific length of time (determined by the physician’s prescription). During this dwell period, water and solutes transport via passive diffusion and convection from the blood which circulates through the tissues surrounding the cavity. After the dwell period, the fluid is drained and replaced with a fresh solution, and the entire process is repeated. If this process is repeated 4-5 times in a 24 hour period, the dialysis technique is termed continuous ambulatory peritoneal dialysis or CAPD. Continuous cyclic peritoneal dialysis or CCPD is an automated version of peritoneal dialysis which employs multiple, nighttime exchanges with one or two longer daytime exchanges. Nightly peritoneal dialysis or NPD (sometimes called NIPD for nightly intermittent peritoneal dialysis) uses automated exchanges at night only; the cavity is dry during the day. Because the time of active exchange is limited, NPD does not provide as much dialysis as CCPD or CAPD, but is used in patients with sufficient residual renal function to warrant less dialysis. Tidal peritoneal dialysis or TPD is another variant of automated peritoneal dialysis which leaves a large portion of the dialysis fluid in contact with the peritoneum and exchanges a smaller portion; this eliminates the time between exchanges in which there is a minimal amount of fluid in the cavity. All of these techniques are variations of the same transport process for water and solutes across the peritoneum. One goal of this chapter is the description of the physiologic transport system, including the anatomy and tissue level mechanisms. A second goal is the review of the mathematical approaches toward the quantification of this process and the data which supports each of these approaches. Although the overall dialysis process requires less sophisticated hardware than hemodialysis, the fundamental physiologic mechanisms of peritoneal dialysis are complex and present a challenge to the biomedical engineer who would attempt to quantitatively evaluate the process.

The Artificial Kidney: Physiological Modeling and Tissue Engineering, edited by John K. Leypoldt. ©1999 R.G. Landes Company.

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The Artificial Kidney: Physiological Modeling and Tissue Engineering

Peritoneal Anatomy The human peritoneum consists of a single layer of mesothelial cells and four to five layers of connective tissue, and is of the order of 100 mm thick over the intestine.1 It is relatively avascular and does not contain enough blood vessels to supply the observed transperitoneal transport of small solutes. Although the surface area which can be dissected in autopsy studies of humans approaches 1-2 m2, the actual area in contact with the fluid may be a fraction of the total dissected area2 (discussed below). The peritoneum covers most of the stomach, the small and large intestines, a portion of the liver, spleen, and pelvic organs (the portion of the peritoneum covering these tissues is often called the visceral peritoneum) and the anterior abdominal wall, diaphragm, and retroperitoneal structures of the kidneys and the muscles of the back (this part of the peritoneum is termed parietal peritoneum). The three-dimensional surface is very convoluted, as illustrated in various references3 and in Figure 3.1; however, the best way to appreciate the nature of the surface is to visit the operating room during abdominal surgery or to observe a dissection of the abdominal cavity in a human cadaver or in a small animal. Except for the solid organs (kidney, spleen, and liver), the serosal side of the hollow organs of the gastrointestinal tract and the parietal tissues is made up of muscle. The observations that the makeup of the tissue underlying the peritoneum resembles skeletal muscle and that the transfer rates for small solutes are very similar for the different surfaces4 lend support to the lumping of all tissues together in a single “peritoneal tissue.” This issue will be further examined below. Blood flows to the visceral tissues via the celiac artery and the superior and inferior mesenteric arteries which then flow into the portal vein and to the liver. The parietal tissue, on the other hand, has separate arteries supplying each muscle and these drain directly to the vena cava. This difference in blood supply in the various tissues does not affect dialysis, but it will affect the metabolism of drugs and other substances which are absorbed from the cavity. Substances which are absorbed chiefly across the visceral peritoneum will be subject to uptake and metabolism by the liver in a “first pass effect” prior to their arrival in the general circulation. Because the blood does not have to flow through an extracorporeal shunt during peritoneal dialysis, patients with heart disease may tolerate the technique better than hemodialysis. A very unique but important tissue to the peritoneal cavity is the diaphragm, which forms the uppermost boundary of the cavity when the patient is sitting or standing. The diaphragm contains a specialized system of lymphatics5,6 which are responsible for 60-70% of the total lymph flow from the cavity (the remainder presumably transports via the lymphatics of the gut). Specialized stomata open between mesothelial cells which lie over the muscle cells of the diaphragm during the relaxation phase of diaphragmatic movement. Cells, protein, and fluid in the cavity enter small spaces (termed lacunae) between the cells of the diaphragm during this phase. The stomata close with contraction of the diaphragm, and the contents of the lacunae are forced upward into collecting lymphatics which lead to larger lymph ducts which ultimately transfer the lymph (cells, protein, fluid) back to the venous system. As might be expected, such a mechanism is dependent on the rate of diaphragmatic movement or rate of respiration. The movement of the diaphragm also sets up pressure gradients in the cavity which tend to propel the contents of the cavity toward it.

Physiology of the Dialysis Transport Process System Overview Figure 3.2 provides a conceptual overview of the transport system. The diagram illustrates a primary difference between hemodialysis and peritoneal dialysis. In hemodialysis,

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Fig. 3.1. Sagittal section of the human peritoneum. This illustrates a portion of the complex surface of the peritoneum which can be truly appreciated only in three dimensions. The actual space has been exaggerated.

the dialysis fluid makes intimate contact with the artificial blood capillary, and transport is governed by the properties of the synthetic membrane. In contrast, blood flows through exchange vessels within the peritoneal tissue (tissue surrounding the cavity and adherent to the peritoneum), and transport between the blood and the cavity occurs through the tissue. This barrier of the tissue in addition to that of the blood capillary wall is the primary reason why peritoneal dialysis is so inefficient in comparison with hemodialysis. Solute and water transfer occurs initially between the blood capillaries and the tissue interstitium and

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Fig. 3.2. Three compartmental model of transport between the blood and the peritoneal cavity. Bidirectional fluid flow (convection) and mass transfer occurs between capillaries within the tissue space and the solution in the cavity. This illustrates the major difference between peritoneal dialysis and hemodialysis, where transport occurs across synthetic capillary membranes which make direct contact with the dialysis fluid. Because of the specialized lymph drainage, the diaphragmatic system is shown as a separate pathway. (Adapted from ref. 84).

subsequently between the solution in the cavity and the tissue space. During dialysis, the transport forces in the tissue favor removal of solutes and water from the circulation. Because of the specialized nature of the subdiaphragmatic lymphatic system, this transport process is shown as a path separate from the tissue space. Solute transfer across the peritoneum occurs via passive diffusion and convection. Diffusion dominates over convection in the transfer of small molecular weight solutes (MW <6,000 daltons) such as urea, creatinine, or glucose. Convection, if of sufficient magnitude, may be more important in the transport of proteins (MW >50,000 daltons). Diffusion of serum proteins, however, is not insignificant and should be included in the model formulation. 7,8 Solutes with molecular weights between 6,000 and 50,000 daltons are affected by both mechanisms. Charge of the solute can affect the transport; cationic charge will retard transport of dextrans with molecular size of >1.5 nm (approximately 10,000 daltons).9 Water transfer between the cavity and the tissue occurs in response to forces of hydrostatic and osmotic pressure. Early practitioners of peritoneal dialysis10,11 attempted to use normal saline as the dialysis solution. When they observed their patients experiencing congestive heart failure due to fluid absorption, they empirically made the solutions hypertonic with the addition of glucose to the solution. The dextrose-salt solution (330-500 mosm/kg) is still the major solution used today.

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Fig. 3.3. Dependency of the rate of fluid loss from the peritoneal cavity of cats on the i.p. pressure. The rate of fluid loss of an isotonic solution with 8% bovine serum albumin (BSA) was found to be the same as that of the solution without protein. Figure is taken from ref. 24, original data from ref. 12.

The reason for the fluid absorption is due only in part to the lymphatic uptake. Experiments in cats demonstrated that the absorption rates of solutions containing no protein or containing 8% bovine serum albumin (BSA) were equivalent and were directly proportional to the intraperitoneal (i.p.) hydrostatic pressure (Fig. 3.3).12 Some considered the apparent lack of sieving of protein at the peritoneum as proof that the fluid absorption was equivalent to lymph flow.13 However, the role of lymphatics is the transfer of serous or interstitial fluid back to the plasma. It was shown in rats that the rate of transfer from the peritoneal cavity to the blood was a small fraction of the total fluid loss from the cavity.14 That patients experience this range of i.p. pressures was subsequently verified by clinical investigators15,16 as is illustrated in Figure 3.4. Pressures in these patients were referenced to the highest point in the cavity and are therefore minimal pressures in the cavity. Further experiments in rats14,17 have demonstrated that the hydrostatic pressuredriven convection is independent of the osmotic pressure in the cavity, that lymph flow is independent of i.p. hydrostatic pressure, and that, above a threshold pressure of 2 cm H2O (supine position, referenced to the right heart of the rat), lymph flow makes up a small fraction of the total flow. Studies in healthy dialysis patients have shown analogous results with total absorption rates of 60-90 ml/h (estimated from the total protein clearance from the cavity),13,18-20 and lymph flow rates between 10 and 20 ml/h.18-20

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Fig. 3.4. Intraperitoneal pressures versus solution volumes in human patients. Curves have been derived from data in refs. 15-16. In each case, the highest position in the peritoneal cavity was chosen as the reference point for the pressure readings; thus these pressures represent minimal pressures. Twardowski’s data16 demonstrate the effect of position on i.p. pressure. The differences between the two sets of data are attributed to the state of hydration and the preparation of each set of patients.

Hypertonic solutions (1.36% to 3.86% dextrose in an isotonic salt solution) are used to cause fluid to shift from the surrounding tissue to the peritoneal cavity. The rate of fluid shift varies directly with the degree of hypertonicity, and volumes generally follow curves as illustrated in Figure 3.5.21 The curves tend to rise until 120-180 minutes, and then decrease with time. The decrease in ultrafiltration with time is due to the decrease in dextrose concentration and osmolality in the cavity with time; the dextrose is not only diluted by the water transferred into the cavity but also absorbed by diffusion from the cavity into the body. The actual curves represent the total volume in the cavity resulting from the two opposing flows (see Fig. 3.2) and are quite variable among patients, who transfer glucose at different rates and who likely have different intraperitoneal hydrostatic pressures. The net fluid removal is termed net ultrafiltration, which is equal to the difference between the osmotically-induced flow into the cavity and the hydrostatic pressure-driven flow from the cavity into the tissue. It is measured by subtracting the volume of the fluid instilled into the cavity from that which is drained out; the rate of net ultrafiltration is found by dividing the net volume difference by the duration of the solution dwell. Determination of the residual volumes in the cavity (typically 300-500 ml) before and after the dialysis dwell is necessary for accurate results. Recently, the effect of i.p. hydrostatic pressure on fluid removal was studied by measuring the net ultrafiltration in 34 supine patients after a 2 hour dwell of a

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Fig. 3.5. Volume versus time curves for two typical dialysis solutions (replotted from ref. 21). The initial volume in each case is 2,000 ml. The figure illustrates the effect of osmolality (3.86% solution has an initial osmolality of approximately 500 mosm/kg versus 330 mosm/kg for the 1.36% glucose solution). The glucose transfers from the cavity to the tissue with time; therefore its concentration decreases not only from dilution but also mass transfer. The decreasing portion of the curves is due to hydrostatic pressure-driven flow from the cavity, which continues beyond the time of osmotic equilibration between the blood and the peritoneal cavity.

3.86% dextrose solution and plotting the results versus the i.p. pressure. The data in Figure 3.6 clearly show the negative correlation between net ultrafiltration and i.p. pressure.22 Anecdotally, we have observed that patients with severe hypertension (diastolic blood pressure >130 mm Hg) have large amounts of fluid filtered, but the fluid filtration decreases significantly when the hypertension is controlled. These patients may be better hydrated (see below), in which case rates of convection may increase, or perhaps the Starling forces are shifted toward filtration from the capillaries to a greater degree than under normal pressures.

Tissue-Level Mechanisms The examination of transport mechanisms at the level of the tissue requires a working concept. Figure 3.7 illustrates a hypothetical tissue structure which lies adjacent to the peritoneum. Solutes circulate in the blood capillaries and transport passively across the capillary endothelium (cell layer and its basement membrane which make up the blood capillaries) into the tissue interstitium (space outside of blood vessels which surrounds the cells of any tissue). Once in the interstitium, the solute will continue passive diffusion in accordance with the concentration gradient and will be subject to convection by the solvent

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Fig. 3.6. Effect of i.p. hydrostatic pressure on the net ultrafiltration in 34 dialysis patients after two hours of dialysis with a 3.86% solution. Data replotted from ref. 22.

flow present. The presence of fresh dialysis fluid in the cavity in contact with the peritoneum overlying the tissue sets up the blood-to-cavity concentration gradient for endogenous substances such as urea and creatinine. In contrast to endogenous substances, the typical dialysis solution has a high concentration of glucose, which sets up a cavityto-blood concentration gradient. As each substance moves through the interstitium, uptake and metabolism by cells may occur. In addition, solutes may be taken up by other blood capillaries or by lymphatic capillaries and returned to the venous system. Although the blood and lymph capillaries are illustrated to be uniformly distributed in the tissue space, there is considerable variation among the different tissues. Lymphatics, for example, tend to be located in the tissue planes between layers of muscle; in contrast, they are more diffusely located in the wall of the gut.23 The major barriers to transport are the blood capillary endothelium and the interstitium. The peritoneum itself does not present any more barrier than the equivalent cellular and interstitial layer which underlies it. A recent review has detailed several studies of transmesenteric permeability. 24 The mesentery is essentially a double-walled fold of peritoneum with a small amount of connective tissue and vessels in between the layers, and it has been used as a surrogate for the peritoneum, which is difficult to dissect from the surrounding tissues. Unfortunately, in vitro permeabilities tend to be unreliable because mesothelial cells degRADe quickly in vitro and detach from the remainder of the peritoneum.25 However, the permeability of the peritoneum can be assessed indirectly. As noted above in the discussion of absorption, solutions containing 8% serum albumin are readily absorbed without change in protein concentration in the cavity12 (see Fig. 3.3). Absorption of solutions

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Fig. 3.7. Tissue-level model of the peritoneal transport system. Solutes and water must cross the blood capillary endothelium, the interstitial space, and the mesothelial cell layer, which together with several layers of connective tissue makes up the peritoneum (From ref. 83).

containing immunoglobulin G occurs the same way: without sieving of the protein at the peritoneum.26 In the past there have been proposals27 that protein is transported across via vesicles. However, other histologic studies have demonstrated intermesothelial gaps of approximately 50 nm,28 which would offer little resistance to the passage of macromolecules. Functional studies29 have shown that labeled iron coupled to transferrin transports across the mesothelium without disassociation. Since dissociation of the iron from the apoprotein would occur if the protein were taken up in an acidic compartment of the vesicle, it is unlikely that this transport occurs via endocytic vesicles. Because of the large intercellular gaps, the mesothelium also does not present a barrier to the osmotic agent glucose. Therefore, the same osmotically-induced transport mechanism which occurs across renal epithelium cannot be invoked, since there is no retardation of the transmesothelial transport of the glucose. This is further supported by the fact that interstitial hydrostatic pressures adjacent to the peritoneum are not negative, as one would anticipate if water were extracted by osmosis across a membrane.30

Blood Capillary The capillary barrier depends on the tissue type which determines the capillary density (surface area per unit volume of tissue) and the capillary permeability. For example, the liver has been reported to have a capillary permeability x area density of nearly 40 times that of skeletal muscle.31 Capillaries of the gut mucosa are known to be much more permeable than those of muscle.31 However, most of the exchange is with the outer layers of the gut,

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which are made up of muscle, or with capillaries in skeletal muscle attached to parietal peritoneum. Therefore, muscle capillaries appear to be the dominant capillary exchange element, since most of the parietal tissue is equivalent to skeletal muscle (retroperitoneum, abdominal wall, and diaphragm) and most of the visceral tissue is made up of the gastrointestinal tract with smooth muscle in the outer layers adjacent to the peritoneum. Starling’s Law is the basis of transport across a homogeneous membrane and has been modified by irreversible thermodynamics.32 Briefly, the volume flux can be described as:

J v = L p ( ∆ P - Σni=1σ i ∆ π i )

[Eq. 3.1]

where: Jv = volume flux; P = hydrostatic pressure; σi = the reflection coefficient for substance ‘i’ (fraction of solute ‘i’ which would be retained or reflected during convection of a solute through the membrane and accounts for the fact that most biological membranes are not truly impermeable to small solutes); πi = osmotic pressure of substance ‘i’. The general equation for solute transfer is

[Eq. 3.2] where: J s,i = flux of solute ‘i’; p i = diffusional permeability of membrane to ‘i’; Cm,i = mean concentration of ‘i’ in membrane (typically calculated as the arithmetic mean or log-mean). The major paRADigm which has been used to describe transcapillary transport is pore theory, originated by Pappenheimer and colleagues.33 In this theory, transport occurs through holes or “pores” in the membrane which provide a sieving action or retardation of the solute according to its molecular size. Rippe and colleagues have most eloquently applied this theory to peritoneal transport in the so-called Three-Pore Theory.34,35 Figure 3.8 depicts the essence of the theory, which attempts to account for the observed size discrimination and water flux of the peritoneal barrier. Let us imagine that we are looking at the capillary endothelium with the lumen to the right and the tissue interstitium on the left. The protein content (large circles) within the capillary is usually greater than in the interstitium while in peritoneal interstitium the concentration of dextrose or other small solutes (small circles) can be higher than in the capillary lumen. If we focus on the very small pore at the top of the diagram, termed “transcellular pore,” we see that no solutes, but only water, can pass through. The value for σ is one, and all solutes exert the full potential osmotic pressure across the pore. The RADius of this pore is on the order of 0.2-0.4 nm. Even if only 1-2% of the pores are of this type, as much as 40% of the filtration induced by hypertonic glucose solution in the peritoneal cavity can occur across these pores because they function as a true semipermeable membrane. Since this 40% is solute free, the introduction of this pore resolved the dilemma of the great difference between capillary membrane transmittance coefficients (equal to 1 - σ, where σ = capillary reflection coefficient, which is typically 0.05 for small solutes) and the sieving coefficient of the lumped structures (often called the “peritoneal membrane”) of 0.6-0.7 for small solutes. The morphologic equivalent of the “water-only” pore is a new class of “aquaporin.” The existence of this structure was discovered by Agre and his colleagues in the red cell membrane and was termed the CHIP-28 molecule.36 CHIP-28 is also located in the mammalian kidney (proximal tubule and the descending thin limb of the nephron), in the eye (ciliary and lens epithelium, corneal endothelium), in the gastrointestinal tract (hepatic bile duct, gall

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Fig. 3.8, top. Illustration of the three-pore theory of transendothelial transport. See text for details. Fig. 3.9, above. Solute clearance rates calculated from the triple-pore theory illustrated in Figure 3.8. The curves have been replotted from ref. 75. In calculating these rates, Rippe and colleagues (see Rippe43) assumed that the interstitium and the peritoneum offered negligible resistance to mass transfer.

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bladder epithelium), in eccrine sweat glands, lymphatic endothelium and in nonfenestrated endothelia.36 The next pore to discuss is the “small” pore which allows passage of small substances (molecular RADius <1.3 nm, up to the size of molecules such as inulin) but severely restricts proteins. Here, a small part of the osmotic pressure (σ = 0.02-0.05) of the small solutes and the full oncotic pressure (oncotic pressure = osmotic pressure exerted by protein concentration difference across a membrane; σ approaches 1 for protein) is exerted across these pores, as well as the hydrostatic pressure. The intercellular junction is the morphological equivalent of the small pore and has a RADius of 4-6 nm.34 Approximately 90-93% of the total pore area is likely made up of the small pores, and they are responsible for the majority of fluid transport. Physiologists such as Michael Perry have been able to calculate functional capillary permeabilities based on the intercellular gap morphology.37 Most physiologists accept this as the small pore. Let us next focus on the large pore (RADius >20 nm) at the bottom. Gunnar Grotte, a Swedish physiologist, was the first to propose in 1956 that the capillary endothelia had to possess large leaks or pores which would allow passage of large proteins and dextrans.38 Others proposed that this transport occurred by vesicular transport. Jens Frokhar-Jensen39 subsequently carried out rigorous electron microscopic studies in which he sequentially sectioned capillary endothelia in 7.5 nm sections from the interstitium to the lumen. He found that so-called “vesicular transporters” were in fact blind invaginations into the cell, which appeared to be circular “vesicles” on cross-section. However, a few of these (1/50,000) could be observed to fuse together to form a large pore. These pores offer no resistance to the passage of small solutes and minimal resistance to large solutes, and they account for 3-5% of the total pore area. There is essentially no osmotic or oncotic force across the pore and therefore hydrostatic pressure dominates in the Starling relationship. Typical capillary hydrostatic pressures vary from 9.5 mmHg in the rat40 to 18 mmHg in the human.41 The interstitial pressures are typically -0.4 to -0.5 mmHg41,42 for both species. Therefore, transport is one-way out of the capillary because there is almost always a 10 mmHg hydrostatic pressure gradient out of the capillary. Since their transport is dominated by convection under normal conditions, large proteins will pass from the circulation to the interstitium but will not return. The chief role for lymphatics within the tissue is the return of this protein and fluid from the interstitium to the general circulation. Although fluid and small solutes are carried out with the protein in the convection through the large pore, the total solute or fluid flow is small compared with that through the other two pores.43 The mathematical approach to this theory modifies Eqs. [3.1] and [3.2] as follows. The volume flux across the capillary wall is as follows:

J total = J TC + J Sv + J vL = L p ( ∆P - Σ 3j=1α j ( Σi σ i, j ∆ π i )) v v [Eq. 3.3] where: Jvtotal = net total volume flux across the capillary; JvTC = volume flux across the transcellular pores; JvS = volume flux across the small pores; JvL = volume flux across the large pores. αj = fraction of Lp accounted for by pore ‘j’; Σj αj = 1; σi,j = reflection coefficient of pore ‘j’ for solute ‘i’. An analogous equation for the solute flux sums the contributions of both the small and the large pores. (By definition, no solute flux occurs across the transcellular pore.)

J total = J iS + J iL = Σ 2j=1( pi, j ∆ C i + J vj C m,i (1 - σ i,j )) i

[Eq. 3.4]

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With this theory Rippe, Stelin, and Haraldsson have applied fundamental microcirculatory physiology to describe the sieving characteristics of the peritoneal capillary membrane. Figure 3.9 is a plot of the transperitoneal solute clearances for a variety of solute sizes, based on their theory and plotted with calculations from their own data. These clearances are estimated by assuming that the interstitial barrier is negligible and that transfer occurs directly between dialysate and blood across the capillary barrier. The pore RADius curve of 4.7 nm fits solutes from urea to albumin. However, the transition to larger molecules such as IgG or IgM requires introduction of a larger pore of RADius 30 nm. Finally, to calculate the total solute or water transport for a particular tissue element, the flux must be multiplied by the capillary area-density, which has been estimated to be 70 cm2/g tissue.31 While the model can be reasonably fitted to peritoneal solute clearance data, the parameters which are often obtained appear to be unrealistic with respect to the tissue-level mechanisms. Because of the lack of the interstitial barrier, the efficiency of transfer increases substantially, and the capillary surface area in the model of Rippe and colleagues required for the total cavity transfer would be contained in 50 g of tissue; from tissue concentration gradients of small and large solutes, the actual amount of tissue involved in transfer between the blood and peritoneal cavity likely approaches ten times this quantity (see below).8,44 In addition, the simultaneous hydrostatic pressure-driven fluid loss from the cavity and the osmotic flow into the cavity cannot be explained by a single membrane system.

