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Chapter 4.2 192 ABSTRACT Tenofovir, an antihuman immunodefi ciency virus (HIV) drug, has activity against lamivudine-resistant hepatitis B virus (HBV) mutants. To describe the effi cacy of tenofovir in patients with lamivudine-resistant hepatitis B we applied two investigative approaches based on mathematical models of viral dynamics: the individual nonlinear fi tting and the mixed-effect group fi tting approaches. Eleven chronic HBV patients on lamivudine for a median of 176 weeks (range: 72–382) with YMDD mutation-related HBV-DNA breakthrough received ‘add-on’ tenofovir 300 mg once-daily, while maintaining their existing therapy. Sequential sera were taken at day 1 (t = 0 and t = 8 h), days 2, 4, 7, 10, 14, 21, 28 and every 4 weeks thereafter, and HBV-DNA levels were assessed using a validated quantitative polymerase chain reaction (PCR) assay. Median baseline log HBV-DNA was 8.62 (range: 6.48 9.76 log HBV-DNA). Tenofovir treatment resulted in a mean (±SD) log HBV-DNA decline of 1.37 ± 0.51 in the fi rst phase, 2.54 ± 0.91 after 4 weeks, and 4.95 ± 0.90 log HBV-DNA after 24 weeks. The median effectiveness of blocking viral replication in the individual fit model was 93% (range: 73–99) for η=0 and 93% (range: 59–99) for η=1. There was only a small difference between the effi cacy parameter ‘ε’ of the individual nonlinear fi tting and mixed-effect group fi tting on the biphasic exponential model. These data show that tenofovir has good effi cacy in blocking viral replication in HBV patients with lamivudine induced drug-resistant HBV mutants, but effectiveness varies greatly among individuals. Both models can be used to describe viral decay during tenofovir therapy.
INTRODUCTION Viral dynamics during tenofovir therapy 193 Treatment of hepatitis B virus (HBV) infection with standard interferon-α produces a durable response in one-fi fth to one-third of patients but has undesirable side-effects and must be administered subcutaneously three times per week. 1–3 Although lamivudine treatment also produces a modest response rate with few side-effects, prolonged treatment is often necessary to prevent relapse on cessation of therapy, and continuous treatment can lead to the development of lamivudine resistance. 4–5 Phenotypic lamivudine resistance, with the emergence of YMDD drug-resistant mutants in the polymerase gene of the HBV, leads to an increase in serum HBV-DNA levels. This suggests that there is a clinical need for new antiviral agents that adequately inhibit DNA-polymerase activity, both in wild type and in mutant virus populations. The search for drug-resistant mutants is usually initiated after an increase in serum HBV- DNA load has been observed. 6 Studies have shown that the lamivudine mutations are localized in two major domains of the reverse transcriptase (RT) region of the polymerase gene. 7–8 Analyses of the YMDD region of the C-domain of the polymerase gene have shown that, in the case of resistance, methionine (rtM204) is replaced either by valine (rtM204V), isoleucine (rtM204I) or serine (rtM204S). The valine (rtM204V) variant is, in most cases, accompanied by another mutation (leucine to methionine; rtL180M) in the B-domain. 9 A mixture of YMDD variants can exist in one individual. Tenofovir disoproxil fumarate, an acyclic nucleotide analogue RT inhibitor, appears to be effective against the YMDD drug-resistant mutant population. In vitro studies, tenofovir demonstrated a combination of low cytotoxicity and antiviral effi cacy. It was equally effective at inhibiting wild-type HBV-DNA replication and at inhibiting DNA replication in the YMDD variant, rtM180V. 10 Clinical studies investigating the effect of tenofovir on HBV replication have shown that it has signifi cant activity against lamivudineresistant mutants both in chronic HBV patients and in human immunodefi ciency virus (HIV)/HBV co-infected patients. 11–17 Mathematical modelling provides a tool for evaluating the effect of antiviral therapy. It can provide insight into the speed and variability in patterns of viral decay, which may be useful in the design of future treatment strategies. The decay curve of HBV during therapy with nucleoside analogues exhibits a biphasic decline during the fi rst 4 weeks of treatment. Analysis of these viral kinetics can be used to calculate both the effectiveness of therapy in inhibiting viral production as well as the clearance of cells infected with HBV. We have used two previously published models to describe viral decline during treatment in chronic hepatitis B patients and investigate the viral dynamics of HBV replication after the addition of tenofovir to lamivudine therapy. 18–19
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Statistical Models of Treatment Eff
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ISBN: 978-90-8559-069-9 © Bettina
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PROMOTIECOMMISSIE Promotor: Prof.dr
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CONTENTS Chapter 1. Introduction 11
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Cha pter 1 Introduction
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Chapter 1 14 Epidemiology Worldwide
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Chapter 1 16 Prediction models with
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Chapter 1 18 standard method fails
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Chapter 1 20 The extended non-linea
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Chapter 1 22 References 1. Blumberg
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Cha pter 2 Prediction of response t
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Chapter 2.1 28 ABSTRACT The aim of
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Chapter 2.1 30 been hepatitis B sur
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Chapter 2.1 32 HBV DNA decline The
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Chapter 2.1 34 Figure 2 continued.
