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Chapter 4.2 202 release. Although we cannot rule out a possible effect of tenofovir on blocking infection (η varying between 0 and 100%), the viral kinetic data for ten ofovir could be fi tted assuming both effects (η = 1 and η = 0). The HBV-DNA levels can fl uctuate even in untreated pa tients. However, pretreatment levels of HBV-DNA in the patients in our study were similar to t = 0. This suggests that the rapid decrease in HBV-DNA levels after t = 0 could be attributed to treatment with tenofovir, and was not the consequence of a spontaneous decrease. In our study, 4 weeks after addition of tenofovir to the treatment regimen, a mean log HBV-DNA decline of 2.54 ± 0.91 (median 2.34; range: 1.33 4.02) could be observed. This is comparable with the 2.42 log HBV-DNA decline found in a study with tenofovir in fi ve HIV/HBV co infected resistant patients, 12 and is higher than the 0.9 log HBV-DNA decline in a study performed in 12 HIV/ HBV co-infected patients who were treated with tenofovir. 11 Taken together, the data which showed a mean log HBV-DNA decline of 4.95 ± 0.90 log HBV-DNA (median 5.05; range: 3.64 5.94) after 24 weeks of tenofovir in our study and the data which showed a mean log decline of 3.4 copies/ mL after 24 weeks treatment with adefovir in lamivudine resistant HIV/HBV co-infected patients, 27 suggests that tenofovir may have an important role to play in patients who experience breakthrough viraemia on lamivudine therapy. The second-phase decline in viral levels refl ects the death rate of productively infected cells. The death of these cells is thought to require a host immune response. A possible marker of the strength of host immune response is the level of ALT, which is an indicator of the level of cell damage and death. Previously, authors have observed a positive correlation between the decay rate of infected cells and the pretreatment ALT level among chronic HBV patients who were treated with lamivudine therapy. 25 Another study, which analysed the infl uence of lamivudine dose and baseline ALT on the viral dynamics of the HBV, confi rmed that higher baseline ALT levels were signifi cantly related to the slope of the second-phase of viral decay. 28 Nevertheless, in another study, in which patients were treated with either lamivudine monotherapy or with a combination of lamivudine and famciclovir, the investigators found no association between the slope of the second-phase and baseline ALT. 29 This is in agreement with our study, in which kinetic parameters λ1 , λ2 and e were not associated with the pretreatment ALT levels. This discrepancy with some other studies may be explained by the selection of patients in our study, which included patients with only moderate elevation of ALT. We speculate that the ALT levels were too low to produce a detectable association with the slope of viral decay.
Viral dynamics during tenofovir therapy 203 Our data demonstrate that direct comparison of the effi cacies given by different mathematical models is not always possible. As we have demonstrated, variations between the models with respect to sampling frequencies and duration of follow up result in different outcomes. In addition, our data show that tenofovir is capable of effectively blocking viral replication in patients with lamivudine-induced mutant viruses in both HBV and HBV/ HIV co-infected patients. However, for effective treatment of patients, the first goal should be to totally inactivate disease by completely blocking virion production. In terms of modelling this will mean an antiviral effi cacy ‘ε’ equivalent to 1. Our results show that, in patients with lamivudine-induced drug-resistant mutants, we can reach an effi cacy of 0.99. Therefore, despite the drug having an excellent effect, our data also show some low-grade viral replication remains. We suggest that the residual replication may present a risk for genotypic succession during tenofovir therapy.
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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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Cha pter 3.2 Discriminant analysis
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Introduction Discriminant analysis
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Discriminant analysis using a MLMM
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Estimation Discriminant analysis us
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Discriminant analysis using a MLMM
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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
Page 184 and 185: Chapter 4.1 182 Figure 1.
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 and 194: Cha pter 4.2 Viral dynamics during
Page 195 and 196: INTRODUCTION 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 203: Viral dynamics during tenofovir the
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
Page 245 and 246: Samenvatting en conclusie 243 Er we
Page 247 and 248: Samenvatting en conclusie 245 Data
Page 249 and 250: Samenvatting en conclusie 247 Behan
Page 253: 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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