Interstitium After transporting across the blood capillary endothelium, the solute enters the tissue interstitium, through which it must move toward the peritoneal cavity (see Fig. 3.7). Studies in rats which employ quantitative autoRADiography to measure tissue concentration profiles, have demonstrated that the tissue concentration of 14C-EDTA (approximately the molecular size of sucrose), which is injected i.p. and allowed to diffuse into the surrounding tissue for 60 min, does not approach the plasma concentration until a distance of 500-800 mm from the peritoneum (see Fig. 3.10).44 Since the rat peritoneum is 25 µm thick, the finding that the concentration profile extends to hundreds of microns suggests that the majority of the capillaries actively involved with the transport are contained within the underlying tissue. Since individual capillaries are located at variable distances from the cavity, the barrier presented by interstitium will vary depending on the location of the capillary with respect to the peritoneum. That the interstitial barrier is significant is supported by studies of gas transfer from the cavity. If inert gases equilibrate with the blood flowing in capillaries, their rate of transfer should be the same and proportional to the rate of local blood flow, if the interstitium is an insignificant part of the barrier. When Collins45 measured the simultaneous clearance of several inert gases from the peritoneal cavity of piglets, he found a threefold range of gas transfer rate. That the clearance rate of each gas was proportional to its diffusivity in water implied that gas transport was limited by diffusional barriers of the interstitium in conjunction with the blood capillary wall. The interstitial barrier varies among peritoneal tissues due to the tissue type and the pressure forces on the tissue. During its transit through the interstitium, the solute will be excluded from much of the tissue space by cells, collagen fibers, and large molecular weight interstitial matrix proteins, called glycosaminoglycans. Small solutes such as sucrose (MW = 360 daltons) are restricted to as little as 20% of the extravascular space.46,47 Large solutes such as albumin (MW = 58,000 daltons) are excluded from approximately 90% of the extravascular space.47 This tissue exclusion results in proportionate decreases in the rate of diffusion. Added to this is the tortuous path of the solute, which has been estimated to be two to three times the linear distance between two points.48,49 This causes further reduction

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of rates of transport. Depending on the solute charge, it may be further retarded in its progress through the matrix maze. The result of the exclusion phenomena, the tortuousity, and the charge effects is a decrease in the rate of diffusion by 1-2 orders of magnitude. The effective diffusivity or Deff is defined by

Deff = DT θ s

[Eq. 3.5]

where: DT = the diffusivity within the interstitium and incorporates the effects of tortuousity and charge; θs = nonexcluded fraction of tissue which is available to solute. Figure 3.11 displays the estimated diffusivities in tissue (DT) versus molecular weight. The diffusivities in water are plotted for comparison. In order to estimate the relative contribution of the capillary wall and the interstitium to the total peritoneal transport resistance during steady-state conditions, we define the following relationship for small solutes (MW <5,000 daltons), which transport primarily by diffusion:50

Rtotal = 1/pcapillary + (diffusion distance)/Deff

[Eq. 3.6]

where: pcapillary = permeability of the capillary, and diffusion distance = the distance between the capillary and the peritoneal cavity. Using pcapillary = 3.2 x 10-4 cm/min,31 and Deff = 4 x 10-6 cm2/min,46 estimates of the total transport resistance which is due to the interstitium has been estimated for various distances from the peritoneum.50 At a minimal distance of 50 µm, the interstitial contribution to the total resistance is 29%; at 100 µm, 44%; at 300 µm, 71%; and at 600 µm, 83%. For small solutes, therefore, the interstitium presents a significant barrier. Estimates of the interstitial barrier for larger solutes, such as proteins, are complicated by the fact that their transport may be a combination of diffusion and convection coupled with binding to constituents in the tissue. As discussed below, there is limited data with which to model and calculate the transport of large molecules. The presence of the interstitium surrounding the capillaries has significant effects on water transport. The average dialysis patient ingests 1.5-2.0 liters per day of fluid, and, if we assume that the healthy dialysis patient has an insensible loss of 500 ml per day, approximately 1.0-1.5 liters must be removed by dialysis each day. The total accessible area of the peritoneal cavity is likely a maximum of 10,000 cm2.2 As seen in Figure 3.10, the glucose concentration profile (equivalent to the profile for EDTA) extends approximately 500 mm into the subperitoneal tissue. Thus it is likely that approximately 500 mm of tissue from the peritoneum is actively involved with the process of water removal, and the total potential volume of peritoneal tissue involved with osmotic ultrafiltration is on the order of 500 ml (10,000 cm2 x 0.05 cm). Clearly, this volume of tissue cannot form the amount of fluid which is often removed during daily dialysis (1-1.5 liters). Removal of fluid from the patient’s body must occur primarily from the blood capillaries which are contained in this tissue. Unlike an artificial dialyzer in which the capillary carrying blood is in direct contact with the dialysis fluid, peritoneal tissue capillaries are separated from the dialysate by the interstitium. As shown in Figure 3.10, the concentration of the a small solute such as glucose will diminish with the distance from the peritoneum. This means that capillaries which are 300-

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Fig. 3.10. Tissue concentration profiles of a small marker molecule (14C-EDTA, 360 daltons) (replotted from ref. 44). The concentrations have been normalized to the concentration in the cavity at time zero. Concentrations at x = 0 do not match the concentration in the peritoneal cavity because only about 30% of the tissue space is available to the solute. For the same reason, the tissue concentrations appear to be lower than the plasma concentration in most tissues at x = 800 mm from the peritoneum. To find the estimated concentration in the interstitium, these concentrations would be divided by the local fraction of tissue which is available to the solute. The distributed model, which incorporated tissue diffusion and a distributed blood capillary sink, most closely fit profiles from the visceral tissue. Profiles of the diaphragm and abdominal wall were likely higher than predicted because of convection across these surfaces and consequent tissue swelling. This swelling increases the relative volume available to the solute in the tissue, and this results in higher observed tissue concentration.

500 mm from the peritoneum will be exposed to markedly different osmotic forces than those in the 0-200 mm range. In addition to osmotic pressure, hydrostatic pressure varies within the tissue space, depending on the tissue and on distance from the peritoneum. This is most pronounced in the abdominal wall, where the full hydrostatic pressure inside the cavity is exerted across the tissue. Figure 3.12 displays the interstitial hydrostatic pressure profile in the abdominal wall in a rat in which the i.p. pressure is held constant at 6 mm Hg.30 Note that the curves are the same no matter whether the solution in the cavity is isotonic or hypertonic (450 mosm/kg with 4% mannitol). If the peritoneum were a significant barrier to mannitol or dextrose, the tissue pressure adjacent to the peritoneum would be well below zero for the first few hours after introducing the hypertonic solution.51 That the curves are the same signifies that the mesothelium/peritoneum is not an osmotic barrier to small solutes such as dextrose.

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Fig. 3.11. Tissue diffusivity versus free diffusivity in water. Replotted from ref. 83.

The hydrostatic tissue pressure gradient is the driving force for convective fluid loss from the peritoneal cavity into surrounding tissue. The rate of this fluid loss can be described by Darcy’s Law:52 where: K = hydraulic conductivity; A = peritoneal surface area; dP/dx = interstitial pressure gradient or slope of the curves in Figure 3.12.

[Eqn. 3.7] As i.p. pressure is increased, the value of dP/dx increases; Eq. [3.7] predicts that fluid loss will increase, if K and A are constant. Figure 3.3 demonstrates that the rate of isotonic fluid loss from the peritoneal cavity of cats increases as the i.p. pressure rises. Figure 3.13 displays results from similar experiments in rats, in which protein solutions with labeled IgG (used to mark the fluid flux) were infused into the cavity, and the cavity was maintained under constant pressure. The rate of fluid loss was determined by direct measurement, while the rate of lymphatic flow was calculated by the clearance of the labeled IgG from the cavity to the blood.17 The concentration in the abdominal wall, which represents the total flow into the tissue per unit volume of tissue during a three hour solution dwell in the cavity, rises linearly with pressure. In subsequent experiments, dP/dx at the peritoneum (initially 200300 µm from the peritoneum) was determined simultaneously with the rate of flow into a given area of tissue (Q/A). Unexpectedly, dP/dx did not rise proportionately with the increase in i.p. pressure or with the increase in Q/A. From this data and Eq. [3.7], values for

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Fig. 3.12. Interstitial pressure profiles in the abdominal wall of the rat with either no fluid in the cavity or sufficient fluid to maintain i.p. pressure at 6 mm Hg. That the pressure profiles for the hypertonic solution (500 mosm/kg) and the isotonic solution were the same implies that there is no barrier at the peritoneum to the osmotic solute (180 daltons, equivalent to dextrose). Data replotted from ref. 30.

K were calculated and are displayed in Figure 3.14.53 The results clearly demonstrate that the hydraulic conductivity of the abdominal wall tissue increases in linear fashion above an i.p. pressure in the rat of 2 cm H2O (1.7 mmHg). Further experiments have demonstrated that the volume of the interstitial space nearly doubles between 2 and 4 cm H2O of i.p. pressure,54 which in part accounts for the increase in K. However, more research is needed to fully account for the marked decrease in resistance to water flow which occurs with increasing i.p. pressure and presumed stress on the tissue. From the previous discussion, we can now return to our tissue model, which is portrayed in Figure 3.7. Not only is the basic structural unit of the peritoneal transfer system more complicated than that of hemodialysis, but there is now an overlying problem of variability of the tissue space with the pressure imposed by the fluid in the cavity. If the interstitial space (determined by equilibrating the tissue space with the plasma space containing a small marker molecule and determining the ratio of tissue concentration to plasma concentration) swells with the flow of water into the tissue, the space available for small solutes (θs) will increase. From Eq. [3.5], the value for Deff is directly dependent on θs and will increase with hydration. Therefore, rates of diffusion and convection will be significantly affected by not only the driving forces (gradients of concentration and pressure) but also by the degree of tissue swelling or dehydration.

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Fig. 3.13. Fluid and protein movement from the peritoneal cavity of the rat. Rats were dialyzed at constant i.p. pressure with an isotonic solution containing RADiolabeled immunoglobulin G, which acted as a fluid movement marker. The clearance of the tracer into plasma provides an estimate of lymph flow, which did not change with hydrostatic pressure. The total fluid movement from the cavity was paralleled by the fluid movement (protein deposition) into the abdominal wall. Note the threshold pressure of 2 cm H 2O in the supine, anesthetized rat (referenced to the right heart), above which fluid moves out of the cavity and into the abdominal wall. Data is from ref. 17.

Mathematical Models of Peritoneal Transport Traditional “Membrane” Approach In the early days of PD, biomedical engineers adopted from hemodialysis the concept of a single membrane separating blood and dialysate. Figure 3.15 depicts this model in its simplest form: two well-mixed compartments separated by a homogeneous membrane. The model assumes that blood flow is sufficiently rapid in peritoneal tissue that its delivery of small solutes such as urea does not limit their transfer across the peritoneal surface. Dialysis fluid flow does not typically flow continuously as in hemodialysis and therefore does limit transport; the longer the fluid dwells in the cavity, the more closely the dialysate concentration will approach that of the plasma. The rate of solute transfer can be described by the following equation:

d( C D V D ) = MTAC(C B - C D ) + QS Cm dt

[Eq. 3.8]

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Fig. 3.14. Hydraulic conductivity of the interstitium of the abdominal wall versus i.p. pressure. Above the threshold pressure of 2 cm H2O, K rises fivefold between the pressures of 2 and 11 cm H2O. Data is from ref. 53.

where: CD = concentration in the dialysis fluid; CB = concentration in blood or plasma; Q = net ultrafiltration = net rate of flow from the body to the cavity; S = sieving coefficient = the fraction of solute transported with the flow; Cm = mean solute concentration in membrane which is often set = (CB+CD)/2; VD = dialysate volume; t = time; MTAC = mass transfer-area coefficient or MTAC = Σi MTCi x Ai, where: MTCi = the mass transfer coefficient; Ai = peritoneal surface area for each tissue “i.” Since the mass transfer coefficient cannot be separated from the area in the clinical setting, the parameter is an average of all the active transport surfaces; MTAC is also termed “PA” or “KoA.”

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Fig. 3.15. Two-compartment model of peritoneal transport which lumps all transport resistances into a single “membrane-like” barrier and which is used in clinically-oriented mathematical models of peritoneal transport. See text for details.

In vivo observations have demonstrated that all of the elements of Eq. [3.8] are variables, including Q and MTAC. Obviously, to solve the equation, one must make some simplifying assumptions. As outlined by Waniewski,55 there have been several methods used to reach a solution: 1. The plasma solute concentration is assumed constant; 2. Net ultrafiltration is assumed to be unidirectional flow due to osmosis only. Fluid loss due to hydrostatic pressure-induced water flow from the cavity to the surrounding tissue as well as the contribution of lymphatic flow from the cavity back to the plasma is not modeled separately; 3. The peritoneal transport barrier can be described as a homogeneous membrane separating two well-mixed compartments. The properties of the membrane (S, MTAC) are assumed to be constant for the period of the dwell. Implicit with this assumption is that blood flow is assumed to be more than sufficient for transport. Assumption 1 is a reasonable one for small endogenous solutes. Urea and creatinine concentrations drawn during a specific dwell will show nearly constant levels because the rate of removal of these substances during peritoneal dialysis and approximates their rate of generation by the body. This is unlike hemodialysis, in which wide swings in these levels occur during the dialytic period because of the greater efficiency of solute removal. However, if the model intends to include predictions of glucose concentration (which can be quite high in hypertonic solutions) or exogenous drugs such as antibiotics, then some error may be inherent in the assumption of a constant plasma (blood) concentration. Most of the simple models do not attempt to calculate driving forces due to glucose gradients or to estimate the actual osmotic filtration. Nor have any simple models

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incorporated measurements of i.p. hydrostatic pressure. Most often, an empiric equation of the following form is fitted to the volume versus time curve (see Fig. 3.5):21,56

V(t) = V(0) + a1 (1 - exp(-bt)) - a 2

[Eq. 3.9]

where: V(t) = volume of peritoneal cavity (including residual volume), a function of time ‘t’; V(0) = initial peritoneal volume (includes residual volume); a1, a2, b = arbitrary constants which are determined from fitting Eq. [3.9] to volume curves such as Figure 3.5. Because of the complexity of water transport across the peritoneum, empirical approaches in the clinical setting are reasonable in the description of the water transfer. Unfortunately, neglecting the flow from the cavity may lead to some strange values for S, which should theoretically range from 0 to 1. This author as well as others have obtained values for S >1 or <0 in attempting to fit this equation to actual data.57 Often, S is indeterminate in the fitting procedure and is set equal to 1 or 0.58,59 Assumption 3 actually contains several implicit assumptions. That the entire peritoneum can be considered a homogeneous membrane assumes that all peritoneal surfaces have similar transport coefficients and can be lumped together in an overall transport coefficient. In addition, there is the assumption of no blood flow limitation. Lumped Peritoneal Parameter Assumption Experiments in patients are limited to collection of all of the peritoneal dialysate (minus residual volume) and measurement of the volume and mean concentration. Since one cannot differentiate fluid which came from the surface of the abdominal wall from that which came from the surface of the liver, a lumped model, such as that portrayed in Eq. [3.8], is the most appropriate. To examine the assumption of lumping all surfaces into a single uniform membrane, experiments were carried out in rats to determine the value of MTC for specific surfaces of the peritoneum. 4 The animals were anesthetized and the peritoneum was exposed via a midline opening in the abdominal wall. Plastic chambers were glued to the surfaces of the liver, cecum, stomach, or abdominal wall. The chamber was filled with an isotonic solution, which could be sampled and the volume of which could be precisely measured. In one set of experiments, 14C-mannitol was included in the chamber solution and its disappearance from the chamber was measured. In a second set of experiments, the tracer was infused i.v. to set up a constant plasma concentration, and its appearance was determined. Since the base of the chamber defined a precise area and the volume change was negligible, Eq. [3.8] with CD replaced by the concentration in the chamber (CB = 0 for chamber to body transport and CB = constant during i.v. infusions) could be used to calculate the MTC. The results are illustrated in Figure 3.16 and demonstrate that there are no significant differences among MTCs for different tissues or for different directions of transport.4 Similar results were obtained for urea, but the magnitude of MTC was higher than that of the larger molecule mannitol. For small molecules, these studies support the lumping of the entire peritoneum into a single barrier. These results were initially disturbing since theoretical predictions had previously shown that the liver, with its high capillary permeability-area density, would have an order of magnitude higher MTC x A.60 Blood Flow Limitation In order to address the question of whether blood flow limits urea transfer across the peritoneum, the chamber technique was modified to measure the local blood flow in

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Fig. 3.16. Mass transfer coefficients for the bidirectional transport of 14C-mannitol across the peritoneum. The labeled mannitol is injected i.v. or into a chamber glued to the peritoneal surface of four specific tissues. Rates of transport are determined by the appearance or disappearance of the solute from the chamber. Data has been replotted from ref. 4.

the tissue directly under the chamber with a laser Doppler flowmeter and simultaneously determine the MTC for 14C-urea. The experiment was run under control conditions and after the blood flow was reduced by 70%. Of the four tissues (abdominal wall, cecum, stomach, liver), only the liver demonstrated a decrease in MTC with the drop in blood flow.61,62 Zakaria and Rippe63 have recently shown that the liver is responsible for a very small amount of the total transport area in rats, and therefore a drop in blood perfusion would have limited effects on solute transfer during dialysis. In unpublished studies, analogous chamber experiments have been carried out to examine whether blood flow limits osmotic water flow across the peritoneum. With decreases in blood flow of 50-70%, osmotic water flow was observed to decrease in most tissues but the changes were not statistically significant.64 These data concur with previous studies of PD in dogs during conditions of shock65 and support the use of PD for solute removal during times of low systemic blood pressure and presumably low perfusion of peritoneal tissues. The blood flow limitation in the liver also explains the relatively low MTC for this highly vascular organ. All of these data support the assumption that blood flow does not significantly limit the overall transport of small solutes and water across the peritoneum. Peritoneal Area of Contact with Dialysate Although MTC cannot be separated from A in clinical experiments, the peritoneal surface area is often estimated from dissection of cadavers. 2 The rate of mass transfer between the blood and the peritoneal cavity is directly dependent on the area of the

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peritoneum in contact with dialysis fluid in the cavity (see Eq. [3.8]). Beyond a certain volume of fill, it is often assumed that the area of contact is a maximum and approximates the total measured area if one dissected all the internal organs and summed their measured areas.2 Keshaviah and colleagues66 demonstrated a direct relationship between MTAC and i.p. fill volume between 0.5 and 2.0 L. The curve peaked for patients with an average body surface area (1.7 m2) at a volume of approximately 2.5 L, while the MTAC for larger patients peaked at 3.5 L. Although increases in MTC cannot be ruled out, they concluded that most of the increase in MTAC is likely due to recruitment of peritoneal surface area. Results of animal studies indicate that a much smaller area than the total area found on dissection is available for transport. In studies in rats,4 the individual MTCs (measured in the chamber experiments) of different surfaces of the peritoneum were multiplied times the dissected area of each of the surfaces after dissection. All the MTC x A products of all the tissues surrounding the cavity were summed in order to estimate the MTAC for the whole cavity. The MTAC for the entire cavity was measured in a large volume of dialysis solution (50 cc in 300 g rat) and was found to be 25-30% of the sum of the MTACs. A separate set of animals was then dialyzed with an intensely staining dye in order to leave a record of those surfaces which came in contact with the solution. Large parts of the peritoneal surface had no staining: i.e., one side of the cecum, colon, and the stomach, and large parts of the abdominal wall and diaphragm. Although qualitative, these observations demonstrated that large portions of the peritoneum were untouched by solution while the animal was in an anesthetized, quiescent state. Levitt and colleagues67 measured the rate of transport of urea, creatinine, and glucose in rats at rest or agitated with an orbital shaker; they found a fourfold increase in MTAC, in good agreement with our studies. Others68 have carried out studies analogous to Levitt and have found similar results. All of these studies in animal models imply that fluid in the quiescent peritoneal cavity comes in contact with only about 25-30% of the total peritoneal area. The fact that such a large portion of the peritoneum is not available to the fluid may account for variability in the technique. Rates of transfer could vary by 20% without much increase in surface area. Bowel contents, eating habits and postsurgical adhesions between tissues may all affect the distribution of fluid to the peritoneal surface, which will determine the final MTAC. Clinically-Useful Models With the degree of variability in peritoneal surface area in contact with the fluid but the relative equivalence of different organ surfaces in exchange, simple models based on two well-mixed compartments separated by a homogeneous membrane barrier are useful representations of peritoneal exchange. These are thoroughly discussed in the reviews by Waniewski.55 Some authors have attempted to model more of the details of the system. Mass and water balances are taken around each compartment and the known inputs and outputs of each are included. The mass balance around the body compartment is as follows:55,69-71

d(V B C B ) = G - MTAC( C B - C D ) - QS C m - Cl R C B + F L C D dt [Eq. 3.10]

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where: G = rate of generation in the body; VB = volume of distribution in the body compartment; note that this can vary substantially from anatomical compartments. For example, VB for urea would equal the total body water, while VB for an immunoglobulin would be the plasma space. Cm = CB - k1(CB - CD); k1 = MTAC/QS - 1/(eQS/MTAC - 1); FL = total fluid loss rate from cavity to surrounding tissue and includes both lymphatic loss and hydrostatic pressure-driven fluid flow. This flow can be determined empirically by the slope of volume curve after it has peaked at 3-4 hours. Typically this can be computed after a long, overnight exchange and is generally in the range of 60-90 ml/h;18,19 ClR = residual renal clearance. The reader will note that QS/MTAC is equivalent to the Peclet number (ratio of the convective coefficient to the diffusive coefficient of transport) for this system. The general equation for solute transfer is:

d (V D C D ) = MTAC( C B - C D ) + QS C m - F L C D dt

[Eq. 3.11]

The volume balance on the cavity is as follows:

Q =

dV D = J v A - F L = Lp A( ∆ P - Σni=1σ i ∆ π i ) - F L dt [Eq. 3.12]

These formulations include the total fluid loss from the cavity, the total osmotic ultrafiltration into the cavity, variable volume compartments, and variable concentrations in both compartments.55,69,72,73 Two models account for contributions from three pores of the capillary membrane,69,70,72 and have adopted the three-pore capillary model to calculate transperitoneal water flow. Gotch and Keen74 have taken an empiric approach, which fits equations to the patient data. Vonesh and Rippe70 attempt a blend of physiology with empirical fits to patient data. Because of the great variability in day-to-day conditions in a single patient, a priori predictions are seldom exact. Simplifying assumptions permitted three of these models to be solved analytically. This has permitted their application to clinical situations via user-friendly computer programs: PD-ADEQUEST (Baxter Healthcare Inc.),70 PACK-PD (Fresenius USA Inc)73,74 and PDC (Gambro).72 Each of these has been tested in patient populations of approximately 100, and each adequately estimates urea and creatinine clearance. Because the Rippe 3-pore capillary model mimics the sieving characteristics of the capillary membrane, the extension to larger molecular weight species may be easier than with the models which empirically fit the solute curve. Parameter Data for the Membrane Models There is abundant data for the mass transfer-area coefficient, MTAC. Rippe and Krediet75 have plotted values based on their own work and that of others. The resulting values are similar to the curve in Figure 3.9. These values are a good starting point in the estimation of this parameter. Individual patients may vary considerably from these mean values derived from study patients, and one of the patient-specific programs mentioned above may be

