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Chapter 4.2 194 PATIENTS AND METHODS Patients Eleven chronic hepatitis B patients [all liver-biopsy proven or serum hepatitis B surface antigen (HBsAg)-positive for at least 6 months] with breakthrough HBV-DNA on lamivudine therapy received tenofovir 300 mg once daily while maintaining their existing therapy, which included lamivudine. Five of these patients were co-infected with HIV. Sequential sera, taken at day 1 (at t = 0 and t = 8 h), days 2, 4, 7, 10, 14, 21, 28 and every 4 weeks thereafter, were quantitatively assessed for HBV-DNA. The presence of YMDD mutants was determined at t = 0 and t = 28 days. Virological measurements The HBV-DNA was isolated using the MagnaPure LC isola tion station (Roche Applied Science, Penzberg, Germany) with a modifi ed protocol HBV-02 in which the proteinase K digestion occurred first. 20 HBV-DNA serum levels were quantitatively assessed using the HBV-DNA TaqMan assay and calibrated using EUROHEP HBV-DNA standards. 21 The TaqMan assay enabled accurate quantitative determination to levels of 1000 copies/ mL. 20 At days 1 and 28, HBV polymerase mutant analysis was performed on HBV-DNA using a Line Probe assay (INNO-LiPA HBV DR; Innogenetics N.V., Ghent, Belgium). 22 Where the INNO-LiPA assay was indeterminate, sequence analysis was used. A selected genome region of the polymerase gene was amplifi ed and sequenced with particular primers described earlier. 23 Models for viral dynamics during the fi rst 4 weeks of treatment Mathematical modelling of viral decline was previously described by Neumann et al. 18 for hepatitis C and Nowak et al. and Tsiang et al. 24–25 for hepatitis B. We have used Neumann’s biphasic-exponential model to describe the viral decay during the fi rst 28 days of treatment in our patients: V(t) = V 0 {A exp[-λ 1 t] + (1 – A) exp [-λ 2 t]} where, V 0 = initial viral load; λ 1 = slope of the fi rst phase of viral decline; λ 2 = slope of the second phase of viral decline; A = (εc - λ 2 )/(λ 1 - λ 2 ); λ 1,2 = 1/2{(c + δ)± [(c - δ) 2 + 4(1 - ε) (1 - η) cδ] 1/2 }; t = time; δ = death rate of productively infected cells; c = clearance rate of free virus; ε = effectiveness of tenofovir in blocking virion production in infected cells;
Viral dynamics during tenofovir therapy 195 η = effectiveness of tenofovir in blocking the de novo infection of uninfected cells. The fi rst-phase decline refl ects the clearance rate of free virus from plasma; the secondphase decline refl ects the death rate of productively infected cells. We used two different approaches to describe viral decay: individual nonlinear fi tting and mixed-effect group fi tting. Mixed modelling implies a group-wise analysis while each patient retains his or her own subject-specifi c decline by introducing random effects on all parameters. All variables as well as all patient data are related; based on these data, group effects can be derived and compared. In the group fit approach, the random effect of λ1 was set to zero, because of lack of variation between individuals. This indicates that λ1 is stable and therefore justifi es the choice of a fi xed λ1. The nonlinear modelling approach, which was used to fit the biphasic model, was conducted in the NLINMIX macro in SAS 8.02. Neumann et al. 18 assumed that η = 0 (there was no block of de novo infection of uninfected cells), while Tsiang et al. 24 and Nowak et al. 25 assumed that η = 1 (there was a complete block of infection of uninfected cells). If η = 1, then λ2 = δ, and A refl ects antiviral effi cacy. We have explored both assumptions in both models (individual vs group fit) as a possibility and we report the mathematical effi cacy for all four situations. Models of viral dynamics during the fi rst 24 weeks of treatment Validated models of HBV viral kinetics are available only for the fi rst 4 weeks of therapy and are unavailable for later viral kinetics. The viral kinetics patterns have been classifi ed according to the defi nitions used by Neumann et al. for describing HBV-DNA early kinetics in chronic hepatitis B patients treated with adefovir dipivoxil. 26 We modifi ed these existing defi nitions to describe the fi rst 24 weeks of kinetics, taking into account the availability of frequent quantitative HBV-DNA measurements in the fi rst 4 weeks of tenofovir treatment. First, we investigated viral decay in the fi rst week (fi rst-phase), then we examined decay in the following 23 weeks (second-phase), which we further divided into two periods (up to 4 weeks, days 8–28 and up to 24 weeks, days 29–168). Defi nitions of viral kinetic patterns in the fi rst-phase (days 1–7): 1 Rapid (R): decline of =1 log 2 Slow (S): decline between 0.5 and 1 log 3 Flat (F): decline of 1 log/4 weeks
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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
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
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: INTRODUCTION Viral dynamics during
Page 199 and 200: 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
Page 245 and 246: 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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