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Chapter 2.1 36 Figure 4. Estimated
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Chapter 2.1 38 DNA was observed in
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Chapter 2.1 40 15. Kao JH. Role of
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Chapter 2.2 44 ABSTRACT Background:
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Chapter 2.2 46 low baseline HBV DNA
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Chapter 2.2 48 patient subgroups wa
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Chapter 2.2 50 p=0.002). Previous I
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Chapter 2.2 52 Application of the m
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Chapter 2.2 54 PEG-IFN, the proport
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Chapter 2.2 56 References 1. Lavanc
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Chapter 2.2 58 26. Bonino F, Lau GK
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Chap ter 2.3 Prediction of the resp
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INTRODUCTION Dynamic prediction of
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Statistical analysis Dynamic predic
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Dynamic prediction of response to P
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References Dynamic prediction of re
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Cha pter 2.4 Dynamic prediction of
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INTRODUCTION Dynamic prediction of
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For the indirect approach we rewrit
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logit pi,j = logit Pr(Ri =1|visit =
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Dynamic prediction of response usin
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Table 1. Results of different stopp
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Chapter 2.5 106 ABSTRACT Peginterfe
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Chapter 2.5 108 and placebo daily.
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Chapter 2.5 110 for patients with a
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Chapter 2.5 112 Mean HBV DNA declin
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Chapter 2.5 114 peginterferon and n
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Chapter 2.5 116 hepatocellular carc
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Chapter 2.5 118 13. Werle-Lapostoll
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Chapter 2.5 120 Greece - G Germanid
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Chap ter 3.1 Long-term clinical out
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INTRODUCTION Glycyrrhizin for IFN-n
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Statistics Glycyrrhizin for IFN-non
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Glycyrrhizin for IFN-non responders
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Table 2. Time dependent Cox regress
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Page 143 and 144: Cha pter 3.2 Discriminant analysis
Page 145 and 146: Introduction Discriminant analysis
Page 147 and 148: Discriminant analysis using a MLMM
Page 149 and 150: Estimation Discriminant analysis us
Page 153 and 154: where wi,k(y i, θ) = (k =1,...,K).
Page 171 and 172: Appendix Discriminant analysis usin
Page 175: Cha pter 4 Statistical models of ea
Page 178 and 179: Chapter 4.1 176 ABSTRACT Nucleoside
Page 180 and 181: Chapter 4.1 178 at day 1 at t =0 an
Page 182 and 183: Chapter 4.1 180 Table 1. Baseline c
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Page 186 and 187: Chapter 4.1 184 Figure 1. Viral dec
Page 188 and 189: Chapter 4.1 186 suppression in seru
Page 190 and 191: Chapter 4.1 188 13. Seifer M, Hamat
Page 193: Cha pter 4.2 Viral dynamics during
Page 197 and 198: Viral dynamics during tenofovir the
Page 201 and 202: log HBV DNA (copies/mL) 10.0 9.0 8.
Page 211 and 212: Chap ter 4.3 Modelling of early vir
Page 213 and 214: INTRODUCTION HBV viral kinetics dur
Page 215 and 216: HBV viral kinetics during PEG-IFN 2
Page 229: HBV viral kinetics during PEG-IFN 2
Page 233 and 234: SUMMARY AND CONCLUSION Summary and
Page 235 and 236: Summary and conclusion 233 The mode
Page 237 and 238: Summary and conclusion 235 The infe
Page 239: Summary and conclusion 237 Early st
Page 243 and 244: SAMENVATTING EN CONCLUSIE Samenvatt
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Samenvatting en conclusie 243 Er we
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Samenvatting en conclusie 245 Data
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Samenvatting en conclusie 247 Behan
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Dan kwoord
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Dankwoord 254 Lieve Marion en Margr
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BIBLI OGRAPHY Bibliography 257 1. K
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Bibliography 259 Interferon-ribavir
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Bibliography 261 Long term clinical
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Bibliography 263 Flares in chronic
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Bibliography 265 Genetic characteri
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Bibliography 267 106. Reijnders JG,
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