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used along with clinical data from the patient to obtain more precise results. If human data for a specific compound is not available, it can be estimated from animal data by using an empirically-derived relationship: MTAC α (body weight)0.7.76 For example, some molecular weight markers are not available in forms which can be administered to humans, and therefore we must rely on animal experiments. Leypoldt and Henderson9 demonstrated in rabbits the significant effect of molecular charge on MTAC, with the positively charged dextrans (molecular RADius 2.0-4.0 nm) having MTACs approximately 50% of those with neutral or negative charges. With Dedrick’s scaling criteria (scaling factor = {body weight}0.7), the values could be converted to estimates of the human parameters. The sieving coefficient has been reviewed by Leypoldt and Mistry.57 Results between animal experiments and human data correlate well, with ranges for S for urea being 0.6-0.9 and values for sodium being 0.55. These values are not equivalent to the value (1 - σcapillary), since σcapillary is less than 0.05 for these compounds.34 The discrepancy between S and σcapillary was removed by introduction of the transcellular pore or aquaporin, through which nearly 40% of the water will flow, but no solute. Values may differ depending on the direction of transport. The most striking difference arises with proteins, which have values of S <0.1 in the blood-to-dialysate direction,57 but S approximates 1 in the reverse direction. The great differences between values of S for transport of macromolecules arises because the simple models do not include the interstitial barrier in the model system. Values for the total fluid loss FL vary between 60 and 90 ml/h for healthy PD patients.13,18,20,77 The lymphatic portion of this flow has been calculated as 10-20 ml/h, with the remainder presumably due to hydrostatic pressure-driven fluid loss to the tissue. All of these studies were carried out in small groups of patients without measurement of i.p. pressure. As implied in Figure 3.4, the i.p. pressure is dependent on the volume in the cavity and the position of the patient. Animal studies have demonstrated that the lymph portion of this flow is sensitive to respiratory rate and depth of respiration.78 Distributed Model Approaches While mass and water transfer across the peritoneum can be described by the approaches given above, they do not address the underlying mechanisms of transport at the tissue level. In order to explore more fundamental mechanisms at work in peritoneal dialysis, several groups have proposed the use of the “distributed model” approach. This concept includes the microcirculatory exchange vessels distributed within a tissue space. Simplifying assumptions of uniformity of capillary permeability-area density within the tissue interstitium are generally made in these models. Inclusion of variability of the tissue space or of transport coefficients has generally not been attempted. The chief problem with these models is that the data necessary to fit them requires invasive techniques, which, at present, can only be carried out in animals. These approaches will be briefly reviewed, but more detail can be obtained from individual research papers. In an effort to more closely model the physiology of Figure 3.7, Flessner and colleagues79 developed an unsteady-state, unidirectional formulation which modeled the interstitium as a rigid porous medium. The model simulated diffusion and a distance-averaged rate of convection in the interstitium, with simultaneous blood capillary uptake in a tissue bed with uniformly dispersed blood capillaries. Later the conceptual model was supported with in vivo concentration profiles of a small solute transporting from the peritoneal cavity into surrounding tissue primarily by diffusion.44 However, the authors were unable to fit the model to tissue concentration profiles which resulted from the transport of macromolecules (convection-dominated transport) from the cavity.80 Seames et al51 improved on the original model by incorporating the theories of An and Salathe 81 to simulate interstitial fluid movement. In their model, the hydraulic conductivity and tissue void space

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(porosity) vary with the local interstitial hydrostatic pressure. Unfortunately, they had no intratissue data to validate their theory of convection, and they made no attempt to model the transport of proteins, which had been demonstrated experimentally.80,82 A current approach (a work in progress being guided by experimental work) is based on a mathematical model which incorporates previous work79 with modifications to take into account recent theory51,81 and experimental findings80,82 in the transport process. The model and the previous experimental plan focused on hydrostatic pressure as the chief driving force for convection. The general balance equation for a solute transporting via unidirectional, unsteady-state diffusion and convection in the interstitium can be formulated in the following equation:

[Eq. 3.13] where: CT = CT(x,t) = free interstitial solute concentration; x = distance from the peritoneum; t = time; Θs = Θs(PT) = solute void fraction or fraction of tissue space available to the solute, dependent on the local interstitial hydrostatic pressure (PT(x)); it will be assumed that Θs = BsΘif, where Bs = constant for each solute and Θif = interstitial void fraction or fraction of tissue space made up of interstitial water. Deff = DvoidΘs, where Dvoid = solute tissue diffusivity within the solute void space; f = solute retardation factor = ratio of solute to solvent velocity; K = K(Θif,Cmatrix) = the specific hydraulic conductivity; Cmatrix = concentration of matrix components which determine the resistance to flow; Jsac = local solute exchange with distributed blood or lymph capillaries, where Js = transendothelial solute flux, ac = the capillary area per unit area; nbind = net rate of binding of free solute to local tissue; nmetab = rate of metabolism in tissue; ngen = generation rate of solute within tissue. For solutes that are not bound to the tissue, CT equals the concentration in the solute void space or CTIS (the measured concentration based on the entire volume of tissue) divided by Θs. For proteins which bind to tissue, CTIS equals the sum of Cfree and Cbound. The binding characteristics of each protein tracer need to be determined experimentally with an appropriate technique.8 Unless there are specific receptors for the protein in the tissue, binding is typically nonsaturable. Both saturable and nonsaturable binding are dependent on time of exposure of the tissue to the agent.8 If the rates of local synthesis or metabolism by cells within the tissue are significant, they must be included in the mass balance. The volume balance on the interstitium is as follows:

∂Θ if ∂ ∂P = [Θif K T ] + J c ac ∂t ∂x ∂x

[Eq. 3.14]

where: Jcac = local volume flux to the blood or lymph capillaries. Eqs. [3.13-3.14], along with rate equations for blood or lymph capillary transport and appropriate boundary conditions are solved for values of tissue concentration (CT) versus time and distance. Jsac is set equal to the rate of transcapillary transport minus lymph flow per unit volume of tissue. If proteins are modeled, it is assumed that proteins do not transfer

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directly from the interstitium to the blood, and therefore the Jsa is set equal to the lymph flow. Some of the intratissue coefficients are being obtained in animal experiments. Tissue diffusivities (Fig. 3.11), tissue hydraulic conductivity (Fig. 3.14), capillary permeabilities (see Fig. 3.9), and even tissue-specific mass transfer coefficients (see Fig. 3.16) have been determined utilizing in vivo animal models. While their dependency on tissue hydration is being investigated, there is great need for more data concerned with the changes in tissue parameters with continued exposure to hypertonic dialysis fluid. Inflammatory conditions such as bacterial peritonitis are known to result in major changes in transport rates, but studies at the level of the tissue are nonexistent. While these more complex models may never be directly applied to patients, their ability to focus research on the mechanisms of transport will continue to be invaluable.

Notation (L = length, M = mass, T = time, P = pressure)

A ac a1,a2 b Ci Clr Cm Deff f FL G K Js Jv LP MTC MTAC n p P Q R S t V x Greek: α σ π Θ Subscripts: i

area of the peritoneum in contact with dialysis fluid (L2) capillary area-density (L2/M tissue) constants of the empiric peritoneal volume versus time Eq. [3.9] (L3) rate constant of Eq. [3.9] (T-1) concentration in compartment “i” (M/L3) residual renal clearance (L3/T) mean concentration (M/L3) effective tissue diffusivity (L2/T) = DTΘs = solute diffusivity in the tissue void, where Θs = fraction of tissue available to solute. solute retardation factor = solute velocity divided by solvent velocity fluid loss rate (L3/T) generation rate (M/T) tissue hydraulic conductivity (L2/P/T) solute flux (M/T/L2) volume flux (L3/T/L2) membrane hydraulic conductivity (L3/T/L2) mass transfer coefficient (L/T) mass transfer-area coefficient (L3/T) mass flow/unit volume of tissue (L3/T/L3 tissue) capillary permeability (L/T) hydrostatic pressure (P) net ultrafiltration rate (net fluid removed from patient/unit time, L3/T) resistance (T/L) sieving coefficient time (T) volume (L3) linear distance, typically perpendicular to the surface of the peritoneum in a one-dimensional model, (L) fraction of membrane Lp capillary reflection coefficient osmotic pressure fractional space of tissue solute or compartment or tissue

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j r D B m s if bind metab TIS free T bound Superscripts: total tc S L

pore number renal dialysate blood mean solute interstitial fluid binding metabolism tissue unbound refers to tissue void space refers to solute bound total transcellular pore small pore large pore

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44. Flessner MF, Fenstermacher JD, Dedrick RL et al. A distributed model of peritoneal-plasma transport: tissue concentration gradients. Am J Physiol 1985; 248:F425-F435. 45. Collins JM. Inert gas exchange of subcutaneous and intraperitoneal gas pockets in piglets. Respir Physiol 1981; 46:391-404. 46. Schultz JS, Armstrong W. Permeability of interstitial space of muscle (rat diaphragm) to solutes of different molecular weights. J Pharm Sci 1978; 67:696-700. 47. Bell DR, Watson PD, Renkin EM. Exclusion of plasma proteins in interstitium of tissues from the dog hind paw. Am J Physiol 1980; 239:H532-H538. 48. Brookes N, Mackay D. Diffusion of labelled substances through isolated rat diaphragm. Br J Pharmac 1971; 367-378. 49. Page E, Bernstin RS. Cat heart muscle in vitro. V. Diffusion through a sheet of right ventricle. J Gen Physiol 1964:1129-1140. 50. Flessner MF. The importance of the interstitium in peritoneal transport. Perit Dial Int 1996; 16:S76-S79. 51. Seames EL, Moncrief JW, Popovich RP. A distributed model of fluid and mass transfer in peritoneal dialysis. Am J Physiol 1990; 258:R958-R972. 52. Levick JR. Flow through interstitium and fibrous matrces. Q J Exp Physiol 1987; 72:409-438. 53. Zakaria ER, Lofthouse J, Flessner MF. In vivo hydraulic conductivity of muscle: Effects of hydrostatic pressure. Am J Physiol 1997; 273:H2774-H2782. 54. Zakaria ER, Lofthouse J, Flessner MF. Fluid loss from the peritoneal cavity is dependent on dilution and washout of the interstitial matrix. (abstract) J Am Soc Nephrol 1997; 8:294A. 55. Waniewski J, Werynski A, Heimburger O et al. A comparative analysis of mass transport models in peritoneal dialysis. ASAIO Trans 1991; 37:65-75. 56. Pyle WK, Popovich RP, Moncrief JW: Mass Transfer Evaluation in Peritoneal dialysis. New York: Masson, 1981:32-52. 57. Leypoldt JK, Mistry CD. Ultrafiltration in peritoneal dialysis. In: Gokal R, Nolph KD, eds. The Textbook of Peritoneal dialysis. Dordrecht: Kluwer Academic Publishers, 1994:135-160. 58. Garred LJ, Canaud B, Farrell PC. A simple kinetic model for assessing peritoneal mass transfer in chronic ambulatory peritoneal dialysis. ASAIO J 1983; 6:131-137. 59. Henderson LW, Nolph KD. Altered permeability of the peritoneal membrane after using hypertonic peritoneal dialysis fluid. J Clin Invest 1969; 48:992-1001. 60. Flessner MF, Dedrick RL. Role of the liver in small solute transport during PD. J Am Soc Nephrol 1994; 5:116-120. 61. Kim M, Lofthouse J, Flessner MF. A method to test blood flow limitation of peritonealblood solute transport. J Am Soc Nephrol 1997; 8:471-474. 62. Kim M, Lofthouse J, Flessner MF. Blood flow limitations of solute transport across the visceral peritoneum. J Am Soc Nephrol 1997; 8:1946-1950. 63. Zakaria ER, Carlsson O, Sjunnesson H et al. Liver is not essential for solute transport during peritoneal dialysis. Kidney Int 1996; 50:298-303. 64. Demissachew H, Lofthouse J, Flessner MF. Does blood flow limit osmotic water flow into the peritoneal cavity? (abstract) J Am Soc Nephrol 1997; 8:281A. 65. Erbe RW, Greene JAJ, Weller JM. Peritoneal dialysis during hemorrhagic shock. J Appl Physiol 1967:131-135. 66. Keshaviah P, Emerson PF, Vonesh EF et al. Relationship between body size, fill volume, and mass transfer area coefficient in peritoneal dialysis. J Am Soc Nephrol 1994; 4:1820-1826. 67. Levitt MD, Kneip JM, Overdahl MC. Influence of shaking on peritoneal transfer in rats. Kidney Int 1989; 35:1145-1150. 68. Zakaria ER, Carlsson O, Rippe B. Limitation of small-solute exchange across the visceral peritoneum: effects of vibration. Perit Dial Int 1996; 17:72-79. 69. Vonesh EF, Rippe B. Net fluid absorption under membrane transport models of peritoneal dialysis. Blood Purif 1992; 10:209-226. 70. Vonesh EF, Lysaght MJ, Moran J et al. Kinetic modeling as a prescription aid in peritoneal dialysis. Blood Purif 1991; 9:24-270.

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71. Robertson BC, Juhasz NM, Walker PJ et al. A prescription model for peritoneal dialysis. ASAIO J 1995; 41:116-126. 72. Haraldsson B. Assessing the peritoneal dialysis capacity of individual patients. Kidney Int 1995; 47:1187-98. 73. Gotch F, Lipps BJ. PACK PD: A urea kinetic modeling computer program for peritoneal dialysis. Perit Dial Int 1997; 17(Suppl 2):S126-S130. 74. Gotch F, Keen M. Kinetic modeling in peritoneal dialysis. In: Nissenson AR, Fine RN, Gentile DE, eds. Clinical Dialysis. 3rd ed. Connecticut: Appleton and Lange, 1995:343-76. 75. Rippe B, Krediet RT. Peritoneal physiology-transport of solutes. In: Gokal R, Nolph KD, eds. The Textbook of Peritoneal dialysis. Dordrecht: Kluwer Academic Publishers, 1994: 69-114. 76. Dedrick RL, Flessner MF, Collins JM et al. Is the peritoneum a membrane? ASAIO J 1982; 5:1-8. 77. Rippe B, Stelin G, Ahlmen J. Lymph flow from the peritoneal cavity in CAPD patients; In: Maher JF, Winchester JF, eds. Frontiers in peritoneal dialysis. New York: Field, Rich, 1986: 24-30. 78. Yoffey JM, Courtice FL. Lymphatics, lymph and lymphomyeloid complex. London: Academic Press, 1970. 79. Flessner MF, Dedrick RL, Schultz JS. A distributed model of peritoneal-plasma transport: Theoretical consideratons. Am J Physiol 1984; 246:R597-R607. 80. Flessner MF, Dedrick RL, Reynolds JC. Bidirectional peritoneal transport of immunoglobulin in rats: Tissue concentration profiles. Am J Physiol 1992; 263:F15-F23. 81. An K-N, Salathe EP. A theory of interstitial fluid motion and its implications for capillary exchange. Microvasc Res 1976; 12:103-119. 82. Flessner MF, Dedrick RL. Monoclonal antibody delivery to intraperitoneal tumors in rats: Effects of route of administration and intraperitoneal solution osmolality. Cancer Res 1994; 54:4376-4384. 83. Flessner MF: Peritoneal dialysis system: Importance of each component. erit Dial Intl 1996;17(suppl 2):S92-S95. 84. Flessner MF, Dedrick RL: Intraperitoneal chemotherapy. In: Gokal R, Nolph KD: The Textbook of Peritoneal dialysis. Dordrecht: Kluwer Academic Publishers, 1994:769-789.

CHAPTER 4

The Bioartificial Renal Tubule H. David Humes, Sherrill MacKay and Janeta Nikolovski

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he kidney was the first solid organ whose function was approximated by a machine and a synthetic device. In fact, renal substitution therapy with hemodialysis or chronic ambulatory peritoneal dialysis (CAPD) has been the only successful long term ex vivo organ substitution therapy to date.1 The kidney was also the first organ to be successfully transplanted from a donor individual to an autologous recipient patient. However, the lack of widespread availability of suitable transplantable organs has kept kidney transplantation from becoming a practical solution for most cases of end stage renal disease (ESRD). Although long-term chronic renal replacement therapy with either hemodialysis, hemofiltration, or CAPD has dramatically changed the prognosis of renal failure, it is not a complete replacement therapy. Current treatment only provides filtration function (usually on an intermittent basis) and does not replace the homeostatic, regulatory, metabolic, and endocrine functions of the kidney. The reabsorptive functions of the kidney include not only sodium and water balance but also reclamation of metabolic substrates including glucose and essential amino acids. The kidney also serves as a critically important metabolic organ, synthesizing glutathione and its free RADical scavenging enzymes as well as providing gluconeogenic and ammoniagenic capabilities.2-4 The kidney has a considerable hormonal role, with production of the active form of vitamin D3 and multiple cytokines critical to inflammatory and immunologic regulation.5,6 Loss of these important metabolic, synthetic and endocrinologic functions could be responsible for high rates of complications from renal failure. A possible explanation for high mortality may include the possibility that hemodialysis and hemofiltration replace only the excretory capacity of the kidney but not other critical components of renal function. Various forms of artificial kidneys have been utilized during the past fifty years during which renal replacement systems have been used clinically.7 Hemodialysis, a mass exchange procedure which utilizes a membrane separation device, has evolved from the use of large, nondisposable flat-plate dialyzers to disposable parallel-plate dialyzers and coil dialyzers.7 The widely used dialyzer of today involves a hollow fiber design. Artificial kidney design has progressed with the aid of versatile biomaterials and the principles of mass transfer and fluid dynamics. The tissue engineering approach to renal replacement can incorporate these engineering principles along with biological components and the latest techniques of cell culture and genetic engineering.8 Use and packaging of synthetic materials, biological compounds and cellular components of specific tissue can be envisioned to replace physiologic functions of diseased organs. An effective bioartificial renal tubule as a combination of renal epithelial cells on synthetic membranes has been developed. This bioartificial device may optimize treatment of renal failure by adding metabolic, reabsorptive and endocrinologic capacity to current filtration therapy. Development of cell therapy techniques to provide these key functions of the The Artificial Kidney: Physiological Modeling and Tissue Engineering, edited by John K. Leypoldt. ©1999 R.G. Landes Company.

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kidney may enhance current treatment and hopefully have substantial benefits for patients suffering from renal disease.

In Vitro Development and Characterization of the RAD The approach of a tissue engineered construct for renal replacement is to mimic the natural physical forces—to duplicate filtration and transport processes in order to attain adequate excretory capacity lost in renal disorders, as well as to add important metabolic components. In designing a bioartificial kidney for renal replacement, essential functions of kidney tissue must be retained and utilized to direct the design of the tissue-engineering project.9 The excretory unit of the kidney is composed of the filtering unit, the glomerulus, and the regulatory or reabsorptive unit, the tubule. Therefore, a bioengineered kidney requires two main units, a bioartificial glomerulus and a bioartificial tubule, to replace renal excretory function. A bioartificial renal tubule satisfies a major requirement of reabsorbing a large volume of filtrate to maintain salt and water balance within the body. The need for additional tubule equivalents to replace other nephronal segment functions, such as the loop of Henle, in order to perform more refined homeostatic elements of the kidney, including urine concentration or dilution, may not be necessary. Patients with moderate renal insufficiency lose the ability to finely regulate salt and water homeostasis because they are unable to concentrate or dilute; yet they are able to maintain reasonable fluid and electrolyte homeostasis due to redundant physiologic compensation via other mechanisms. Thus, a bioartificial renal tubule, which reabsorbs isoosmotically the majority of the filtrate, may be sufficient to replace required tubular function to sustain fluid electrolyte balance in a patient with end stage renal disease. With the identification of methods to isolate and culture renal tubule cells from kidney tissue, the bioartificial renal tubule, or renal tubule assist device (RAD), is now clearly feasible when conceived as a combination of living cells supported on polymeric substrata. With appropriate membranes and biomatricies, immunoprotection of cultured progenitor cells can be achieved concurrent with long term performance, as long as conditions support tubule cell viability.10-12 The technical feasibility of an implantable epithelial cell system derived from cells grown as confluent monolayers along the luminal surface of polymeric hollow fibers has been achieved.13 These previously constructed devices, however, have used permanent renal cell lines which do not have differentiated transport capability. The ability to purify and grow renal proximal tubule progenitor cells with the ability to differentiate morphogenically may provide a capability for replacing renal tubule function. Due to the high degree of similarity between porcine and human tissue, porcine tissue is frequently used in xenographic applications.14-16 Although human tissue is an option for use in the RAD cartridges, viable human tissue would be difficult to isolate in the quantities necessary. Porcine tissue is accessible and abundant and was chosen as the cell type for the initial stages of development of the RAD cartridges.

Development of the RAD Isolation and Growth of Cells The renal tubule cells to be seeded in the RAD cartridges were harvested with well developed techniques.17,18 Kidneys were isolated from Yorkshire breed pigs weighing 20-30 pounds. The renal cortex was removed, minced and collagenase treated. Purified proximal tubule segments were then obtained through Percoll gRADient centrifugation. The purified proximal tubules were resuspended in a serum-free, hormonally-defined Dulbecco’s modified Eagle (DME)-Ham’s F-12 media. From the isolated progenitor proximal tubules,

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proximal tubular epithelial cells were formed. The proximal tubule cells were maintained in culture for 3 serial passages or 9 cell generations. Primary proximal tubule epithelial cells at second pass were seeded into polymeric hollow fibers. The intraluminal surfaces of the polysulfone hollow fibers were coated with proNectin L (Protein Polymers, San Diego, CA) prior to cell seeding. Once injected into the hollow fibers, the cell suspension was allowed to incubate for 90 minutes. After this incubation, the hollow fiber system was rotated 90˚ and another seeding of cells was performed. This process was repeated two more times to complete a 360˚ seeding of the hollow fiber. The final cell suspension was flushed from the fiber after the final seeding. Culture media was perfused through the extracapillary space (ECS) of the fiber beginning 24 hours after the final seeding. Four days after the cell seeding, the fiber was perfused intraluminally with culture media. The culture media was replaced every 2-3 days to assure a proper nutrient supply. Confluent cell growth along the lumen surface was achieved within 14 days after seeding. Characterization of Cells and Bioreactors To determine whether cell confluency along the lumen surface could be attained, primary porcine proximal tubule cells were initially seeded into single polysulfone hollow fibers as described above. (Fig. 4.1) The single fiber bioreactor was tested for confluency by monitoring 14C-inulin leakage across the fiber membrane. To monitor inulin leakage, the concentration of inulin was measured both in the lumen and the ECS of the bioreactor. Measurements were made under three separate experimental conditions. After baseline measurements, bovine serum albumin was added to the ECS to stimulate reabsorption with an oncotic pressure gRADient. As the final condition, ouabain was added to the ECS to inhibit Na+K+ATPase activity, which is responsible for active transport across the renal epithelium. This simple experiment has yielded convincing results. The percent recovery of inulin within the lumen of a single hollow fiber as a bioartificial tubule averaged greater than 98.9%.13 While maintaining this high inulin recovery, the proximal tubule cells also displayed active fluid reabsorption. Absolute reabsorption across the single fiber membrane has been shown to increase with the addition of albumin and decrease to near baseline levels with the addition of ouabain.13 These results are significant because they suggest that renal epithelial cell lines maintain active transport function after isolation, expansion and manipulation in an in vitro tissue engineered device. Both permanent and primary renal epithelial cells demonstrated this ability. This single fiber bioreactor was the precursor to scaled up RAD cartridges that are currently being used extracorporeally in large animals. The proximal tubule cells demonstrated active sodium and fluid reabsorption in the single hollow fiber bioreactor, but further proximal tubule cell characterization had to be assessed prior to in vivo testing. To characterize the isolated porcine proximal tubule cells, metabolic function of the cells grown on culture plates was determined. Metabolic functions that were tested on the isolated cells included oxygen consumption, glutathione metabolism, glucose production, ammonia production and vitamin D3 activation. Average oxygen consumption rates of porcine proximal tubule cells were determined as a basis for a mathematical model to estimate the maximum length of a cartridge that the oxygen supply could support. Serial passaging of cells has shown no difference in respiration rates in suspended cells from primary culture through six passages or 18 cell generations. Glutathione and its enzymes were detected in measurable quantities in porcine proximal tubule cell homogenate through 9 passages or 27 cell generations. The proximal tubule cells demonstrated gluconeogenesis. Ammonia production of the proximal tubule cells was shown to be regulated by pH changes. Proximal tubule cells grown on culture dishes also converted 25-hydroxyvitamin D3 to

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Fig. 4.1. Single hollow fiber bioartificial renal tubule. A hollow fiber encased in glass was connected at both ends to silicone tubing and sealed with silicone adhesive. Access ports designed into the glass casing allow sampling of the ECS.

1,25-dihydroxyvitamin D3 and were responsive to normal physiologic regulators of 1-hydroxylase, the enzyme in the kidney that is responsible for the conversion to the active form of vitamin D3. After completion of in vitro cell characterization, the isolated porcine proximal tubule cells were seeded into multiple fiber cartridges. Renal epithelial characteristics of fluid and glucose reabsorption were assessed in the multiple fiber cartridges with a single pass perfusion design. The initial multiple fiber cartridges had a fiber lumen surface area of 97 cm2. To amplify metabolic and reabsorptive function, an increase in cell number was required and the lumen surface area of the cartridges was subsequently increased to 0.4 m2 and 0.7 m2 (Fig. 4.2).

Scale-Up Design Considerations for the RAD The design of a bioartificial renal tubule assist device requires modeling and evaluation systems based upon well established biological, chemical and mechanical engineering principles. A fundamental problem that must be solved in designing such a device is to create as prototype a device that will maintain the viability and performance of the cells within a tissue engineered construct. To achieve this goal, adequate oxygenation to all cells must be maintained. Oxygen is known to be a critical and limiting factor for cell growth and viability and, therefore, an adequate supply is essential to cell differentiation and function.19 In a hollow fiber device, the supply of oxygen will be obtained from the convective flow of blood through the ECS, the diffusive transport through the permeable fiber walls to the cells, and the flow of ultrafiltrate through the lumen. Knowing the oxygen uptake rate of cultured cells, a mathematical analysis of oxygen depletion can be performed.

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Fig. 4.2. Multiple hollow fiber renal tubule assist device (RAD) cartridges. Commercially available dialysis cartridges were connected at both ends to silicone tubing. The tubing extensions allowed connections for luminal perfusion and ECS sampling. This picture displays the scale up in luminal surface area of the RAD cartridges from 97 cm2, 0.4 m2 and 0.7 m2, respectively (from bottom to top).

The development of a computational model will serve to assess the effectiveness of the hollow fiber construct, allow for the critical study of specific parameters, and aid in the scale up and overall design of bioartificial tubule reabsorbing unit. Another consideration in bioreactor design is fluid flow and distribution. Convective transport is an important means for nutrient delivery as well as metabolite excretion in this device design. It is critical that fluid flow is well dispersed and distributed. Flow visualization techniques can be used to identify areas of stagnant blood flow in the extracapillary space and to assure uniform flow distribution in the lumen of the fibers in a scaled up bioartificial renal tubule cartridge containing bundles of hollow fibers.

Ex Vivo Performance of the RAD While assessing the in vitro functionality of a RAD unit is an important component of bioreactor design, a more accurate assessment is obtained by monitoring the device in vivo or ex vivo. Before clinical trials can be undertaken with a RAD, extensive testing must be done in large animals where the system can be evaluated under physiologic conditions and can be optimized for functionality and ease of use. The RAD will be used as a component in an extracorporeal circuit where care must be taken to insure proper operating conditions. Conditions under operation should mimic, as closely as possible, those that the cells in the device are accustomed to seeing physiologically. Some important parameters to monitor and adjust are flow rates, pressures, and temperature.

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The bioartificial kidney design consists of a filtration device (a conventional hemofilter) followed in series by the tubule unit (Fig. 4.3). Specifically, blood is pumped out of a large animal using a peristaltic pump. The blood then enters the fibers of a hemofilter, where ultrafiltrate is formed and flows into a second pump. This pump is added to ensure a stable delivery of the flow rate of ultrafiltrate into the fibers of the tubule lumens within the RAD downstream to the hemofilter. Processed ultrafiltrate exiting the RAD is collected and can be discarded as urine. The filtered blood exiting the hemofilter enters the RAD through the ECS port and disperses among the fibers of the device. Upon exit of the RAD, the processed blood travels through a third pump, and is delivered back to the animal. The pressures of the blood and ultrafiltrate are monitored before entering the RAD. Heparin is delivered continuously into the blood prior to entering the RAD to diminish clotting within the device. The RAD is oriented horizontally and placed into a temperature controlled environment. The temperature of the cell compartment of the RAD must be maintained at 37˚C throughout its operation to ensure an optimal environment for the cells. Maintenance of a physiologic temperature is a critical factor in the functionality of the RAD. In setting up the circuit described, initial priming of sterile tubing must be performed prior to attaching the RAD. Priming, or filling the tubing with fluid (typically from an IV bag), is a common and necessary procedure done in conventional dialysis and hemofiltration. Prior to attachment to the circuit, the cell culture media in the RAD is thoroughly rinsed out and the RAD itself is primed with a heparinized solution. Once the tubing is free of air, the RAD device is hooked up to the circuit using sterile technique within a laminar flow hood. As flow is initiated, care must be taken to ensure that the pressures of the fluid entering the device remain below a design threshold. The flow rate of the blood should be kept at a high rate so as to avoid clotting in both the hemofilter and the RAD. Excessive clotting and protein accumulation can impede flow, cause increases in pressure, and lead to an added resistance to diffusion, which is especially critical in oxygen/nutrient delivery to the cells of the RAD. Since the ultrafiltrate is in direct contact with the cells lining the fibers of the RAD, control of the ultrafiltrate flow rate is critical. Hydraulic pressures entering the RAD, as well as transmembrane pressure gRADients, must also be monitored. Functionality and cell adhesion can be adversely affected if shear forces and pressures are not controlled within allowable levels. Concentration gRADients established by either active or passive transport are reliant on the establishment and maintenance of tight junctions. The maximal rate of flow that a cell can withstand is cell and environment specific and must be determined for each system. Net fluid volume should be monitored throughout the operations of the RAD, just as in dialysis. Adjustments should be made to control fluid balance throughout RAD treatment. In particular, fluid is lost as urine and is exchanged with replacement fluid similar to hemofiltration therapy. The tubule unit is able to maintain viability because metabolic substrates and low molecular weight growth factors are delivered to the tubule cells from the ultrafiltration unit and the blood in the ECS. Furthermore, immunoprotection of the cells grown within the hollow fiber is achievable, due to the impenetrance of immunologically competent cells through the hollow fiber. The encapsulating membrane is immunoprotective if the membrane has a pore size that excludes compounds with a molecular weight greater than 150,000 daltons. Rejection of transplanted cells will, therefore, not occur. This arrangement allows the filtrate to enter the internal compartments of the hollow fibers, which are lined with confluent monolayers of renal tubule cells for regulated transport function. Due to the novel nature of the described cell-based renal assist device, one can foresee some potential problems with its use. Excessive transmembrane pressures or inherent defects in the cartridges could lead to the breaking of fibers, the release of xenogeneic cells,

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Fig. 4.3. Schematic of ex vivo use of renal tubule assist device (RAD) cartridge. The bioartificial renal replacement therapy consists of a filtration device followed in series by the RAD cartridge. Blood is pumped from the large animal through the hemofilter. Ultrafiltrate produced from the hemofilter is delivered to the tubule lumens within the RAD cartridge. Circulating blood within the ECS of the RAD cartridge promotes reabsorption of metabolites within the luminal ultrafiltrate. Processed ultrafiltrate and blood samples are collected for analysis.

and a possible immune response. A protective mechanism can be designed in order to address this problem. The use of existing dialysis membrane biomaterials ensures minimal immunologic responses due to contact with the fibers/cartridge. Excessive flow rates or incorrect temperatures, especially of the ultrafiltrate, could lead to loss of functionality. Clotting of the cartridge could also pose a problem, and care should be taken to provide for adequate heparinization. The use of such a device in uremic and non-uremic large animals has shown that inulin leak rates increased immediately after use. After being maintained in culture for two weeks, the inulin leak rates returned to pre-treatment levels. This indicates that the renal cells remain viable and continue to proliferate. Preliminary data in uremic animals suggest that the RAD activates 1,25-dihydroxyvitamin D3 and maintains plasma glutathione levels in the normal range. Metabolic parameters such as ammonia production within the RAD cartridge during large animal studies are being analyzed. A surface area expansion from 0.4 m2 to 0.7 m2 results in an increase in the number of cells to approximately 4 x 109 cells. The use of scaled-up cartridges can, therefore, increase metabolic production. RAD cartridges have been maintained in culture in excess of 2 months after use in large animal studies. Currently, temperature and pressure conditions within the extracoporeal design are being modified to maximize the function of the RAD. These initial ex vivo studies mark significant progress toward the use of RAD cartridges to enhance current clinical treatment for renal failure.

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Conclusion The development of a bioartificial renal tubule device (RAD) has progressed from in vitro cell isolation, design and scale-up, to ex vivo trials in large animals. The RAD has been shown to be functional in terms of metabolic, reabsorptive and endocrinologic characteristics. Looking to the immediate future, further optimization of the system and the conclusion of animal testing will lead to clinical trials in humans. The development of both a bioartificial filtration device and a bioartificial tubule processing unit would lead to the possibility of an implantable bioartificial kidney. The specific implant site for a bioartificial kidney will depend upon the final configuration of both the bioartificial filtration and tubule device. As presently conceived, an autologous endothelial cell-lined bioartificial filtration hollow fiber cartridge would be placed into an arteriovenous circuit using the common iliac artery and vein, similar to the surgical connection for a renal transplant. The filtrate would be connected in series to a bioartificial proximal tubule device so that reabsorbate would be transported and reabsorbed into the systemic circulation. The processed filtrate exiting the tubule unit would then be connected via tubing to the proximate ureter for drainage and urine excretion via recipient’s own urinary collecting system. The development of a bioartificial renal tubule assist device is one step toward the realization of an implantable renal replacement bioartificial device. A functional bioartificial renal tubule as a combination of renal epithelial cells on synthetic membranes has been developed. This bioartificial renal tubule assist device may optimize current treatment of renal failure by adding reabsorptive, metabolic and endocrinologic function. By providing these key functions of the kidney, current renal replacement therapy may be enhanced and hopefully have substantial benefits for patients suffering from renal disease.

References 1. Iglehart JK. The American health care system: The end stage renal disease program. N Engl J Med 1993; 328:366-371. 2. Deneke SM, Fanburg BL. Regulation of cellular glutathione. Am J Physiol 1989; 257:L163-L173. 3. Kida K, Nakato S, Kamiya F et al. Renal net glucose release in vivo and its contribution to blood glucose in rats. J Clin Invest 1978; 62:721-726. 4. Tannen RL, Sastrasinh S. Response of ammonia metabolism to acute acidosis. Kidney Int 1984; 25:1-10. 5. Stadnyk AW. Cytokine production in epithelial cells. FASEB J 1994; J 8:1041-1047. 6. Frank J, Engler-Blum G, Rodemann HP et al. Human renal tubular cells as a cytokine source: PDGF-b, GM-CSF and IL-6 mRNA expression in vitro. Exp Nephrol 1993; 1:26-35. 7. Colton CK and Lowrie EG. Hemodialysis: Physical principles and technical considerations. In: Brenner BM and Rector FC, eds. The Kidney. 2nd ed. Philadelphia: WB Saunders Company, 1981:2425-2489. 8. Humes HD. Application of cell and gene therapies in the tissue engineering of renal replacement devices. In: Lanza RP, Langer R and Chick WL, eds. Principles of Tissue Engineering. San Diego: Academic Press. 1997: 577-589. 9. Humes HD. Tissue engineering of the kidney. In: JD Bronzino, ed. The Biomedical Engineering Handbook. Boca Raton: CRC Press, 1995:1807-1824. 10. Nikolovski J, Poirier S, Funke AJ et al. Development of a bioartificial renal tubule (BRT) for the treatment of acute renal failure (ARF). J Am Soc Nephrol 1996; 7:1376. 11. Ip TK, Aebischer P. Renal epithelial-cell-controlled solute transport across permeable membranes as the foundation for a bioartificial kidney. Artif Organs 1989; 13:58-65. 12. Aebischer P, Whalberg L, Tresco PA et al. Macroencapsulation of dopamine-secreting cells by coextrusion with an organic polymer solution. Biomaterials 1991; 12:50-56.

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13. MacKay S, Funke A, Buffington D et al. Tissue engineering of a bioartificial renal tubule. ASAIO J 1998; 44:179-183. 14. Cozzi E and White D. The generation of transgenic pigs as potential organ donors for humans. Nature Medicine 1995; 1:965-966. 15. Calne RY. Organ transplantation between widely disparate species. Transplant Proc 1970; 2:550-553. 16. Cooper DKC, Ye Y, Rolf JLL et al. The pig as a potential donor for man. In: Cooper DKC, Kemp E, Reemtsma K, White DJG (eds): Xeno-Transplantation. Berlin: Springer, 1991: 481-500. 17. Humes HD and Cieslinski DA. Interaction between growth factors and retinoic acid in the induction of kidney tubulogenesis. Exp Cell Res 1992; 201:8-15. 18. Humes HD, Krauss JC, Cieslinski DA et al. Tubulogenesis from isolated single cells of adult mammalian kidney: Clonal anaylsis with a recombinant retrovirus. Am J Physiol 1996; 271:F42-F49. 19. Colton CK. Implantable biohybrid artificial organs. Cell Trans 1995; 4:415-436.

CHAPTER 5

Percutaneous Access for Peritoneal Dialysis: A Tissue Engineering Approach Jennifer A. LaIuppa and Clifford J. Holmes

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he newly emerging field of tissue engineering, which combines recent advances in the fields of molecular and cell biology with developments in material science, chemical engineering and biotechnology, promises to address clinical needs in many areas of medicine. The ability to fabricate or reconstruct tissues and organs either in vitro or in vivo using engineering principles would allow a wide variety of disease states to be better treated than by conventional therapy. Examples include bone and cartilage reconstruction, periodontal regeneration, liver, pancreas and renal replacement devices, and cardiac prostheses. For a comprehensive overview, a recent book by Lanza et al is recommended.1 Tissue engineering may also play a useful role in the treatment of end stage renal disease with peritoneal dialysis (PD). PD is currently a widely accepted form of renal replacement therapy, with over 110,000 patients being treated worldwide by the end of 1997. PD therapy is based on the ability of the peritoneum to exchange fluid and metabolic products from the blood and surrounding tissue with the dialysis solution. There are many regimens of therapy delivery today, such as continuous ambulatory PD, which uses four to five exchanges of 2-3 liters of dialysis solution per day, or automated PD, which employs a hardware device to automatically exchange even larger volumes of dialysis solution during the night. An extensive review of this therapy can be found by Gokal and Nolph.2 The successful delivery of this therapy, however, requires the use of a permanent indwelling percutaneous catheter which is used daily to infuse and drain dialysis solution into and out of the patient. The most frequent complication associated with these long term devices is infection of the exit site and subcutaneous tunnel, an outcome resulting from the inability of current designs to permit tissue-device integration. The success of tissue engineering in the areas of skin regeneration and wound healing might be leveraged into the design of PD catheters. The incorporation of a substitute matrix composed of a three dimensional polymer scaffold, either alone or in combination with extracellular matrix proteins (ECM), has already proven beneficial in clinical trials. Researchers are also actively studying the effect of exogenous growth factors on wound healing rates and wound closure strengths. In the following chapter, we examine the basis of the tissue response to conventional PD catheters and discuss the potential benefits of applying skin tissue engineering principles to their design.

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PD Catheters

The most widely used PD catheters were developed by Tenckhoff3 and consist of a piece of silicone rubber tubing with two polyethylene teraphthalate (Dacron™) velour cuffs attached to the outer surface (Fig. 5.1). The subcutaneous Dacron™ cuff, situated 2-3 cm below the skin surface, promotes the rapid formation of a fibrous capsule around the cuff, which is thought to stabilize the catheter and form a barrier to microbial colonization of the subcutaneous tunnel. The deep cuff is typically positioned in the abdominal muscle wall and the tissue ingrowth at this site prevents leakage of dialysis solution into the subcutaneous tunnel. PD catheters are placed either by surgical dissection, as are 90% of PD catheters in the US,4 or by peritoneoscopic insertion. Gokal et al recommends that the deep cuff be positioned in the anterior abdominal wall, while the intraperitoneal segment of the catheter be inserted caudally and placed between the visceral and parietal peritoneum.4 The subcutaneous cuff should be positioned no less than 2 cm from the exit site with the catheter extending either downward or laterally from the exit site. Following catheter implantation, PD should be postponed for 10 to 14 days to allow for tissue ingrowth into the cuffs.4 To further enhance healing, the exit site should remain covered with sterile gauze for at least 2 weeks, and the catheter should be anchored to the skin for stabilization. A non-irritating solution is recommended for exit site cleaning.5 Agents such as povidone iodine or hydrogen peroxide which are toxic to cells at bacteriocidal concentrations should not be allowed to enter the sinus tract. After the catheter has healed, routine exit site care should consist of daily cleansing with antimicrobial liquid soap and water, assessment of the exit site, immobilization of the catheter, and protection of the catheter from trauma.

Tissue Response to Current PD Catheters Current PD catheter designs allow safe access to the peritoneal cavity for extended periods of time. However, the tissue response to these catheters may limit their lifespan. The tissue response to catheter implantation is characterized by an initial inflammatory reaction, followed by reepithelialization and granulation tissue formation, and finally collagen deposition and scar tissue formation. During implantation of PD catheters, tissue injury occurs and blood fills the space surrounding the implant as well as the space between the fibers of the Dacron™ cuff. Blood coagulation and platelet aggregation create a fibrin rich clot which reestablishes hemostasis and provides a provisional matrix for subsequent cell migration. Fibrin and plateletderived vasoactive mediators and chemotactic factors attract inflammatory leukocytes into the provisional matrix. Neutrophils are the predominant cell type during the first several days following implantation and serve to cleanse the wound site of foreign particles, including bacteria. Silicone and Dacron™ are also viewed as foreign by neutrophils, which thus attempt to ingest these components of the catheter. Macrophages and lymphocytes arrive next, with macrophages positioned close to the tissue-implant interface. In an effort to ingest the catheter materials, macrophages will often fuse together to form multinucleated foreign body giant cells.6 The intensity and duration of this inflammatory response depend on the size, shape, and chemical and physical properties of the implant material.6 Several studies in animals7-9 as well as humans10 have found that Dacron™ velour implants elicit a severe inflammatory reaction with foreign body giant cell formation persisting for at least one month in animals and 6 months in humans. The presence of a large number of foreign body giant cells within the Dacron™ cuff appears to limit the ingrowth of collagenous tissue into this space.7 In contrast to Dacron™, silicone rubber induces only a mild inflammatory reaction with few if any foreign body giant cells present.9,10

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Fig. 5.1. Tenckhoff PD catheter.

Within hours after injury, reepithelialization of the wound begins.11 The epithelial cells undergo phenotypic changes which result in dissolution of desmosome links between cells and hemidesmosome links between the epidermis and the basement membrane.11 As a consequence, wound epithelial cells have lateral mobility and begin to migrate over the provisional matrix, separating viable and nonviable tissue in the wound. The epidermis continues to migrate downward into the space between the catheter tubing and wound edge. The epithelial cells can not adhere to the silicone rubber and tend to invaginate toward the subcutaneous cuff, creating a sinus tract. This aspect of the tissue response is called epidermal downgrowth. Twardowski and Prowant12 have reported that epidermis starts to enter the sinus within 2-3 weeks after implantation. A study of 18 human catheter tunnels found that the sinus tract is on average 17.6 mm (ranges from 8-33 mm) in length.10 The segment of the sinus tract closest to the exit site is covered with wrinkled epidermis. Extending further into the sinus tract is nonkeratinizing epithelium, granulation tissue, and in the region closest to the subcutaneous cuff a fibrous sheath covers the sinus tract. Migration of epidermis has been found by Twardowski et al10 to proceed until it reaches granulation tissue, anywhere from 1-14 mm from the exit. Using percutaneous animal models, other researchers have found that the epidermis stops migrating when it reaches the collagenous capsule surrounding the cuff.13 These combined results are consistent with the theory that sinus tract epidermal downgrowth is inhibited by either persisting granulation tissue or the collagenous network of the dermis. Cytokines in combination with provisional matrix molecules stimulate migration of fibroblasts and endothelial cells into the wounded area. These cells work together with macrophages present at the wound site to promote tissue repair. Macrophages provide a continuous source of cytokines required for stimulation of fibroplasia and angiogenesis, while fibroblasts actively synthesize proteoglycans and collagen necessary to support cell ingrowth. The blood vessels formed by angiogenesis provide oxygen and nutrients to sustain cell metabolism. Macrophages, fibroblasts, and endothelial cells and their surrounding matrix comprise the granulation tissue. This newly formed tissue is named for its pink,

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granular appearance and may be seen as early as 3-5 days following implantation of a biomaterial, depending on the extent of injury.6 The length of time that the granulation tissue remains at the wound site depends on the type of implant and the implant material. Gangjee et al7 found that the granulation tissue capsule formed by connective tissue around percutaneous and subcutaneous Dacron™ implants was initially the same. However, after 20 days, the granulation tissue capsule decreased in thickness and inflammatory cell content in subcutaneous implants, but remained unchanged throughout the observation period in the percutaneous implants. During the later stages of wound healing, myofibroblasts (fibroblasts with features of smooth muscle cells) are the predominant cell type in the mature granulation tissue.6 These myofibroblasts begin to remodel the granulation tissue into scar tissue. During the reorganization into fibrous scar tissue, there is a gRADual reduction in cells and a simultaneous increase in types I and III collagen.6 As with many other implants, the end stage healing response to silicone and Dacron™ PD catheters is fibrous encapsulation which isolates the implant from the blood supply (Fig. 5.2). A thin non-adherent fibrous capsule surrounds the silicone rubber tubing, while a much more dense fibrous capsule surrounds the Dacron™ cuff. In exit sites greater than 6 months old, tissue ingrowth into the subcutaneous cuff was found to be comprised of foreign body giant cells, collagen, and sparse fibroblasts.10 At the exit site, an open wound with granulation tissue remains even after the catheter has been implanted for several years.10

Complications Associated with PD Catheters Catheter-related complications may account for up to one third of all patient transfers to hemodialysis, with the major complication being infection.4 Other catheter-related complications include dialysate leaks from the peritoneum to the subcutaneous tunnel, fibrin clots in the catheter lumen, omental wrapping of the device, and catheter dislocations within the peritoneum causing outflow failure. The most common types of catheter-associated infections are exit site infections, which are diagnosed as the presence of erythema, skin inderation and/or purulent discharge from the exit site.4 Tunnel infections in the segment between the two cuffs occur less frequently and are often only detected with sonography of the subcutaneous catheter pathway.14,15 Once the infection enters the inner cuff of the catheter, peritonitis is likely to occur within a short period of time;15 catheter-related peritonitis is difficult to resolve with antibiotic therapy. 16 Staphylococcus aureus (40%) and Pseudomonas aeruginosa (12%) are the organisms responsible for the majority of PD catheter infections.17 Uncomplicated exit site infections often respond to appropriate antibiotic therapy, except for those infections due to P. aeruginosa. P. aeruginosa infections are infrequently resolved by antibiotic therapy and result in catheter loss more often than infections due to any other microorganism.17 Infectious complications associated with PD catheters are due in large part to the tissue response to the catheter materials and the presence of an open wound at the site where the device exits the skin. The formation of a sinus tract at the exit site creates an environment which allows accumulation of biological debris and bacteria which, in combination with the local foreign body reaction of the implant, are features that predispose the catheter to infection. In animal experiments, the incidence and severity of infections are greater with long sinus tracts, while short sinus tracts appear to decrease the risk of contamination by decreasing the range of catheter motion outside the skin.18 If an infection occurs, the fibrous capsule surrounding the catheter may prevent an adequate blood supply from reaching the device, which further complicates the condition by making systemic treatment of the infection difficult. As a result, the catheter may need to be removed or replaced.

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Fig. 5.2 Tissue response to current PD catheter designs.

While new procedures and techniques have been incorporated into the clinical practice of PD therapy in the last 10 years (e.g., swan neck catheters, disconnect delivery systems, rhEPO therapy), catheter infection and survival rates have not significantly improved. Exit site infection rates were found to be identical between patients starting PD between 1986-89 and those starting PD from 1990-94.19 Exit site infection rates are reported as 0.6-0.7 per dialysis year,20 and the average lifespan of PD catheters is 2.73 years.21 Infection is the primary cause of catheter removal, accounting for 68% of removals in a recent study.21

Previous Attempts to Solve Access Complications Historically, a variety of implant techniques, catheter designs, and antimicrobial coatings have been studied to see if any could improve healing around the catheter and/or reduce the incidence of infection. The Moncrief-Popovich PD catheter implant technique22 was designed to minimize postoperative contamination of the surgical wound and decrease the risk of bacterial invasion from the external environment during the immediate postoperative period. This technique leaves the external segment of the catheter buried subcutaneously for 3-6 weeks before exteriorization. This external segment is exteriorized via a small incision distal to the subcutaneous cuff. A retrospective multicenter study of this technique showed that exit site infection rates were not significantly different from those found using standard implant procedures. However, peritonitis rates were decreased, suggesting that the Moncrief-Popovich technique may reduce peritonitis by the periluminal route.23 Another implant procedure designed to reduce trauma and contamination situates the catheter exit site in the presternal area, which requires an elongated subcutaneous tunnel.24 Preliminary clinical results indicate that infection rates are not significantly different between presternal and

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abdominal catheters. However, this implant method may prove to be beneficial for selected groups such as obese patients. Several different materials and cuff configurations have been developed to try to reduce infection rates, with little success thus far. While the causes for failure remain uncertain, several of these designs require more complex implant procedures and some cause additional stress and strain at the exit site. In 1983, a catheter with a Gore-Tex transcutaneous cuff and flange was commercially introduced. In this design, the external catheter segment was implanted perpendicular to the skin so the flange would remain in place. A single center clinical study with this design showed a significant decrease in tunnel infections.25 However, no significant difference in peritonitis or exit site infection rates between the Gore-Tex and the Tenckhoff catheter was observed in this study, and subsequent national registry data showed an increased incidence of exit site infection with this device.26 Thermedics (Woburn, MA) developed a catheter fabricated of segmented aliphatic polyurethane. 27 This catheter had a cylindrical terminal that protruded perpendicularly through the skin, a flange positioned in the subdermal plane, and an adjustable porous cuff at the peritoneal entry site. Both the cylindrical terminal and flange were textured to promote tissue ingrowth. However, this design presented several problems in the clinical setting.28 The skirt separated from the terminal in several cases, and the slow tissue ingrowth into the adjustable inner cuff caused dialysate leakage. Antimicrobial coatings, especially silver coatings, have been evaluated in the PD area with conflicting results. In vitro and animal testing with silver coated devices showed that silver can be an effective antimicrobial treatment.29-31 However, several randomized prospective clinical studies have reported no clinical benefit to using a silver coating.32, 33 A wide variety of antibiotic and/or antiseptic treated catheters are also being evaluated for other medical applications.34-36 Early experience with surfactant bonded antibiotics in PD was disappointing.37 Perhaps the use of more effective drug combinations, such as minocyline plus rifampin38 or EDTA,39 or longer term release technologies may prove beneficial in the future. The experiences described above exemplify the enormous challenges faced when attempting to design a long-term percutaneous device which is infection free and fully integrated into soft tissue. The relatively new field of tissue engineering, which has made significant advances in fields such as wound healing and skin regeneration, has the potential to make equally impressive progress towards this goal.

Tissue Engineering Approach to Reduce PD Catheter Complications Tissue engineering is based on the knowledge of normal structure-function relationships in biological tissue. Such knowledge is the key to targeted development of implants that integrate stably into the healing tissue. As discussed above, current PD catheter designs are not well integrated; they are encapsulated by an avascular and acellular scar, and an open wound persists at the exit site. A catheter that could adhere to keratinocytes at the exit site and promote ingrowth of vascularized collagenous tissue in the subcutaneous region would be more stable and less susceptible to trauma and bacterial colonization. To achieve these results, the catheter would have to mimic naturally occurring structures. Textured surfaces, ECM-based coatings and growth factors all show promise as elements of such a catheter. These approaches are discussed in detail in subsequent sections. Each approach is based on the widening knowledge of cellular and tissue physiology. Each has its strengths and weaknesses, and it is possible that future catheter designs might incorporate a combination of some or all of these elements. Future implant designs based on these elements might even accelerate the wound healing process.

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Surface Architecture As discussed above, the suboptimal tissue response to current PD catheter materials predisposes them to infection. Dacron™ velour is surrounded by a dense fibrous capsule and is filled with inflammatory cells and loose collagen, while the smooth silicone rubber surface is surrounded by a non-adherent fibrous capsule. Close vascular structures are absent around both materials. The lack of tissue attachment to the silicone creates a dead space at the interface where extracellular fluids, cellular debris, implant material particles, and bacteria can accumulate. Tissue ingrowth into the cuff and tissue adhesion to the tubing need to improve in order to resolve these problems. Modification of a material’s surface architecture using texturing methods could resolve some of these inadequacies. Surface textures such as porous structures, woven fibers, ridges, pillars, or wells have been shown to affect the tissue response.40 By altering various surface characteristics such as pore size, degree of porosity, interconnectivity of pores, and depth of pores, one can control tissue attachment, inflammatory response, extent of fibrous encapsulation, and type of tissue ingrowth (vascular or collagenous) into the structure. Examples of both microtexturing and macrotexturing approaches follow.

Microtexturing Microtexturing involves the creation of pores, grooves, wells, or ridges on a surface with dimensions on the cellular scale (smaller than 20 µm). Membranes with small pore size or surfaces created by micromachining are the main types of microtextured surfaces that have been studied. A variety of membranes with a range of pore sizes have been screened in both subcutaneous and percutaneous animal models.41-43 Both Brauker et al and Campbell et al screened membranes comprised of a variety of materials with pore size ranging from 0.1-15 µm in subcutaneous animal models. They discovered that membranes with pore sizes too small to allow invasion of host cells (approximately less than 1 µm) were surrounded by a standard foreign body capsule without vessels adjacent to the implant (Fig. 5.3A). However, an altered foreign body capsule (Fig. 5.3B) was induced when implanted membranes had pores large enough (greater than 1 µm) to allow cell entry into the membrane. These capsules were more complex, consisting of scattered macrophages and fibroblasts with capillaries immediately adjacent to the surface of the membrane or within the pores. A closer examination of one of these larger pore membranes (5 µm) revealed that it had 80-100 fold more vascular structures than smaller pore (0.02 µm) membranes, and the close capillary structures persisted for up to one year after implantation.41 In contrast to the findings by Brauker et al that all larger pore membranes (1-15 µm) elicited a desirable tissue response, Campbell et al observed several layers of inflammatory cells in between membranes and the surrounding fibrous capsule when the pore size was 5 to 10 µm. While the mechanism for this altered tissue response is unknown, Brauker and colleagues have hypothesized that because the macrophages within the membrane retain a rounded morphology and do not adhere to the structural elements of the material, their pattern of gene expression is altered from one which favors fibroblast proliferation and collagen formation to one that favors neovascularization. Squier and Collins43 implanted filters with pore sizes ranging from 0.025-8.0 µm percutaneously, rather than subcutaneously as was done in the previous studies. As with the studies described above, Squier and Collins found that pore sizes of greater than or equal to 3 µm allowed extensive infiltration of cells and tissue elements, whereas filters with a smaller pore size had little cell infiltration. Significantly less epithelial migration was observed with filters that allowed cell infiltration, which is believed to be due to soft tissue attachment which restricts the extent of epithelial downgrowth.

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A

B Fig. 5.3. Membranes made from mixed esters of cellulose (Millipore) with 0.45 µm pores (A) and 1.2 µm pores (B). Close vascular structures are adjacent to the membrane-tissue interface (arrows) in membranes with 1.2 µm pores. Reprinted with permission from Brauker JH et al, Neovascularization of synthetic membranes directed by membrane microarchitecture. J of Biomed Mater Res 1995; 29(12):1517-24. ©1995 John Wiley & Sons, Inc.

Chehroudi et al evaluated the potential of horizontal microgrooves rather than interconnecting porous structures to impede epithelial downgrowth on percutaneous devices. Horizontal microgrooves may help to control the cell migratory behavior through contact guidance, a phenomenon largely studied in vitro to control cell migration. V-shaped grooves 10 µm deep and 17 µm across the top were micromachined into epoxy44 and titanium coated45 substrata, and the epithelial cell response to these surfaces was evaluated in a percutaneous animal model. Less epithelial downgrowth was observed on the grooved surface than on the smooth surface. In the smooth implant, the epithelium migrated down

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the length of the implant all the way to the base. In order to determine whether texturing at the subcutaneous level or texturing at the epidermal level contributed more to the decreased epidermal downgrowth, Chehroudi et al46 performed an additional study. In this study the connective tissue contacted either grooved or pitted surfaces, while the epithelium encountered only a smooth surface. Those implants with grooves or pits formed an interface with the connective tissue that inhibited epithelial downgrowth. Implants with a grooved surface were found to evoke a different tissue response than those with a smooth surface. Epithelial cells were elongated and arranged in a multilayer on the smooth surfaces, whereas on the grooved surfaces, the multilayer of elongated cells was interrupted by groups of cells with circular nuclei in or above the grooves.45 These grooved or pitted implants also did not have a distinct fibrous capsule, while the smooth surfaces were surrounded by a thick capsule.46 There are several potential advantages to applying a microtexture to the catheter tubing. Attachment of a 1-3 µm average pore size membrane structure to the tubing between the catheter cuffs may promote attachment of tissue with close vascular structures, thereby creating more highly perfused tissue adjacent to the device. In the transcutaneous exit site portion of the tubing, microtexturing may prevent epidermal downgrowth. However, a texture with interconnecting pores probably should be avoided in this region because bacteria may proliferate within this type of structure. Microtexturing is not currently practical from a manufacturing point of view. Microporous membranes would be difficult to attach without filling the structure with silicone adhesive, and micromachining is difficult to perform on silicone tubing. However, as the mechanisms for the altered tissue response to these types of structures are better understood and advances are made in micromachining, microtexturing is likely to become an important technology for many types of long term implantable devices.

Macrotexturing Macrotextured surfaces contain surface features that are several orders of magnitude larger than the cells they contact. Macrotextured materials with interconnecting pores averaging 100-200 µm have been shown to promote the greatest extent of soft tissue ingrowth, while materials with smaller or larger pores have less complete tissue ingrowth. Therefore, biocompatible materials with these characteristics are good candidates for PD catheter cuffs. Several different processes have been developed to create macroporous silicone and titanium materials, and these materials have been evaluated in various animal models. One such material is a porous silicone structure known as Searematrix™, with an open cell structure and interconnecting pores ranging from 50 to 400 µm in diameter. Developed by Seare et al,47 Searematrix™ is fabricated using a mold of fused sugar particles (Fig. 5.4). Several animal studies have compared the histological response to both Dacron™ and Searematrix™ covered percutaneous drivelines used in artificial hearts. The Searematrix™ had a greater depth of tissue ingrowth,47 superior epidermal apposition, increased vascular area and number of blood vessels, and a marked reduction in thickness of the granulation tissue reaction zone compared to Dacron™.48 In order to determine whether the increased blood supply at the Searematrix™-implant interface reduced implant infection, drivelines with either smooth, Searematrix™, or Dacron™ covered surfaces were implanted into a calf and evaluated using bacterial biofilm techniques.47 After 13 weeks, the only sterile surfaces were those covered with Searematrix™. The smooth surfaces had the greatest amount of bacteria, and the Dacron™ surfaces had an amount of bacterial contamination intermediate between smooth and Searematrix™ covered surfaces. The infection resistance of Searematrix™ was also evaluated in a percutaneous animal model that was innoculated

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Fig. 5.4. Scanning electron micrograph of Searematrix™ porous silicone material.

with S. aureus at the exit site.48 Several weeks after challenge, the Searematrix™ was culture negative more often than Dacron™ controls. Another porous silicone material was fabricated with pore sizes ranging from 18-180 µm using the replamineform process, which uses the skeletal structure of marine invertebrates as a template.49 The tissue response to this material was evaluated in a subcutaneous animal model. After 4 weeks, material with the smallest pore sizes (<50 µm) had tissue occupying 10-15% of the pore volume, while material with larger pore sizes (75-180 µm) had almost complete ingrowth of tissue containing fibrocytes and capillaries. Another alternative to Dacron™ velour cuffs is a titanium mesh flange developed by Paquay et al.50 This flange is composed of titanium fibers that are 50 µm in diameter with a porosity of 86%. Titanium is well established as a relatively biocompatible material. The titanium mesh was implanted into rabbits and the tissue response evaluated after subcutaneous implantation for 5-10 weeks.50 At all of the time points evaluated, the porosity of the mesh was filled with connective tissue containing capillaries with few macrophages and lymphocytes. A thin to medium-thin fibrous capsule which contained fibrocytes and collagen surrounded the flange. These titanium mesh flanges were also percutaneously implanted into rabbits in a two-step procedure and were found to inhibit epidermal downgrowth.51 Macrotextures such as pillars, nodules, and depressions also have the potential to decrease fibrous encapsulation and promote tissue attachment on catheter tubing. The histological response to such materials with a range of surface depths was evaluated in animals for 30 days. Those surfaces with textures of less than 150 µm in height or depth resulted in the formation of complete capsules similar to smooth silicone,40 while in textured implants with a depth of greater than 350 µm, the tissue appeared to grow into the texture rather than over the texture, preventing formation of the fibrous capsule.40,52 Vascularization was also affected by surfaces with greater texture depths. Blood vessels were closer to pillared (100 µm in diameter and 500 µm in height) silicone implants than to the flat control surfaces (10-20 µm vs. 40-80 µm with the flat control), although there was no

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significant difference in the density of the vessels and capillaries between the smooth and pillared devices.52 A number of general requirements for textured materials need to be considered in the context of PD catheters. The material from which these structures are created can significantly affect the tissue response. For long-term implants such as PD catheters, a biocompatible material that does not induce a prolonged foreign body reaction or degRADe over time is desirable. Materials that are flexible, resilient, and have a compliance similar to soft tissue are preferred for percutaneous devices implanted into soft tissue because of the mechanical stresses experienced in the clinical setting. The fabrication procedure, attachment method, and design (flange vs. cuff) also need to be taken into account when incorporating surface architectures into PD catheter designs. Methods for fabricating these textures need to be incorporated into current manufacturing processes. If the texture is not made directly on the catheter surface, but attached to the surface in the form of a cuff, the cuff should be securely attached with an adhesive that would not disrupt the textured surface. Finally, substitution of the cuff with alternative designs, such as subcutaneous flanges, often requires a more complicated implantation procedure, which might discourage widespread use of these devices in a clinical setting.

Surface Treatments Researchers are exploring ways to coat conventional implant materials, because the molecular surface of these implants is recognized by the body as foreign. Although many proteins adsorb to the material surface from the multi-component biological fluid, only a few of these promote cell adhesion. In addition, proteins may adsorb to surfaces in many different configurations, 53 and these proteins may also undergo post-adsorption conformational changes when they contact hydrophobic surfaces such as silicone.54 It is highly unlikely that current PD catheter implants could ever adsorb enough conformationally active protein to prevent the foreign body response. Immobilization of specific proteins, polysaccharides, or peptides directly on the material surface, rather than indirectly via adsorption, could result in a surface that mimics the natural environment of the tissue and promotes selective cell attachment. There are two different approaches to coating catheter surfaces with proteins, polysaccharides, or peptides: these molecules could be immobilized directly on the catheter surface during manufacture or applied as a coating just prior to device implantation. Immobilization allows better control of the surface density and pattern, which may elicit differential responses in cell spreading and proliferation.55 However, retention of biological activity might be less problematic with the second approach, due to extreme conditions encountered during catheter sterilization. Peptides make better candidates for immobilization, as they are more resistant to proteolysis and degRADation than intact proteins and less likely to lose activity during sterilization procedures. Because of their small size, more cell binding regions could be accommodated per unit surface area on the catheter, if desired. The peptide can also be attached to a spacer molecule to permit greater mobility, which could facilitate binding to molecular targets. The use of synthetic peptides or protein polymers,56 based on repeating oligomeric motifs, would eliminate the risk of pathogen transfer inherent when utilizing proteins and polysaccharides of mammalian origin. ECM components, whether whole proteins, peptides, or polysaccharides, make prime candidates for attachment to PD catheters. They appear during key times in wound healing and participate in all aspects of the process. ECM molecules stimulate migration, attachment, proliferation, and differentiation of particular cell types, and even bind growth factors. Coating of the subcutaneous segment of the tubing and cuff with one or more ECM

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molecules may allow specific stages of wound healing and tissue regeneration to be controlled or even enhanced. The advantages and disadvantages of the most promising ECM components are explored in more detail below.

Fibrin The first ECM formed in the wound space and surrounding tissue is the fibrin clot, which is generated by blood coagulation and platelet aggregation and often referred to as the provisional matrix. The major structural protein in this matrix is fibrin, but other proteins including fibronectin, vitronectin, von Willebrand factor, thrombospondin, and growth factors are also present.57 The clot becomes firmly integrated into the tissue, where it provides a scaffold for inflammatory cell and fibroblast migration58-60 and also a reservoir for growth factors, proteases, and protease inhibitors.57 Application of fibrin to the catheter cuff may act as a provisional matrix to enhance cell migration into the cuff. Fibrin is commercially available in some countries in the form of an adhesive designed to mimic the final step in the coagulation cascade where fibrinogen is proteolytically cleaved and converted into fibrin monomer by thrombin. These adhesives consist of two main components: fibrinogen and thrombin. While these adhesives are typically used in surgical situations to stop bleeding, combining certain concentrations of fibrinogen and thrombin can result in a matrix that encourages tissue adhesion.60,61 Application of fibrin adhesive to implants or wound sites has been shown to promote angiogenesis62 and fibroblast migration.63 Fibrin adhesive may also be used for delivery of growth factors or other factors involved in wound repair. Incorporation of heparin and fibroblast growth factor in fibrin prior to implantation induced a significant increase in cell number in the matrix compared to fibrin alone. 64 Addition of platelet-derived growth factor to fibrin-filled chambers implanted subcutaneously into guinea pigs enhanced the intensity of the angiogenic response.62 Fibrin adhesive applied to PD catheter cuffs has been evaluated in a few small clinical studies. Kienzie et al65 applied fibrin to the area around the subcutaneous and deep cuffs and performed a histological evaluation of 14 fibrin-treated catheters that had been explanted from patients for various reasons. The fibrin-treated cuffs had less foreign body giant cell formation and better collagen build-up than control catheters. A study by Joffe66 evaluated fibrin treatment for reducing dialysate leakage. Fibrin was applied to the subcutaneous cuffs of single cuff catheters and was successful in reducing catheter leakage in 5 of 6 patients.

Fibronectin Fibronectin first appears in the initial plasma-derived provisional matrix, and then after clot lysis is subsequently deposited by fibroblasts that have migrated into the matrix (now referred to as granulation tissue). The major functions of fibronectin in mediating cellular adhesion are promoting cell migration and monocyte chemotaxis and helping to regulate cell growth and gene expression.57 Fibronectin functions via a series of functional domains and cell binding sites that permit it to interact with a wide range of cell types, ECM molecules, and cytokines. Fibronectin contains six or more peptide sites capable of mediating cell adhesion which are located in three general regions: a cell binding, an alternatively spliced IIICS, and a heparin binding domain.57 Most cells can adhere to fibronectin at least in part by the cell binding domain, which contains the crucial arginine-glycine-aspartic acid (RGD) cell binding sequence.67 In in vitro cell culture studies, immobilization of the RGD peptide sequence onto a substrate can promote focal contact formation during early fibroblast attachment and spreading68 and has also been found to enhance cell growth more strongly than a fibronectin immobilized surface.69

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Pierschbacher et al70 has designed synthetic RGD-containing peptides that will spontaneously deposit on any material surface in such a way that the RGD sequence is available to interact with the cell surface integrins.71 When materials coated with this peptide are implanted into animals, a rapid integration into the tissue and a significant reduction in encapsulation has been observed. Tweden et al72 applied the RGD peptide coating to polyester and poly(tetrafluoroethylene) (PTFE) textiles and investigated its effectiveness in enhancing wound healing in a vascular model. The RGD-containing peptides promoted the formation of an endothelial-like cell layer on both polyester and PTFE vascular patches in a dog model. Also, the number of foreign body giant cells associated with the RGD peptide-coated Dacron™ fibers was reduced compared to those associated with uncoated patches. Kishida et al73 investigated whether a RGD-albumin conjugate can promote soft tissue ingrowth into a porous matrix in vivo. Polyurethane sponges with a pore size of 150-500 µm were adsorbed with RGD-albumin, fibronectin, or albumin and were implanted in the subcutaneous tissue of rats. Both the fibronectin and RGD-albumin adsorbed sponges exhibited a similar amount of tissue ingrowth, and this tissue ingrowth was greater than that observed in control sponges. However, by day 14, fibronectin-adsorbed sponges had a higher density of fibroblasts and less dense collagenous tissue than RGD-albumin sponges. The RGD peptide has also been immobilized onto biodegRADable matrices of hyaluronate74 or poly(lactic acid-co-lysine) (PLAL).75 These biodegRADable, cell adhesion matrices could be used as a cuff for PD catheters. Preliminary cell culture studies show that the RGD-derivitized hyaluronic acid matrix supported RGD-mediated cell attachment and proliferation, and the RGD-containing PLAL matrix supported cell attachment and spreading.

Hyaluronan and Proteoglycans Hyaluronan is a linear glycosaminoglycan composed of repeating N-acetyl-glucosamine and glucuronic acid disaccharides. It is one of the major components of early granulation tissue and has several possible functions in wound repair. During regeneration and morphogenesis, hyaluronan appears at times of cell movement and mitosis and disappears at the onset of differentiation. 76 There are several possible roles of hyaluronan in cell motility.11 Hyaluronan becomes extremely hydrated and the expanded interstitial space at sites of deposition may allow more cell recruitment and proliferation in these areas. Cell movement into hyaluronan rich areas may also be mediated in part by specific cell surface receptors for hyaluronan. DegRADation products of hyaluronan have been found to have potent angiogenic activities,77-79 while high molecular weight forms inhibit the formation of new blood vessels.80 In addition, hyaluronan is believed to contribute to the rapid and scarless healing of fetal wounds, since hyaluronan is maintained for a prolonged period in the fetal wound, while hyaluronan levels decrease dramatically in adult wounds.81 As the granulation tissue matures, hyaluronan is degRADed and replaced by proteoglycans. While proteoglycans provide the tissue with more resilience than hyaluronan, they do not allow the same extent of cell movement and proliferation. The function of proteoglycans in wounds is still not entirely clear. Several types of proteoglycans contribute substantially to tissue resilience and may also help to regulate collagen fibrillogenesis.11 In addition to their structural roles, proteoglycans may also influence cell function. Proteoglycans promote cell migration and also regulate cell proliferation.11 Both heparin and heparan sulfate have been shown to interact with the fibroblast growth factor family by stabilizing fibroblast growth factor (FGF) into a more active conformation and protecting these factors from proteolytic and heat degRADation.82,83 Edelman et al found that 99% of FGF’s mitogenic activity was lost when conventional matrix polymer-based release devices

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were fabricated.84 However, binding the FGF to heparin-Sepharose beads resulted in release of 87% of the FGF in a biologically active form. Immobilization of hyaluronan, heparin, or chondroitin sulfate in combination with other ECM components onto the PD catheter cuff may enhance cell migration and vascularization. Several in vivo studies examined the effects of addition of hyaluronan to collagen85,86 or fibrin87 based matrices and found greater cellular ingrowth and capillary formation in the hyaluronan-treated matrices than in controls. Similar results were found when heparin88 was added to collagen matrices.

Collagen Collagen is a major constituent of extracellular soft tissue matrices. There are 18 different types of collagen with four major classes.11 Type I and III fibrillar collagens are examined most often in wound healing studies because they are well characterized and their structures are well defined. Type III and type I collagens are deposited in mature granulation tissue, in that order, and provide the healing tissue with increased stiffness and tensile strength. In animal studies, a high rate of collagen I synthesis has been found to correlate with increased wound breaking strength.89 Collagen is not only a structural protein, but an adhesive protein that may alter the phenotype and function of many different cell types. With regard to fibroblasts, collagen matrices reduce cell proliferation and collagen synthesis, while inducing procollagenase and α2β1 integrin expression which may promote the ability of these cells to remodel the matrix.11 A collagen coating on the catheter cuff and tubing may be beneficial according to the studies by Okada et al90 Okada et al immobilized bovine type I collagen onto smooth silicone and porous polyethylene sponges with 50 µm diameter pores and then evaluated the percutaneous histological response and infection incidence in rabbits for up to 30 weeks. The collagen coated devices had no epidermal downgrowth after seven weeks, while the uncoated devices had epidermal downgrowth which reached the dermis in 3 weeks. A greater force was required to pull out the collagen coated device than the uncoated one. The collagen coated porous devices had 1 out of 6 infected at five weeks, and no other infections were present for up to 30 weeks. In contrast, the porous uncoated device had the first infection after two weeks and by the tenth week all of the remaining devices were infected. Kinoshita et al91 examined the tissue reaction to subcutaneous implants of either uncoated or collagen coated porous polyethylene. The presence of a collagen coating appeared to decrease the inflammatory response and enhance the quality (capillaries were present) and rate of tissue ingrowth. The tissue inside the pores of the collagen coated sponges was in close contact with the material wall, and the tissue and material appeared to bond together at later time points. A porous biodegRADable collagen cuff could also be formed by crosslinking collagen fibers. Ishizaki et al attached a collagen cuff to PD catheters directly below the skin surface and investigated the effects of the cuff in a small clinical study.92 Catheters with the collagen cuff had a smaller sinus tract length than the control catheters after 14 months. The exit site infection rates in the collagen-cuffed catheter group were also lower than in the control group.

Laminin-5 Epidermal basal cells are anchored to the basement membrane through hemidesmosomes, which are membrane attachment sites for intermediate filaments with α6β4 integrins as transmembrane molecules.93 Laminin-5 is one of the major adhesion ligands present in epidermal basement membranes and has been shown to play a crucial role in nucleation of hemidesmosome assemblies and maintenance of hemidesmosome

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Fig. 5.5. Human epidermal keratinocyte attachment to silicone rubber coated with various ECM proteins.

structural integrity. 94 In addition to mediating basal cell adhesion via α6β4 in hemidesmosomes, laminin-5 also mediates basal cell adhesion via α3β1 integrins in focal adhesions.95 Laminin-5 interaction with α3β1 and α6β4 induces two distinct adhesion processes.95,96 Interaction via α3β1 results in initial cell attachment, spreading, and migration, while interaction via α6β4 induces stable anchorage without cell spreading. While hemidesmosomes are numerous in keratinocytes in vivo, they are only rarely found in cultured cells. In addition, keratinocytes require several hours to attach and 24 hours or more to spread in tissue culture.97 Hormia et al97 investigated the effect of laminin-5 on keratinocyte attachment, spreading, and hemidesmosome formation in a cell culture study. The response of HaCaT (human keratinocyte cell line) cells to plates coated with various matrix proteins was evaluated in a 30 minute adhesion assay. Rapid HaCaT cell attachment and spreading was observed in plastic wells coated with laminin-5, but not on plates coated with fibronectin, vitronectin, or laminin-1. Researchers at Desmos, Inc. (San Diego, CA) (unpublished data) found similar results when culturing human epidermal keratinocytes on silicone rubber coated with various ECM proteins (Fig. 5.5). Keratinocytes cultured in the presence of laminin-5 also assembled a greater number of mature hemidesmosomes than controls.97 A laminin-5 coating has been evaluated for dental applications. Since the tooth is an excellent model for percutaneous implants, these results are relevant to PD catheters. Similarly to PD catheters, the long-term success of dental implants depends on a tight gingival attachment around the implant to form a biological seal. Tamura et al98 evaluated the ability of a laminin-5 coated titanium alloy to promote attachment of a gingival epithelial cell line. Attachment of epithelial cells and hemidesmosome formation were observed on the laminin-5 coated titanium alloy disks, but not on the uncoated disks. A rat model was developed to further examine the ability of laminin-5 to inhibit the apical extension of

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junctional epithelium after gingival flap surgery and scraping of the dentin.99 Gingival recession and apical extent of the junctional epithelium relative to the cementum-enamel junction were evaluated by histological analysis. In comparison to saline treatment, laminin-5 treatment significantly reduced gingival recession and the apical extension of junctional epithelium. Coating the transcutaneous exit site portion of the PD catheter tubing with basement membrane components may enhance epidermal attachment, creating a biological seal at the catheter exit site. However, several issues may be encountered with a laminin-5 coating for PD catheters, which might be resolved by using a laminin-5 peptide. Keratinocytes may attach to the laminin-5 coated catheter via α3β1 integrin in focal adhesions, rather than by α6β4 integrin in hemidesmosomes. Focal adhesions interact with actin-containing stress fibers and do not promote as strong of a bond between the tissue and substrate as hemidesmosomes. While use of a laminin-5 peptide to target attachment to the a6β4 integrin may eliminate this potential problem, identification of these critical peptide sequences is needed.

Growth Factors Role of Growth Factors in Wound Healing The wound repair process is mediated in large part by growth factors and cytokines. Application of recombinant forms of these factors to the wounded tissue may help to control or accelerate the normal repair process. Accelerated healing around the catheter exit site may reduce infection rates, since a study by Twardowski et al12 found that fast healing exits become infected less often than slow healing exits. Modulation of angiogenesis or collagen deposition through application of specific growth factors could also be of benefit during wound repair. An extensive overview of the role of growth factors in wound healing can be found in Molecular and Cellular Biology of Wound Repair.100 Platelet-Derived Growth Factor Platelet-derived growth factor (PDGF) mediates many processes required for tissue repair including chemotaxis, proliferation, and matrix production. PDGF-treated wounds are characterized by increased numbers of neutrophils, monocytes, and fibroblasts. Formation of granulation tissue rich in fibronectin and hyaluronan is also accelerated when PDGF is applied in implant models101 and excisional wound models.102-104 In addition, a significant increase in wound strength is observed when PDGF is applied to incisional wounds.105 Overall, PDGF appears to act by accelerating, rather than altering, the normal sequence of wound repair. Transforming Growth Factor β Transforming growth factor β (TGF-β) has a profound and unique effect on synthesis of collagen and other ECM. When applied to excisional wounds, TGF-β enhances synthesis and maturation of collagen by bypassing the inflammatory stage of wound repair.103 This increased collagen deposition contributes to the increased breaking strength observed when TGF-β is applied to incisional wounds.106 Although TGF-β appears to play an important role in tissue repair, many effects of TGF-β are time and concentration dependent. Excess amounts result in a fibrotic phenotype and may be responsible for the tissue damage caused by scarring.107 In order to benefit from application of TGF-β at a wound site, the levels must be optimally controlled.

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Fibroblast Growth Factor The primary benefit of application of fibroblast growth factor (FGF) to wounded tissue is stimulation of angiogenesis.103,108,109 In addition, FGF has been shown to stimulate formation of granulation tissue rich in fibronectin and glycosaminoglycans in an implant model 109 and an excisional wound model. 103 An undesirable response of FGF application observed in an excisional wound model103 was an increased collagenolytic activity that resulted in little collagen in the wound site and delayed wound maturation. Epidermal Growth Factor Application of epidermal growth factor (EGF) to the wound site has been shown to stimulate epidermal repair.110,111 The most dramatic stimulation of epithelialization was noted during the migratory phase of wound repair in porcine excisional wounds111 and partial thickness human donor site wounds.110 In both studies, epidermal effects of EGF diminished as wound healing progressed. EGF has also been found to affect other facets of wound healing besides epithelialization, such as increased granulation tissue formation111 and acceleration of tensile strength in incisional wounds.112,113

Technologies for Growth Factor Delivery While many growth factors have potential applications in wound healing and tissue regeneration, there are few clinical products considering the range of effects these factors produce in vitro and in dermal wound models in animals.114 The scarcity of clinical products may be due to disappointing results from many of the early clinical evaluations of these factors. Application of the growth factor to the wounded area may not be enough to control the healing process because the cell response to cytokine signaling depends on not only presence, but also magnitude and duration of the growth factor, which are difficult to control due to diffusive spread, cell uptake, degRADation and inactivation. Regulation of the concentration and duration of the growth factor using controlled delivery systems may prove more successful in the clinical setting. There are several different types of controlled delivery systems currently under development that could be used to deliver growth factors to the wound surrounding PD catheters. A coating that releases growth factors could be applied to the catheter cuff and/or tubing, or growth factors could be immobilized to the catheter surface. A biodegRADable matrix in the form of a cuff could also be used for growth factor release. A hydrogel coating would be advantageous when shorter release periods are desired. Hydrogels have been prepared from both synthetic and natural polymers such as polyhydroxyethylmethacrylate, polyvinylalcohol, collagen, and hyaluronic acid derivatives and are biocompatible and relatively inert.115,116 The initial release rates are diffusion–controlled, although erosion-controlled release may contribute once the polymer begins to degRADe. One disadvantage of hydrogels is their ability to rapidly swell with water, which can lead to very rapid release rates and polymer degRADation rates.115 Hydrogels containing growth factors are often topically applied to wounds in an attempt to accelerate healing.104,117 The controlled release implants believed to function best in terms of tissue biocompatibility and release kinetics are poly(ethylene-covinyl acetate) (EVAc) and poly(lactide-coglycolide) (PLGA).116,118 Non-degRADable EVAc matrix systems have been used to release growth factors,84,119 and these systems allow a high degree of control over protein release, versatility in permitting release of a wide range of proteins, and good retention of biological activity. Walsh et al120 have developed a coating of EVAc, PDGF, and bovine serum albumin (BSA) to deliver growth factors locally to a fracture site. This coating was applied to a stainless steel Kirschner wire used for fixation of bony fragments, and the

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release kinetics and bioactivity of the PDGF were evaluated in vitro. The results confirmed that PDGF is released in an active form. BiodegRADable polymers such as poly(lactidecoglycolide) (PLGA) have been used to release growth factors.121 These growth factors can either be physically adsorbed to the surface of the polymer or embedded in the material for subsequent release. The advantage of biodegRADable materials is that they disappear from the implant site after protein release. However, the release is difficult to control in these systems, especially if degRADation occurs in the interior of the matrix rather than from the outer surface. The breakdown products may also adversely affect the surrounding tissue.116 However, Mooney et al122 have developed a method to fabricate porous PLGA sponges (100 µm pore size) without the use of organic solvents. This makes PLGA a more viable material, as organic solvents could inactivate growth factors or harm surrounding tissue. Immobilization of the growth factor to an insoluble substrate offers an alternative to growth factor release systems. This method would retain the growth factor at the wound site for a longer period of time. By using a labile tethering link, the factor could also be released after an appropriate period. Kuhl and Griffith 123 linked EGF via star polyethyleneoxide (PEO) to a substrate and determined that the immobilized EGF retained its biological activity as assessed by mitogenic and morphological assays. EGF was also immobilized to a poly(methyl methacrylate) (PMMA) film. 124 While the onset of EGF-PMMA effects were delayed, they persisted much longer than those of soluble EGF. Also, the maximal mitogenic activity of EGF-PMMA was greater than that of free EGF and required less growth factor.

Conclusion The ideal PD catheter would promote normal tissue attachment to the material, enhance vascularization around the catheter, prevent chronic inflammatory response, inhibit the formation of a fibrous capsule, and permit epidermal adhesion to the device. Since PD catheters span several layers of tissue, each of which exhibits a different physiological reaction to the implant, the ideal catheter design would take these differences into account. Textured surfaces, ECM-based coatings, and growth factor incorporation all show promise as elements of such a design. Inclusion of any of these individual technologies would likely improve the tissue response to the implant. However, the greatest improvements might be achieved by combining individual technologies into the catheter, perhaps spacing them in such a way as to evoke a favorable response in each region of tissue the catheter passes through. More research and development is required before such multicomponent strategies could be implemented in a stable clinical product. Nevertheless, incremental improvements based on some of the emerging technologies described in this chapter could be the first successful application of tissue engineering principles to PD catheters.

Notation BSA ECM EGF EVAc FGF PD PDGF PLAL PLGA PMMA PTFE

bovine serum albumin extracellular matrix epidermal growth factor poly(ethylene-covinyl acetate) fibroblast growth factor peritoneal dialysis platelet-derived growth factor poly(lactic acid-co-lysine) poly(lactide-coglycolide) poly(methyl methacrylate) poly(tetrafluoroethylene)

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arginine-glycine-aspartic acid transforming growth factor β

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97. Hormia M, Falk-Marzillier J, Plopper G et al. Rapid spreading and mature hemidesmosome formation in HaCaT keratinocytes induced by incubation with soluble laminin-5r. J Invest Dermatol 1995; 105(4):557-61. 98. Tamura RN, Oda D, Quaranta V et al. Coating of titanium alloy with soluble laminin-5 promotes cell attachment and hemidesmosome assembly in gingival epithelial cells: Potential application to dental implants. J Periodont Res 1997; 32:287-94. 99. Tucker BJ, Ross T. Laminin-5 inhibits apical extension of junctional epithelium in the rat. J Dent Res 1997; 76:354. 100. Clark RAF, ed. The molecular and cellular biology of wound repair. New York: Plenum Press, 1996. 101. Sprugel KH, McPherson JM, Clowes AW et al. Effects of growth factors in vivo. I. Cell ingrowth into porous subcutaneous chambers. Am J Pathol 1987; 129(3):601-13. 102. Pierce GF, Vande Berg J, Rudolph R et al. Platelet-derived growth factor-BB and transforming growth factor beta 1 selectively modulate glycosaminoglycans, collagen, and myofibroblasts in excisional wounds. Am J Pathol 1991; 138(3):629-46. 103. Pierce GF, Tarpley JE, Yanagihara D et al. Platelet-derived growth factor (BB homodimer), transforming growth factor-beta 1, and basic fibroblast growth factor in dermal wound healing. Neovessel and matrix formation and cessation of repair. Am J Pathol 1992; 140(6):1375-88. 104. Mustoe TA, Pierce GF, Morishima C et al. Growth factor-induced acceleration of tissue repair through direct and inductive activities in a rabbit dermal ulcer model. J Clin Invest 1991; 87(2):694-703. 105. Pierce GF, Mustoe TA, Senior RM et al. In vivo incisional wound healing augmented by platelet-derived growth factor and recombinant c-sis gene homodimeric proteins. J Exp Med 1988;167(3):974-87. 106. Mustoe TA, Pierce GF, Thomason A et al. Accelerated healing of incisional wounds in rats induced by transforming growth factor-beta. Science 1987; 237:1333-6. 107. Border WA, Ruoslahti E. Transforming growth factor-beta in disease: The dark side of tissue repair. J Clin Invest 1992; 90(1):1-7. 108. Davidson JM, Klagsbrun M, Hill KE et al. Accelerated wound repair, cell proliferation, and collagen accumulation are produced by a cartilage-derived growth factor. J Cell Biol 1985; 100:1219-27. 109. Fiddes JC, Hebda PA, Hayward P et al. Preclinical wound-healing studies with recombinant human basic fibroblast growth factor. Ann NY Acad Sci 1991; 638:316-28. 110. Brown GL, Nanney LB, Griffen J et al. Enhancement of wound healing by topical treatment with epidermal growth factor. N Engl J Med 1989; 321:76-9. 111. Nanney LB. Epidermal and dermal effects of epidermal growth factor during wound repair. J Invest Dermatol 1990; 94:624-9. 112. Brown GL, Curtsinger L, White M et al. Acceleration of tensile strength of incisions treated with EGF and TGF-β. Ann Surg 1988; 208:788-94. 113. Perry LC, Connors AW, Matrisian LM et al. Role of TGFβ1 and EGF in the wound healing process: An in vivo biochemical evaluation. J Wound Repair Regen 1993; 1:41-6. 114. Pierce GF, Mustoe TA. Pharmacologic enhancement of wound healing. Annu Rev Med 1995; 46:467-81. 115. Gombotz WR, Pettit DK. BiodegRADable polymers for protein and peptide drug delivery. Bioconjugate Chem 1995; 6(4):332-51. 116. Langer R, Moses M. Biocompatible controlled release polymers for delivery of polypeptides and growth factors. J Cell Biochem 1991; 45(4):340-5. 117. Ko CY, Dixit V, Shaw WW et al. Extensive in vivo angiogenesis from the controlled release of endothelial cell growth factor—implications for cell transplantation and wound healing. J Control Release 1997; 44(2-3):209-14. 118. Saltzman WM. Growth-factor delivery in tissue engineering. MRS Bulletin 1996; Nov:62-5. 119. Krewson CE, Saltzman WM. Transport and elimination of recombinant human NGF during long-term delivery to the brain. Brain Research 1996; 727(1-2):169-81.

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120. Walsh WR, Kim HD, Jong YS et al. Controlled release of platelet-derived growth factor using ethylene vinyl acetate copolymer (EVAc) coated on stainless-steel wires. Biomaterials 1995; 16(17):1319-25. 121. Camarata PJ, Suryanarayanan R, Turner DA et al. Sustained release of nerve growth factor from biodegRADable polymer microspheres. Neurosurgery 1992; 30(3):313-9. 122. Mooney DJ, Baldwin DF, Suh NP et al. Novel approach to fabricate porous sponges of poly(D,L-lactic-co-glycolic acid) without the use of organic solvents. Biomaterials 1996; 17(14):1417-22. 123. Kuhl PR, Griffith-Cima LG. Tethered epidermal growth factor as a paRADigm for growth factor-induced stimulation from the solid phase. Nature Med 1996; 2(9):1022-7. 124. Ito Y, Li JS, Takahashi T et al. Enhancement of the mitogenic effect by artificial juxtacrine stimulation using immobilized EGF. J Biochem 1997; 121(3):514-20

CHAPTER 6

Tissue Engineering in the Peritoneal Cavity: Genetic Modification of the Peritoneal Membrane Catherine M. Hoff and Ty R. Shockley

P

eritoneal dialysis (PD) is a simple, cost effective form of renal replacement therapy for the management of patients with end stage renal disease (ESRD).1 In PD, hypertonic dialysis solution is instilled into and drained from the peritoneal cavity via a permanent indwelling percutaneous catheter. Typically, dialysate “dwells” in the peritoneal cavity for 2-12 hr depending on the regimen used. During such dwells, waste solutes and excess water move from the circulation and surrounding tissues and accumulate in the instilled dialysate by traversing the peritoneal membrane. With increasing time on peritoneal dialysis, there is evidence of both structural and functional changes in the peritoneal membrane. These changes may compromise dialysis efficiency and eventually lead to membrane failure, precluding continuation of this mode of therapy. The efficacy of peritoneal dialysis and its success as a long term treatment for ESRD depend, therefore, on preserving membrane structure and function integral to its performance as a dialyzing membrane. New and innovative strategies to prevent peritoneal membrane injury during peritoneal dialysis and increase the longevity of the therapy must be investigated. One potential strategy is to use tissue engineering methodologies. The goal of tissue engineering in the peritoneal cavity is to improve peritoneal dialysis as it is currently practiced by changing the characteristics of the peritoneal membrane to enhance its function as well as extend its longevity as a dialyzing membrane. In this chapter we will introduce and describe a molecular genetic approach to achieve this goal: i.e., to genetically modify or engineer the peritoneal membrane to make it a stronger, more viable dialyzing membrane, and to improve the environment of the peritoneal cavity during peritoneal dialysis. This strategy, based on the use of genetically altered tissue to treat a disease or pathophysiological condition, is called gene therapy. We will begin with an overview of gene therapy. This will be followed by a brief discussion of peritoneal membrane biology including a review of peritoneal membrane anatomy and physiology, as well as a summary of the changes to the peritoneal membrane as a consequence of peritoneal dialysis. We will then discuss the strategy of molecular genetic modification of the peritoneal membrane, specifically using the mesothelial cell as the target cell, and conclude by presenting our thoughts on the potential for improving peritoneal dialysis via such a strategy.

The Artificial Kidney: Physiological Modeling and Tissue Engineering, edited by John K. Leypoldt. ©1999 R.G. Landes Company.

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Gene Therapy Definition Gene therapy can be defined as the correction of a disease or chronic condition through the introduction of genetic elements into cells or tissue of the affected organism to produce a desired therapeutic effect. Gene therapy is a relatively new approach, initially proposed as a treatment for inherited diseases caused by a single gene defect. It was thought that replacement of the defective gene with a “normal,” or functioning copy would correct the disease state. It is now understood that changes in normal patterns of gene expression, as well as genetic mutations, are the underlying causes of many other diseases or conditions, and the scope of the pathologies addressed by gene therapy has broadened to include various cancers and malignancies, infectious diseases, and autoimmune disorders. Applications of gene therapy now include genetic modification to prevent the synthesis of an undesirable protein or one that is produced in excess, or to modulate the expression of a factor that is beneficial and required in the body, but needed at different levels to treat a disease state or chronic condition. The tremendous strides made over the past two decades in the manipulation of genetic material have provided the tools to change the genetic makeup of a cell or organism, providing the molecular basis for this therapeutic approach.

Gene Therapy Clinical Trials By mid-1998 there had been more than 200 human gene therapy clinical protocols submitted for approval to NIH-RAC, involving well over 1,000 patients.2 Many of the early protocols were designed for the treatment of diseases caused by single gene defects (e.g., cystic fibrosis, adenosine deaminase deficiency), cancer or diseases for which there is no cure and current treatments are less than satisfactory. There is, however, an increasing number of protocols for the treatment of chronic, less life threatening conditions. The rapid advances and instant cures that were idealistically, and probably unrealistically, expected at the onset of the clinical trials have not been forthcoming. However, safety has been widely demonstrated, the number of side effects are minimal, and in fact there are recent indications of success at the preclinical and clinical levels.3,4 Importantly, gene therapy is a strategy that has captured the interest and support of almost every medical discipline, with significant research currently underway in research centers in academia and industry worldwide. Theodore Friedmann described human gene therapy as “an immature genie, but certainly out of the bottle”5—together with many others, we believe it is an approach not yet come of age, but whose time will certainly come.

Two Approaches to Gene Therapy There are two main strategies for practicing gene therapy, designated ex vivo and in vivo gene therapy. In ex vivo gene therapy, designated cells are removed from the patient and genetically modified in the laboratory to express the gene(s) of interest. These cells are then delivered back to the patient where it is anticipated that they will restablish themselves and continue to express the protein of interest or other specific factor at therapeutic levels. In vivo gene therapy differs in that the patient’s cells are genetically modified in situ through the direct delivery of the genetic material (Fig. 6.1).

Gene Delivery Systems In practice, gene therapy involves the transfer of genetic material into target cells with subsequent expression of the therapeutic molecule. This process requires two main components: the genetic material (e.g., DNA sequences for the gene(s) of interest), and an

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Fig. 6.1. Schematic diagram of ex vivo and in vivo gene therapy.

appropriate delivery system or means by which the genetic information is delivered to the cell. Genetic Material The material to be used for genetic modification usually consists of cDNA sequences for the gene of interest (i.e., the coding region), and various regulatory sequences which control the level of transcription.6 Many of these constructs, or “expression cassettes,” have been designed using viral promoters and enhancers which provide strong constitutive expression of the gene of interest, allowing investigators to evaluate the therapeutic potential and function of the particular protein. It is expected that in the future, constructs will be more sophisticated, with gene expression driven by a cellular or tissue-specific promoter, or one that can be regulated by a specific physiological or pharmacological stimulus. Endogenous gene expression in the target cell can be downregulated through the use of two other classes of genetic elements: antisense oligonucleotides and ribozymes. Antisense nucleotides are short (18-20 nucleotide) DNA segments designed to bind to their complementary sequences in RNA transcripts, disrupt RNA processing, and effectively block expression of a specific protein.7 Unlike the delivered expression cassettes, these elements are not transcribed after cell uptake. Their effect is immediate and relatively transient, and can be used for short term inhibition of cellular gene expression. Ribozymes are catalytic RNA molecules designed to cleave targeted mRNA molecules, and in this fashion prevent expression of a particular protein.8,9 While this approach has yielded promising results in cell culture studies, its effectiveness in vivo must still be explored. Virus-Mediated Delivery A significant amount of research has gone into the development of gene transfer or delivery systems, of which there are two main categories: those involving the use of modified recombinant viruses, and those employing nonviral reagents. Viruses were selected as gene delivery agents because they are extremely efficient at infecting cells and transferring their

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genetic information into cells for expression—essentially the principle of gene transfer. A number of virus-based gene delivery systems have been described to date based on the use of adenoviruses, herpes viruses, lentiviruses, retroviruses, and adeno-associated viruses.10-14 The viruses used as gene delivery agents are modified by deleting viral sequences, including those required for replication, and replacing them with the gene of interest or the “expression cassette”. Vector systems based on the adenovirus, retrovirus, and adeno-associated virus vectors are the most extensively used today.11,15 Nonviral Systems Nonviral gene delivery systems encompass a wide range of techniques for delivering foreign DNA into a cell. These nonviral systems include cationic liposomes, polycationic amino polymers, dendrimers, electroporation, naked DNA, and salt coprecipitation and have been reviewed in detail.16-18 Of the nonviral delivery systems, cationic liposomes have been the most widely used, especially for in vivo delivery.19 The characteristics of the commonly used viral and nonviral systems, as well as the comparative advantages and disadvantages of their use in gene therapy regimens, are summarized in Table 6.1.

Cell Types Used in Gene Therapy Successful gene transfer has been demonstrated in a number of different cell types and tissues. Fibroblasts, myoblasts, hepatocytes, synovial cells, mesangial cells, blood stem cells, and macrophages are among the many cell types that have been transfected and used in ex vivo gene transfer.20-25 In vivo gene transfer to a variety of tissues and organ systems (e.g., cardiovascular tissues, lung, kidney, liver, muscle, joints and various tumors) has been demonstrated in both preclinical animal studies and in human trials.6,26-28 We will focus here on the peritoneal mesothelial cell and describe its role in a gene transfer strategy for the modification of the peritoneal membrane in peritoneal dialysis.

Peritoneal Membrane In order to appreciate the potential of genetically modifying the peritoneal membrane, one must have an understanding of the structure and function of this tissue. For comprehensive reviews on peritoneal membrane structure and physiology, see refs. 29-31.

Structure of the Peritoneal Membrane The parietal and visceral surfaces of the peritoneal cavity are lined by a simple squamous epithelium, the mesothelium, consisting of a single cell type, the mesothelial cell. The mesothelium in an adult covers a surface area of approximately 1-2 m2 32 and contains more than 109 mesothelial cells. This monolayer is separated from the underlying connective tissue or interstitium by the basal lamina consisting of type IV collagen, proteoglycans, and glycoproteins.33 The underlying interstitium contains extracellular matrix (ECM) components such as laminin, collagens type I, III, and IV, and fibronectin. Interspersed throughout the interstitium are fibroblasts, tissue macrophages, and mast cells, and cells of the peritoneal microvasculature. The mesothelium, together with its underlying connective tissue and microvasculature, comprises the peritoneal membrane, a structure of critical importance in peritoneal dialysis. The thickness of the membrane, from peritoneal space to the capillaries, ranges from <30 µm to 100 µm,34,35 and it is across this space that water and solute exchange occurs during peritoneal dialysis.

yes

yes/ diminishing

complement inactivation

yes

INTEGRATION

SUSTAINED EXPRESSION

HOST RESPONSE

REPEAT ADMINISTRATION

envelope modification

replicating cells

HOST CELLS

TARGETED DELIVERY

7.5 kb

ADENONONVIRAL RETROVIRUS

INSERT CAPACITY (kb)

VIRAL VECTOR

Table 6.1. Properties of selected gene delivery systems

fiber modification

with immunosuppression

potential immune response

sustained/ transient

no

replicating/ non-replicating

up to 30 kb

ADENOVIRUS

yes

yes

no

yes

yes/no

replicating/ non-replicating

4.6 kb

ASSOCIATED VIRUS

through surface ligands

yes

no

transient

no

replicating/ non-replicating

unlimited

LIPOSOMES

Tissue Engineering in the Peritoneal Cavity 129

promoter shut down

insertional mutagenesis? host immune response

high titer stocks

best for ex vivo gene transfer

DISADVANTAGES

efficient transfer

possible long-term expression

ADVANTAGES

ADENOVIRUS

RETROVIRUS

VECTOR

VIRAL

Table 6.1. Properties of selected gene delivery systems (con't)

low DNA packaging capacity

repeat administration possible

non-immunogenic

ADENOASSOCIATED VIRUS

low efficiency of plasmid transfer

repeat administration possible

non-toxic

LIPOSOMES

NONVIRAL

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Function of Peritoneal Mesothelial Cells The mesothelial cells play a major role in maintaining physiological homeostasis of the peritoneal cavity. They provide a slippery and nonadhesive surface allowing the free movement of tissues and organs, through the production and secretion of glycosaminoglycans such as hyaluronan36,37 and the small dermatan proteoglycans decorin and biglycan,37 as well as lamellar bodies containing phosphatidylcholine.38,39 The normal mesothelium possesses remarkable fibrinolytic activity. Studies of human mesothelial cells in culture and peritoneal biopsies have demonstrated the expression of both fibrinolytic and procoagulant factors. The balance between these factors is such that the normal membrane is profibrinolytic, with the ongoing deposition and degRADation of intraperitoneal fibrin. Mesothelial cells in culture constitutively express tissue plasminogen activator (tPA), an enzyme that activates the plasmin-mediated fibrinolytic cascade;40 peritoneal biopsies confirm the presence of a second fibrinolytic activator, urokinase-type plasminogen activator (uPA).41 Mesothelial cells also constitutively express two inhibitors of fibrinolytic activity: plasminogen activator inhibitor type 1 (PAI-1) which is secreted from the cell, and type 2 (PAI-2) which remains intracellular.40 The procoagulant protein tissue factor (TF), a membrane-associated glycoprotein involved in the initiation of the coagulation cascade, is expressed at a very low level in the normal peritoneum, but appears to be readily upregulated by the proinflammatory cytokine TNFα.42,43 A change in the environment of the peritoneal cavity, in the form of inflammation, injury, or bacterial contamination, may alter the balance between pro- and antifibrinolytic activities,41,44-46 resulting in increased fibrinogenic activity which supports normal tissue repair. However, if this increase remains unchecked or unresolved, it can lead to the persistence of a fibrin gel matrix, the development of peritoneal adhesions and fibrous connective tissue and, ultimately, peritoneal sclerosis. Mesothelial cells contribute to and modulate the balance of ECM formation and degRADation as part of normal membrane repair.47 While mesothelial cells are involved in ECM formation through the synthesis of type I, III, and IV collagens, they contribute to membrane remodeling through the production of interstitial collagenases, 72 and 92 kDa gelatinases, and tissue inhibitor of metalloproteinases (TIMP). It has been suggested that through the synthesis of these enzymes, mesothelial cells manifest a matrix-degRADative phenotype favoring normal repair, rather than fibrosis, following serosal injury.48 Mesothelial cells also synthesize the TGF-β isoforms 1 and 2.49 TGF-β has profound effects on the synthesis and degRADation of the ECM49 and in mesothelial cells may contribute to membrane repair and regeneration through the upregulation of MMP-9, a type IV collagenase.50 While expression of TGF-β in this manner may favor degRADation of type IV collagen in regenerating mesothelium, excessive TGF-β activity may nonetheless be fibrogenic if the degRADed type IV collagen is replaced by interstitial collagens,50 implicating it in the development of fibroproliferative adhesions and peritoneal fibrosis.49,51 The mesothelium also plays a critical role in host defense in the peritoneal cavity. This subject has been reviewed extensively52-54 and will be summarized here. Numerous studies have documented that mesothelial cells respond to bacterial infection or the presence of foreign material in the peritoneal cavity by responding to and amplifying proinflammatory cytokines released by activated macrophages. Mesothelial cells contribute to the inflammatory response through the synthesis of vasodilatory prostaglandins PGE2 and PGI153,55 and chemotactic and proinflammatory cytokines such as IL-8, MCP-1, RANTES, IL-1α and IL-1β.56-58 While mesothelial cells also constitutively express the adhesion molecules ICAM-1 and VCAM-1,59 the surface is normally nonadherent for leukocytes. However, activation of mesothelial cells by exposure to proinflammatory cytokines TNFα and IL-1β promotes the upregulation of these adhesion molecules and the transition of the mesothelium to an

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adhesive surface. Mesothelial cells are also involved in the resolution of inflammation through the production of IL-6.60 IL-6 may downregulate macrophage expression of IL-1 and TNFα, and induce the synthesis of cytokine antagonists IL-1ra and sTNFRp55,61 thereby contributing to the control of inflammation within the peritoneal cavity.62 The mesothelial cells may also participate in host defense in their role as antigenpresenting cells.63 In vitro studies indicate that they may participate, along with peritoneal macrophages, in activation of specific T cells and in the generation of local cell-mediated immunity to various pathogens.63

Changes to the Membrane in Peritoneal Dialysis The efficacy and success of peritoneal dialysis as a therapy depends on the preservation of the dialyzing capacity of the peritoneal membrane. In the context of developing a therapy to improve PD, we will focus here primarily on the effect of long-term, continuous exposure to dialysis solutions, and on peritonitis, which can act synergistically to produce membrane injury and reduce performance. These effects have been documented through analysis of spent dialysate and peritoneal biopsies from CAPD patients and preclinical animal studies, and a large body of evidence from studies on cultured mesothelial cells. Structural and functional changes in membrane characteristics are summarized in Table 6.2 and will be discussed here. The balance between fibrinolytic and coagulant activities that existed in the normal peritoneum is altered in peritoneal dialysis. Gries called peritoneal dialysis a permanent state of hypercoagulation and hyperfibrinolysis,64 with intraperitoneal coagulation predominating over fibrinolysis. An increased PAI-1:tPA ratio in spent dialysate from peritoneal dialysis patients, as well as increased amounts of fibrin and fibrin degRADation products, confirms the altered balance.42,44 TGF-β, TNF-α, and IL-1 have all been shown to enhance PAI-1 expression in cultured human mesothelial cells, suggesting that they may mediate upregulation of PAI-1 in vivo and lead to the persistence of fibrin depositions in the peritoneal cavity.40,42,45,46,65-67 The synthesis of PAI-1 and TGF-β by tissue macrophages and cells in the submesothelial tissue during inflammation may also contribute to the presence of a more fibrinogenic state.41 Peritoneal dialysis has also been described as a state of chronic inflammation,68 which worsens considerably during episodes of peritonitis. During peritonitis, mesothelial cells activated by exposure to LPS, TNFα, and IL-1β contribute to this state through the synthesis of proinflammatory and chemotactic cytokines such as IL-1α, IL-1β, MCP-1, RANTES, and IL-8. 62 Chemotactic cytokines IL-8, RANTES and MCP-1 facilitate the infiltration of leukocytes, particularly neutrophils, into the peritoneal cavity during infection or inflammation.56,69 The polar secretion of IL-8 may establish a chemotactic gRADient across the mesothelial cell monolayer, and thus facilitate leukocyte recruitment. 70 The directed secretion of IL-8, MCP-1 and RANTES, together with the increased cell surface expression of ICAM-1 and VCAM-1, results in increased numbers of infiltrating leukocytes adhering to the mesothelial cell surface. Reactive oxygen RADicals released from adherent PMNs intended to kill invading bacteria also damage the mesothelial cells, as evidenced by ATP depletion, cell detachment and lysis, and cellular morphologic alterations.71-73 It has been reported that intracellular hydrogen peroxide increases in cultured mesothelial cells upon exposure to glucose-based dialysis solutions.74 This may contribute to oxidative injury, as the ability of the mesothelial cell to metabolize oxygen free RADicals and H2O2 also becomes impaired upon exposure to lactate-based, low pH dialysis solutions.75 IL-1 treatment of cultured mesothelial cells resulted in an inhibition of catalase activity;76 this decreased ability of the mesothelial cell to process oxygen free RADicals, combined with increased free RADical production by me-

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Table 6.2. Peritoneal dialysis-associated changes in the peritoneal membrane • • • • • • •

Altered mesothelial cell phenotype Structural alterations in the extracellular matrix Formation of AGEs; glycation of membrane components Altered fibrinolytic/fibrinogenic balance Increased oxygen free RADical-mediated injury Altered host defense mechanisms Chronic peritoneal inflammation

sothelial cells and activated leukocytes, may partially explain the oxidative damage that occurs during inflammation and is exacerbated in peritonitis. The continuous exposure of the membrane to high levels of glucose in dialysate may predispose it to eventual failure. The effects of glucose on cultured peritoneal mesothelial cells include suppressed mesothelial cell growth and regeneration, and increased TGF-β mRNA expression. This may in turn enhance MCP-1 and fibronectin expression, leading to increased ECM deposition and basement membrane thickening,51,77 which may ultimately affect membrane repair. Long-term exposure to glucose in a mouse model of peritoneal dialysis is reportedly associated with reduced population density, decreased mesothelial cell viability, increased peritoneal membrane permeability and loss of ultrafiltration.78,79 In addition, glucose-mediated changes can be detected in the peritoneal mesothelium and underlying tissue as early as 3 months after initiation of peritoneal dialysis80 and include glycation of components in the interstitium, on submesothelial tissue, and on the endothelial walls, possibly leading to changes in permeability and transport.81,82 Host defense mechanisms also appear to become compromised during peritoneal dialysis. The effects of dialysis solutions and continuous exposure to glucose have been shown in in vitro models to significantly alter responses of peritoneal macrophages, PMNs, T lymphocytes, and mesothelial cells to inflammation and bacterial infection.52,68,83-87 In addition, glucose may impair antibacterial defense mechanisms as evidenced by decreased bacterial cell killing in an animal model of peritoneal dialysis,86 and predispose the peritoneal dialysis patient to bacterial infection and peritonitis. Another consequence of peritoneal dialysis and peritoneal inflammation is the increased synthesis of hyaluronan.36,88 Cell culture studies show that IL-1 and effluent dialysate synergistically enhance the synthesis of HA in mesothelial cells. This increased production may be a response to protect the mesothelium from tissue damage due to chronic inflammation and peritonitis.89 Hyaluronan’s action as a free RADical scavenger,90 and its ability to prevent the release of reactive oxygen from peritoneal macrophages91 and the release of elastase from peritoneal leukocytes may explain its protective effect against dialysateinduced damage of the peritoneum.92 The increased synthesis rate may also be driven by its continuous removal from the peritoneal cavity with spent dialysate, and be indicative of continuous injury and repair during dialysis. It is apparent that the peritoneal membrane can be adversely affected over the course of time by continuous exposure to nonphysiological dialysis solutions and episodes of peritonitis. Changes in mesothelium include loss of mesothelial cell microvilli, increased cell turnover, decreased cell density, denudation of the membrane, membrane thickening, and submesothelial fibrosis corresponding to the increased deposition of ECM components.29,78,93 These alterations can affect transport parameters and membrane permeability, contributing to reduced ultrafiltration with increased time on dialysis.94 Normally, the mesothelium recovers from injury with the regeneration of the mesothelial cell

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Fig. 6.2. Adenovirus-mediated in vivo gene transfer to the parietal and visceral peritoneal mesothelium. β-gal transgene expression (blue staining) was evaluated 24 hours after intraperitoneal delivery of an adenovirus-lacZ vector in a rat model. Extensive staining was observed on the parietal peritoneal wall (A) and small intestine and mesentery (C) of animals receiving the adenovirus vector, while tissues from the parietal peritoneal wall (B) and small intestine and mesentery (D) of control animals were negative.

population. However, with continued exposure to dialysis solutions, and more frequent episodes of peritonitis, the likelihood that the membrane will not heal is increased. During acute peritonitis, the membrane may become completely denuded of mesothelial cells.59,95 If reestablishment of the mesothelial cell monolayer fails to occur, the peritoneum can become lined by a thickened layer devoid of mesothelial cells—the “tanned peritoneum” seen occasionally in biopsies of long term peritoneal dialysis patients.96 It is apparent that continuous exposure to nonphysiological dialysis solutions, together with episodes of peritonitis, acts synergistically to alter mesothelial cell physiology, produce permanent membrane injury and decrease dialyzing capacity.

Genetic Modification of the Peritoneal Membrane Through understanding the structure and function of the normal mesothelium, and identifying the mechanisms by which it becomes compromised during peritoneal dialysis, we can begin to develop a strategy for rendering the membrane more resistant to the effects of peritoneal dialysis and better able to recover from peritonitis. The first step in developing a genetic engineering scheme for modification of the membrane is to select a potential target cell for gene transfer.

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Mesothelial Cell as the Target for Genetic Modification The peritoneal mesothelial cell is an attractive target for genetic modification for a number of reasons. First, the mesothelium in an adult contains over 109 cells, providing a large target cell population. Second, mesothelial cells normally have a relatively slow turnover rate, estimated at 0.1-3% daily, 97 advantageous in that cells which may be transiently modified will not be rapidly lost from the membrane, allowing for prolonged transgene expression. Third, the proven secretory capacity of the mesothelial cells is important if the scheme involves production and secretion of a therapeutic molecule. The peritoneal cavity, by virtue of the diaphragmatic lymphatics, is in direct communication with the systemic circulation, offering the possibility of systemic delivery of a therapeutic protein from a genetically modified peritoneal mesothelium. Fourth, mesothelial cells can be obtained during catheter implantation or laparoscopy, grown in culture and genetically modified. Finally, successful reimplantation of mesothelial cells has been accomplished in rat98,99 and rabbit100 models, and in CAPD patients,100 essential if an ex vivo strategy is desired.

A Model System for Ex Vivo Mesothelial Cell-Mediated Gene Therapy The feasibility and potential of genetic modification of the peritoneal membrane has been demonstrated in an animal (rat) model of ex vivo gene transfer.98 In this study, mesothelial cells were isolated, genetically modified in vitro and transferred back into the animal with continued expression of the gene of interest. Mesothelial cells were transduced with a retroviral vector containing the E. coli lacZ gene coding for the bacterial enzyme β-galactosidase (β-gal), commonly used as a marker protein as it remains intracellular and is readily detectable by a simple histochemical staining procedure. These cells could be successfully reimplanted on the denuded parietal peritoneal surface of syngeneic recipients, as demonstrated by X-gal staining of the peritoneal mesothelium after delivery of the modified cells. This study confirmed previous reports of mesothelial cell reimplantation100 and emphasized the feasibility of mesothelial cell-mediated gene transfer. Using the same ex vivo gene transfer strategy, the ability of a genetically modified mesothelium to secrete a recombinant protein to the systemic circulation was shown.99 Implanted mesothelial cells expressing human growth hormone (hGH) continued to deliver recombinant hGH to the systemic circulation of animals for at least two months. In addition, hGH appeared to be biologically functional as evidenced by a concurrent increase in the serum levels of IGF-1 (Shockley TR, unpublished observations). Similar results were seen when human peritoneal mesothelial cells expressing recombinant hGH were injected into the peritoneal cavity of nude mice. Clusters of these cells implanted on visceral peritoneal surfaces continued to secrete hGH to the systemic circulation for a period of three weeks.101 The potential to modify the cell surface characteristics of the mesothelium has also been shown. Mesothelial cells were genetically modified to produce a biologically relevant protein, the anticoagulant/antithrombogenic cofactor thrombomodulin (TM).102 TM is a transmembrane glycoprotein that functions as an essential cofactor in the coagulation cascade, neutralizing thrombin clotting activity by binding thrombin, and producing activated protein C, ultimately preventing the conversion of fibrinogen to fibrin. The expression of TM on the mesothelial cell surface following ex vivo gene transfer was confirmed by immunohistochemistry. Membrane bound TM retained its ability to activate protein C in a thrombin dependent manner, conferring on the membrane a potent anticoagulant activity. There are a number of advantages to an ex vivo approach to the genetic modification of the peritoneal mesothelium. The target mesothelial cells can be isolated and modified in

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culture, ensuring that a well characterized population of cells is available. Implantation of cells can be well controlled, with delivery of a defined number of cells back to the patient. In our animal model, denudation of the membrane by gelfilm wounding was necessary for reimplantation, as cells did not establish themselves on a healthy, intact peritoneum. However, it appears that cells will seed themselves on an injured peritoneum as noted in reimplantation of mesothelial cells in CAPD patients post-peritonitis.100 Here, the area available for attachment of modified cells would be equal to the area of injured or lost cells, not necessarily restricted to an area physically denuded. There are disadvantages, however, to ex vivo gene transfer, especially with regard to ease of delivery to a patient population. The isolation and genetic modification of cells from a patient may be too time consuming and labor intensive, or simply not feasible when proposing a therapy for a large patient population or for an acute condition, e.g., peritonitis, which requires immediate treatment. In addition, ex vivo delivery may not provide enough cells for therapeutic levels of expression. For these reasons, many of the current gene therapy strategies involve in vivo gene transfer.

In Vivo Gene Transfer to the Peritoneal Cavity Genetic modification of the peritoneal mesothelium by an in vivo approach may be preferable over an ex vivo strategy because of the potential for targeting all of the mesothelial cells in the peritoneal cavity—a much larger area and a greater cell number than would be feasible by an ex vivo approach. In addition, one would not be constrained by having to denude healthy mesothelium or limited to working with a wounded or injured tissue. It is also potentially much simpler, involving direct delivery of the genetic information to the peritoneal cavity for subsequent uptake and genetic modification. Mesothelial cellmediated genetic modification in the peritoneal dialysis population may be well suited for an in vivo approach, as delivery of gene transfer reagents into the peritoneal cavity and immediate access to the target cell population is easily attained through the use of the peritoneal dialysis patient’s indwelling catheter. Viral-Mediated Gene Transfer to the Peritoneal Mesothelium Much of the research focusing on in vivo gene transfer to the peritoneal cavity has been accomplished with the use of recombinant adenovirus vectors. These studies have included marker studies to evaluate mesothelial cell transduction, the use of the peritoneal mesothelium to deliver proteins to the systemic circulation, and preclinical and clinical studies for the treatment of malignancies of the abdominal space. A study carried out in our laboratory to evaluate and define conditions for adenovirusmediated intraperitoneal gene transfer in a rat model produced striking results. A majority of the mesothelial cells could be transduced after a single intraperitoneal injection of a replication deficient, recombinant adenoviral vector containing the gene for β-gal. Transgene expression was noted throughout the peritoneal cavity, as defined by X-gal staining of the parietal peritoneal wall and the visceral mesothelium covering the small intestine and mesentery (Fig. 6.2), as well as the mesothelial cell lining of the diaphragm, liver, and spleen. Administration of the virus in a small volume (i.e., 12.5-50 µl) was optimal, as it resulted in transgene expression over 40-80% of the peritoneal surfaces while minimizing transgene expression outside of the peritoneal cavity. In addition, delivery in a small volume is desirable, as it maximizes proximity of the virus particle to the cell surface and reduces the potential for virus loss by removal of excess fluid from the peritoneal cavity. As with ex vivo-mediated gene transfer, transgene expression was limited only to the mesothelial cell itself; β-gal-specific staining was absent from the underlying interstitium and muscle layers.

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Delivery of therapeutic proteins to the systemic circulation via mesothelial cellmediated gene therapy, demonstrated by our ex vivo model of gene transfer, has also been demonstrated by an in vivo approach. The ability of genetically modified mesothelial cells to deliver erythropoietin (EPO)103 and α1-antitrypsin104 to the systemic circulation was shown in a rat model after intraperitoneal delivery of an adenovirus vector. Similarly, adenovirus-mediated delivery of potentially therapeutic anticancer genes was shown to reduce tumor burden in animal models of intraperitoneal carcinomas.105-112 These studies support the concept of using a genetically modified mesothelium to express and deliver proteins to the systemic circulation, as well as provide therapy for other conditions native to the peritoneal cavity. Nonviral-Mediated Intraperitoneal Gene Transfer Genetic modification of tissue within the peritoneal cavity has also been evaluated using cationic liposomes as gene delivery agents. Delivery of potentially therapeutic anticancer genes appeared to be effective against the peritoneal dissemination of pancreatic cancer in an experimental mouse model113,114 without showing any toxicity or damage to normal organs or tissues. Interestingly, liposome-mediated gene transfer to the peritoneal cavity appeared to result in preferred transfer of sequences to intraperitoneal tumors, as transgene expression in normal mesothelium was minimal. Cellular uptake of the liposome complex is mediated by cell surface characteristics and surface charge. Changing the lipid formulation, in particular the neutral lipid, has had profound effects on improving the efficiency of transfection.115-117 Successful liposome-mediated gene transfer to the mesothelium might be achieved by targeting the liposome complex to a specific mesothelial cell surface receptor.

Regulation of Transgene Expression A successful gene therapy strategy must consist not only of a system by which the genetic information is delivered, either ex vivo or in vivo, to the target cell population, but also of a means by which the expression of the transgene can be controlled. Regulation of gene expression can be accomplished by a number of mechanisms.118,119 Systems have been developed for transcriptional regulation that are based on promoters responsive to antibiotics, 120 synthetic steroids, 121,122 and various physiological conditions (i.e., inflammation).123 Perhaps the best developed system is based on regulation by tetracycline.124 Here, the gene of interest is driven by a promoter responsive to the presence of tetracycline, or a tetracycline analog. Exposure of the genetically modified cells to tetracycline can either activate or inhibit transcription, allowing for tightly regulated transgene expression. Regulation of transgene expression has been evaluated in a model of mesothelial cell-mediated ex vivo gene therapy. Mesothelial cells were transfected with a construct containing the gene for hGH driven by the murine metallothionein (mMT-1) promoter. Transcription from this promoter can be upregulated either transcriptionally or posttranscriptionally by heavy metal ions (e.g., zinc) or by glucocorticoids (e.g., dexamethasone), respectively. Regulation of transgene expression by these two inducers was confirmed in cell culture as hGH production was upregulated in response to the presence of zinc and dexamethasone both singly and in combination.125 Regulation of transgene expression was also accomplished in vivo by both local administration (i.e., through i.p. injection of a zinc-containing solution) and systemic delivery (i.e., including dexamethasone in the animals’ drinking water) of the inducer molecule. The regulation of transgene expression in the peritoneal cavity by a tetracycline derivative has also been reported. Systemic administration of doxycycline resulted in an increase in marker gene expression in genetically modified fibroblasts implanted in the peritoneal cavity of nude mice.126 These

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findings confirm that transgene expression in the peritoneal cavity can be regulated by both systemic and local (i.e., intraperitoneal) delivery of the effector molecule. While the use of a zinc and dexamethasone-responsive construct is acceptable to demonstrate feasibility of transgene control in an experimental system, it would be desirable to develop a therapeutic system in which transgene expression is controlled by specific physiological signals. Varley has reported positive regulation of transgene expression in a dose-dependent fashion by promoters responsive to inflammatory stimuli.127 Use of these or similar promoters may make it possible to produce recombinant proteins in vivo in direct proportion to the intensity and nature of the individual’s inflammatory response. This may be especially pertinent to the regulation of transgene expression during peritoneal inflammation and peritonitis. Other possibilities for regulation of transgene expression include the use of small molecules designed to regulate transcription. For example, a system based on RU486, a synthetic anti-progestin, was shown to induce gene expression at levels significantly below those required for a pharmacological effect.122 The potential to regulate transgene expression through intraperitoneal delivery of a regulator molecule is significant because it provides a means of local control while minimizing any potential side effects that might accompany systemic administration. In the peritoneal dialysis population, the regulator molecule could be delivered easily through the patient’s indwelling catheter, and its residence time in the peritoneal cavity carefully controlled.

Potential for Improving Peritoneal Dialysis Through Genetic Modification The ability to genetically modify the peritoneal membrane, in particular the peritoneal mesothelial cell, has been established. Mesothelial cells can be transfected by a variety of techniques, using both viral and nonviral delivery reagents. Models of ex vivo mesothelial cell-mediated gene therapy, as well as direct in vivo gene transfer, have clearly demonstrated the production of intracellular proteins, the modification of the anticoagulant properties of the mesothelial cell surface with the production of thrombomodulin, and the delivery of a variety of proteins to the systemic circulation, all of which retain biological function. The pharmacological regulation of transgene expression in mesothelial cells by local and systemic administration of effector molecules has also been illustrated. How can all of this information be combined with our knowledge of the peritoneal membrane and its altered physiological state during peritoneal dialysis and inflammation or peritonitis to produce a gene therapy strategy for improving or enhancing membrane function? One can envision several potential applications of mesothelial cell-mediated gene therapy to address the numerous pathophysiological conditions that can arise during peritoneal dialysis; these have been summarized in Table 6.3 and will be discussed here.

Restoration of the Normal Fibrinolytic/Coagulant Balance One of the consequences of peritoneal dialysis is a shift in the balance between intraperitoneal coagulation and fibrinolysis. The decreased fibrinolytic capacity of the membrane, combined with altered mesothelial cell physiology, promotes fibrin deposition on peritoneal surfaces which in turn inhibits re-mesothelialization of the membrane, promotes adhesion formation, and increases the likelihood of irreversible changes leading to peritoneal sclerosis and loss of dialyzing function. It would be advantageous to restore the normal fibrinolytic balance of the membrane altered during peritoneal dialysis and exacerbated during peritonitis. This could be accomplished through increasing the expression of fibrinolytic factors or anticoagulants, or perhaps both. Intraperitoneal administration of recombinant tPA has in fact been shown to reduce intra-abdominal adhesion formation in

glutathione peroxidase catalase

HA synthase

oxygen free RADical injury

decreased levels of HA

CD11/CD18 (antisense transfer to PMNs)

antisense to ICAM, VCAM

PMN-mediated mesothelial cell injury

increase production of HA by the peritoneal mesothelium

increase anti-oxidant potential of the peritoneal mesothelium

reduce MC-PMN adhesion

reduce levels of chemoattractant molecules

block IL-1 and TNF-mediated signaling

soluble receptors IL-1r, sTNFr p55

antisense to MCP-1, IL-8

inhibit IL-1-mediated effects

receptor antagonist IL-1ra

leukocyte infiltration

decrease macrophage synthesis of proinflammatory cytokines

decrease fibrinogenic activity

PAI-1 (antisense)

IL-10

increase anti-coagulant capacity

anti-coagulant TM

peritoneal inflammation

increase fibrinolytic activity

fibrinolytic factor tPA

altered fibrinolytic/ procoagulant balance

ANTICIPATED RESULT

THERAPEUTIC MOLECULE

CONDITION

Table. 6.3. Gene therapy for peritoneal dialysis: Modification of peritoneal membrane function

maintain physiological homeostasis; increase net ultrafiltration

reduce oxidative damage

reduced leukocytemediated MC damage

modulate leukocyte responses; reduce MC damage

prevent excess fibrin deposition and adhesion formation

THERAPEUTIC BENEFIT

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animal models of wound healing.128,129 Genetic modification of mesothelial cells to provide regulated expression of tPA may be just as effective. Thrombomodulin might also be a good candidate for increasing the anticoagulant capacity of the membrane. TM is present on normal human peritoneal mesothelial cells130 and may contribute to the maintenance of a profibrinolytic surface. However, its expression is downregulated by the proinflammatory cytokine TNFα in endothelial cells.131 If this same situation exists in mesothelial cells, it may contribute to the change to a procoagulant surface during chronic inflammation and peritonitis. Genetic modification of the membrane to produce transgenic TM in a controlled fashion might compensate for the decrease in endogenous TM, and restore the net fibrinolytic capacity. Conversely, or in conjunction with tPA therapy, the production of the fibrogenic factor PAI-1 could be inhibited through the transfer of specific antisense cDNAs or antisense oligonucleotides. The feasibility of this approach has been documented by the delivery of PAI-1 antisense oligonucleotides to cultured endothelial and smooth muscle cells, which resulted in a significant decrease in PAI-1 activity.132,133 However, as liposome-mediated gene transfer in the peritoneal cavity results in fairly low transfer efficiencies to the mesothelium, delivery of oligonucleotides would require the development of targeted liposomes, or identification of new lipid formulations with improved delivery to the mesothelium. Alternatively, cells could be modified using an antisense cDNA delivered as an expression cassette, with opportunity for permanent or transient modification and regulated expression. It is anticipated that the genetic modification of mesothelial cells with tPA, TM, or anti-PAI-1 would promote the removal of excess fibrin from the membrane and concurrently allow mesothelial cell repopulation of the membrane to prevent the progression towards peritoneal fibrosis and adhesion formation. This gene therapy strategy could also be used to prevent adhesions that might occlude the catheter in peritoneal dialysis patients and would be applicable in nondialysis settings to prevent post-surgical complications.

Modulation of Proinflammatory Signals It would be beneficial to engineer a peritoneal membrane that is more refractory to activation by IL-1 and TNFα. These cytokines, produced by activated macrophages during the early stages of inflammation and peritonitis, play a role in the activation of mesothelial cells and indirectly contribute to the production of chemoattractants and adhesion molecules, and to ECM deposition. Genetically modifying mesothelial cells to produce antiinflammatory molecules such as IL-10, the receptor antagonist to IL-1 (IL-1ra) or a soluble receptor for TNFα may be effective as an antiinflammatory strategy. IL-10 is known to inhibit the production of IL-1, TNFα, and IL-6 by LPS-activated macrophages.134 Through the inhibition of IL-1 activity, IL-10 may minimize IL-1 activation of mesothelial cells and the subsequent enhancement of chemoattractant and adhesion molecule synthesis. In addition, IL-10 may exert an antagonizing effect on IL-1 activity by inducing the expression of IL-1ra in macrophages,135 and increasing the ratio of IL-1ra to IL-1β in neutrophils and monocytes.136 By inhibiting TNFα production, IL-10 could limit TNFα-mediated upregulation of PAI-1, and curtail the sequence of events leading to peritoneal adhesion formation. Interestingly, intraperitoneal administration of IL-10 in a murine model has been reported to be effective at limiting post-operative adhesion formation with minimal clinical side effects,137 possibly through this pathway. IL-10 may have antioxidant potential as well, as it significantly inhibited the oxidative burst when added to cultures of unstimulated and activated neutrophils.138 Finally, two recent studies of adenovirusmediated delivery of IL-10 in animal models of endotoxemia resulted in decreased synthesis of

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inflammatory cytokines and increased survival.139,140 These studies establish a precedent for the use of IL-10 as a potential therapeutic by gene transfer. Such a strategy (i.e., delivery of IL-10) may be more effective than the use of soluble cytokine receptors and antagonists (e.g., the IL-1 receptor antagonist, IL-1ra and the soluble TNFα receptors, sTNFRp55 and p75) that neutralize the effects of the inflammatory cytokines through competitive ligand binding. Although IL-1 and IL-1ra bind to the same cell surface receptors, binding of IL-1ra does not induce any of the signaling mechanisms seen in the binding of IL-1. However, IL-1ra appears to be required in 10 to 1000-fold molar excess to fully neutralize the effects of IL-1.141,142 Nonetheless, it may be a viable strategy to pursue, as intraperitoneal administration of IL-1ra was able to prevent IL-1-induced local accumulation of neutrophils in a mouse model of peritonitis. 142 In addition, constitutive systemic expression of IL-1ra or the soluble TNF receptor by genetically modified cells suppressed IL-1 and TNFα-mediated responses in an animal model of endotoxemia.143

Modulation of Leukocyte Infiltration Another approach to moderate the consequences of peritoneal inflammation is to limit leukocyte infiltration and make the mesothelium more resistant to the effects of leukocytemediated mesothelial cell damage. One strategy may be to target the chemoattractants MCP-1 and IL-8 secreted by activated mesothelial cells in response to IL-1 and TNFα. The delivery of antisense oligonucleotides or antisense cDNA for either of these factors may potentially decrease their synthesis and lessen their contribution to leukocyte infiltration during inflammation. This scheme may also be applied to the mesothelial cell-produced adhesion molecules ICAM-1, ICAM-2 and VCAM-1. These molecules are upregulated during inflammation and facilitate leukocyte adhesion to activated mesothelial cells through interactions with the leukocyte CD11/CD18 integrins. It has been shown that mesothelial cell injury can be reduced by blocking the adhesion of activated PMNs to mesothelial cells using antibodies to these adhesion molecules.73,144,145 Inhibition of mesothelial cell-leukocyte interactions may be attempted by a gene therapy approach directed towards blocking cell surface expression of ICAM-1 on the mesothelial cells or the corresponding ligands on the leukocytes. This could be mediated by an antisense approach, through the delivery of antisense genes to the mesothelial cells, or perhaps through liposome-mediated transfer of antisense oligonucleotides to the leukocytes. Antisense oligonucleotides to adhesion molecules have been effective in attenuating reperfusion injury and renal failure in an ischemic rat model,146 illustrating the feasibility of this approach.

Prevention of Oxidative Injury A gene therapy strategy can be proposed to make the mesothelium more resistant to activated leukocyte-mediated oxidative damage. Studies on cultured pleural mesothelial cells suggest that glutathione peroxidase is a major source of protection against low levels of oxidant stress, and that catalase protects against severe oxidant stress.147 These two enzymes may therefore be candidates for therapeutic intervention to increase the antioxidant capacity of the membrane. Adenovirus-mediated gene transfer of catalase to endothelial cells in culture has provided protection against oxidant-mediated injury.148 A similar approach to raise the levels of catalase or glutathione peroxidase through genetic modification of the mesothelial cell might increase the antioxidant potential of the cell, and provide a measure of protection against free RADical damage. Cell culture studies have suggested a correlation between the concentration of intracellular glutathione in cells and their susceptibility to injury by free RADicals. Exposure of cells to dialysis solutions led to a decrease in glutathione levels and a subsequent increase in cell injury.149 Therefore, a gene

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therapy strategy to protect against oxidative injury might involve genetic modification with the gene for glutathione peroxidase, coupled with the addition of a glutathione precursor to dialysis solutions to maintain glutathione levels and provide the substrate for the enzyme.

Maintenance of Physiological Homeostasis Hyaluronan (HA) is another potential therapeutic for the maintenance of physiological homeostasis in the peritoneal cavity. HA contributes to peritoneal cavity physiology in several capacities. It is an essential component of the interstitium and ECM, and contributes to wound healing and tissue regeneration.36,150 HA may modulate membrane damage during inflammation and peritonitis due to its ability to suppress the release of oxygen RADicals from peritoneal macrophages, its action as a free RADical scavenger,151 as well as its ability to suppress elastase release from peritoneal leukocytes.92 While its synthesis is increased during dialysis,36,88 possibly to compensate for dialysate-induced damage, and in response to proinflammatory cytokines,152 its turnover is also increased.153 Exacerbating this is the fact that it is also being continuously removed from the peritoneal cavity with successive exchanges of dialysate. As a result, protective levels in the peritoneal membrane are probably not achieved. Supplementation of dialysate with hyaluronan in a rat model has been reported to increase net ultrafiltration154,155 and prevent peroxidation of the membrane.151 Consequently, its use as a dialysate additive has been considered.151 Alternatively, therapeutic levels of HA may be provided through a gene therapy approach by genetic modification of the mesothelium with the gene for HA synthase, the enzyme responsible for its production. Raising HA production through increased HA synthase may supplement endogenous production, yielding levels that would provide a therapeutic benefit. In this scenario, permanent genetic modification of the membrane, with continuous expression would be optimal, as opposed to other therapies in which the transgene expression is needed only for a short period of time.

Potential Limitations of Gene Therapy for Peritoneal Dialysis The strategies for the genetic modification of the peritoneal membrane to improve peritoneal dialysis as suggested here are promising. However, as with all new and untried therapies, therapeutic benefit needs to be proven and limitations of the therapy identified. It is likely that some therapies will not be appropriate for certain patients, and that ultimately the choice of a gene therapy regimen will be tailored for the individual patient, based on the condition to be treated and other coexisting conditions or pathologies. For example, a patient with a bleeding disorder would not be a candidate for gene therapy based on expression of molecules affecting the coagulation cascade. A therapy designed to reduce inflammation may not be appropriate for a patient with a history of peritonitis, as it may compromise the host response to infection. In addition, the expression of factors designed to maintain the fibrinolytic balance in the peritoneal cavity and prevent adhesion formation should be transient and tightly controlled, so that normal tissue repair and wound healing are not compromised. As many of the proposed therapies are based on a modification of a normal physiologic process (i.e., reduction of inflammation, increase in membrane fibrinolytic capacity), systems must provide defined levels of transgene expression, with the ability to terminate expression when no longer needed, or permanently in the event of adverse reactions. As such, delivery of a therapeutic protein through genetic engineering must be thought of as analogous to a drug delivery regimen, both in the need for regulated and controlled expression at a therapeutic level, and that it must be tailored to the individual patient.

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Formulation of a Successful Mesothelial Cell-Mediated Gene Therapy Strategy The development, and more importantly, implementation of a successful strategy for improving peritoneal dialysis through genetic modification of the peritoneal membrane depends on a number of key issues. The targeted pathophysiological condition, the delivery vehicle, and candidate genes are all interrelated and must be carefully evaluated. A number of important questions must be answered in order to formulate a prospective therapeutic strategy. Would the condition being addressed be better served by long-term or transient expression of the therapeutic protein or factor? What delivery agent would be most appropriate, one that provides integration into the host genome or one that exists in episomal form? And, can it be modified to target the mesothelial cells, minimizing the possibility of gene transfer to another cell type? Or, is the goal to modify another cell type, such as the peritoneal leukocyte? Is repeat administration of the delivery agent possible? Does the therapy call for delivery of a gene for a specific protein, or delivery of an antisense oligo to inhibit the expression of a molecule? What regulatory system would provide the tightest control over transgene expression, a promoter responding to a physiological signal, or an exogenously administered effector molecule? And finally, how does the environment of the peritoneal cavity during peritoneal dialysis alter gene delivery characteristics? These are just some of the questions that would have to be addressed in formulating a successful strategy.

Conclusion In this chapter we have discussed tissue engineering in the context of improving the therapy of peritoneal dialysis as it is currently practiced by genetically modifying the peritoneal membrane to improve its performance as a dialyzing membrane. We have provided a basic discussion of the concepts and applications of gene therapy, and described how this has been applied to modification of the peritoneal mesothelium and the environment of the peritoneal cavity. It is important to continue to define functions and properties of the peritoneal membrane, and how they may become adversely affected and compromised during peritoneal dialysis. As our knowledge of the mesothelium and surrounding tissue expands, so will our ability to identify critical membrane components and factors that can be incorporated into a genetic modification strategy for preserving and enhancing peritoneal membrane function and for improving the environment in the peritoneal cavity during peritoneal dialysis.

Notation ATP β-gal CAPD cDNA DNA ECM EPO HA hGH ICAM-1 ICAM-2 IGF-1 IL-1α IL-1β

adenosine triphosphate beta-galactosidase continuous ambulatory peritoneal dialysis complementary deoxyribonucleic acid deoxyribonucleic acid extracellular matrix erythropoietin hyaluronan human growth hormone intercellular adhesion molecule 1 intercellular adhesion molecule 2 insulin-like growth factor 1 interleukin-1 alpha interleukin-1 beta

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IL-1ra IL-6 IL-8 IL-10 kDa LPS MCP-1 MMP9 mMT-1 mRNA NIH-RAC PAI-1 PAI-2 PGE2 PGI2 PMNs RANTES sTNFRp55 TF TGF-β TIMP TM TNFα tPA uPA VCAM-1

interleukin-1 receptor antagonist interleukin-6 interleukin-8 interleukin-10 kilodaltons lipopolysaccharide macrophage chemoattractant protein1 matrix metalloproteinase-9 murine metallothionein-1 messenger ribonucleic acid National Institutes of Health-Recombinant DNA Advisory Committee plasminogen activator type 1 plasminogen activator type 2 prostaglandin E2 prostacyclin polymorphonuclear leukocytes “Regulated upon Activation, Normal T-cell Expressed and Secreted” cytokine soluble tumor necrosis factor receptor p55 tissue factor transforming growth factor beta tissue inhibitor of metalloproteinases thrombomodulin tumor necrosis factor alpha tissue plasminogen activator urokinase-type plasminogen activator vascular cell adhesion molecule 1

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Index A

G

Access 32-35, 38, 39, 41, 43, 44, 47, 48, 50, 52, 54, 55, 77, 92, 94, 101, 102, 105, 136

Gene therapy 125-128, 135-144 Growth factor 96, 101, 106, 111-113, 116-118, 143, 144

B Bioreactor 93, 95 Blood pressure 4, 7, 15, 19, 41, 65, 81 Blood volume 3, 6, 7, 15-19, 45

C

H Hemodialysis 1-20, 25-32, 34, 35, 37, 38, 41, 43, 47-49, 51-54, 57, 59-62, 78, 79, 91, 98, 104

I

Capillary 3, 6, 7, 12, 17, 19, 29, 61, 62, 65-77, 80, 83-86, 93, 95, 107, 114 Catheters 101, 102, 104-106, 111-118 Coagulant 131, 132, 135, 138-140, 146, 148 Compartment 1-14, 16, 17, 19, 20, 25, 28-30, 32-45, 47-52, 55, 62, 67, 75, 79, 82, 83, 85, 86, 96 Confluent 92, 93, 96 Construct 13, 92, 94, 95, 101, 127, 137, 138 Convection 25-29, 59, 62, 63, 65, 68, 70, 72, 77, 78, 84

Infection 101, 104-107, 109, 114, 116, 131-133, 142 Inflammation 131-133, 137-142 Intake 3-5, 12-16, 18, 20 Interdialytic 4, 5, 12-15, 17, 19, 20 Interstitial tissue 16 Interstitium 62, 65, 66, 69-72, 74-77, 84, 85, 128, 133, 136, 142 Intracellular fluid 2, 8, 20 Intraperitoneal pressure (i.p) 64

D

K

Diffusion 1, 3, 11, 20, 25-30, 39, 41-43, 45, 54, 59, 62, 64, 65, 69, 70, 76, 77, 78, 84, 96, 117 Distributed model 70, 84

Kt/V 14, 36-39, 44, 48-53

E Endocrinologic 91, 98 Epidermal downgrowth 103, 109, 110, 114 Epithelial cell 91-93, 98, 103, 108, 109, 115 Exit site 101-106, 109, 110, 114, 116 Extracellular fluid 1-5, 8, 11-13, 15, 16, 19, 20, 107 Extracellular matrix 101, 118, 133, 143

F Fibrinolytic 131-133, 138-140, 142 Flow 1, 10, 12, 13, 16-19, 25-36, 38-48, 50, 52, 54, 55, 60-66, 72, 73, 75, 76, 78-86, 94-97, 104 Fluid loss 63, 75, 78, 79, 82, 83, 86

L Lymph 60, 62-64, 66, 70, 72, 73, 78, 79, 82-85, 102, 110, 133, 135

M Membrane 3, 8-13, 25-28, 30, 38, 39, 43, 47, 55, 61, 62, 65, 67-71, 73, 75, 79, 80, 82, 83, 86, 91-93, 96-98, 103, 107-109, 114, 116, 125, 128, 131-136, 138-143 Mesothelial cell 60, 66, 67, 125, 128, 131-138, 140, 141, 143 Mesothelium 67, 78, 128, 131, 133-137, 139-143 Metabolic 41, 91-94, 96-98, 101 Model 1, 2, 5-13, 15-20, 25, 34-45, 47-49, 52, 54, 55, 59, 62, 67, 70, 74, 75, 77-86, 91, 93-95, 101, 103, 104, 107-110, 113-117, 125, 131, 133-138, 140-142

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O

S

Osmosis 67, 79 Oxidative injury 132, 141, 142

Scale-up 94, 98 Sodium kinetics 1, 8, 9, 11, 12, 19, 20

P

T

Percutaneous 101, 103, 104, 106-111, 114, 115, 125 Peritoneal dialysis 59-62, 79, 84, 86, 91, 101, 118, 125, 128, 132-134, 136, 138-140, 142-144 Peritoneal membrane 70, 125, 128, 132-135, 138-140, 142, 143 Plasma 1-19, 29, 63, 70, 73, 75, 78-80, 82, 97, 112 Proinflammatory 131, 132, 139, 140, 142

Total body water 1, 2, 4, 8, 10, 11-13, 16, 20, 35, 41-43, 45, 54, 82 Transgene expression 134-138, 142, 143

R

Volume 1-20, 26, 27, 29, 31, 34-37, 39-45, 48-50, 52, 54, 55, 64, 65, 67, 68, 70, 72, 77-83, 85, 86, 92, 96, 101, 110, 136

Renal assist device (RAD) 2, 4, 7, 8, 12-20, 25, 27, 28, 30, 32-35, 38, 39, 41, 43, 46-49, 51, 54, 55, 60, 65, 66, 69, 71-75, 78-80, 83, 91-98, 104, 111, 113, 114, 117, 118, 131-133, 139, 141, 142 Reabsorptive 91, 92, 94, 98 Recirculation 25, 31-35, 38, 39, 41, 43, 44, 47, 48, 52, 54, 55 Renal tubule 91, 92, 94-98

U Urea 1, 11, 12, 14, 15, 25, 27, 29-32, 34, 35, 37-47, 49-55, 62, 66, 73, 79-83

V

W Wound healing 101, 104, 106, 111-114, 116, 117, 140, 142