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Chapter 4.3 222 13-14 This model takes the decreasing effi cacy of PEG-IFN α-2b at the end of the week into account during once-weekly administration. We observed a signifi cant correlation between the AUC of the PEG-IFN α-2b concentration and body mass index (BMI) and a correlation between sex and the AUC of PEG-IFN α-2b. Based on these fi ndings, weightbased PEG-IFN α-2b dosing should also be considered in the treatment of chronic hepatitis B to optimize drug availability as is the standard in hepatitis C treatment. 20-21 However, despite the infl uence of BMI on the pharmacokinetic constants of PEG-IFN α-2b, no clear effect of the PEG-IFN α-2b concentration was observed on treatment outcome or decline in viral load, as previously shown for PEG-IFN α-2a. 22 Furthermore, treatment of chronic hepatitis B patients with escalating doses of both PEG-IFN α-2a and α-2b did not lead to a better treatment outcome in chronic hepatitis B. 5, 16 Next we incorporated the pharmacokinetic model for multiple weekly PEG-IFN α-2b injections proposed recently 13-14 in a combined pharmacokinetic-pharmacodynamic model. Viral kinetics were modelled using equations 3-5 We were able to use per patient PEG-IFN α-2b pharmacokinetics as well as viral kinetics data in 19 patients of the PEG- IFN α-2b monotherapy group. With this approach, it was possible to fi t the viral decline during the fi rst month of PEG-IFN α-2b monotherapy in patients with HBeAg-positive chronic hepatitis B. The maximum antiviral effectiveness of PEG-IFN α-2b monotherapy, εmax , was 70% and this is slightly lower than the antiviral effectiveness (83%) of PEG-IFN α-2b 100/200 μg in HBeAg negative chronic hepatitis B patients in the study by Sypsa et al., probably due to the lower PEG-IFN dose given. 16 There was no clear association between the antiviral effectiveness and several baseline factors, only older patients showed a slightly reduced maximal antiviral effectiveness (p=0.046). This antiviral effectiveness is lower compared to the estimated antiviral effectiveness of approximately 92-99% for nucleos(t)ide analogues. 23-26 In the combination therapy group, viral load showed a biphasic decline pattern as a result of the addition of lamivudine. This pattern has already been extensively described in chronic hepatitis B patients treated with nucleos(t)ide analogues and therefore we did not model viral decline in the combination therapy group. 15, 24-26 In the fi rst week, there was a pronounced decline in viral load in the combination therapy group and after one month of treatment there was a 2.94 log10 copies/ml decline in viral load. In the monotherapy group, probably as a result of the decline in drug concentration associated with once-weekly administration of PEG-IFN α-2b, we observed only a minimal decline in viral load with a rise towards the end of the week as also recently reported by Sypsa et al. in HBeAg-negative chronic hepatitis B 16 Therefore, there was only a limited decrease in viral load at the end of the fi rst week of treatment in the monotherapy group and no clear biphasic decline pattern was observed as seen during
HBV viral kinetics during PEG-IFN 223 PEG-IFN α-2a treatment in HBeAg-negative chronic hepatitis B. 27 After one month of PEG-IFN α-2b monotherapy there was still only a marginal decline of 0.45 log 10 copies/ ml in viral load. Regardless of this minimal decline in viral load early during treatment, treatment outcome was comparable in both treatment arms. 6 This emphasizes that a rapid early antiviral effect of PEG-IFN α-2b is not necessary for a sustained response 24 weeks post-treatment in HBeAg-positive chronic hepatitis B as it is in chronic hepatitis C infection. In line with these results, we previously showed that patients with a delayed rather than with an early viral load decline pattern exhibited the highest rates of HBeAg loss after PEG-IFN α-2b treatment. 10 In conclusion, the pharmacokinetics during the fi rst week of therapy with PEG-IFN α-2b alone showed a peak one day after the administration with a rapid decline thereafter. Concurrently, after an initial decline an increase in HBV DNA was found during the second half of the week. Using the PEG-IFN α-2b pharmacokinetic data it was possible to model the HBV viral dynamics during the fi rst month of treatment. Despite the minimal viral decline in the fi rst weeks of PEG-IFN α-2b treatment, a sustained HBeAg-response was achieved in a considerable proportion of patients
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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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where wi,k(y i, θ) = (k =1,...,K).
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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 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 223: 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
Page 256 and 257: Dankwoord 254 Lieve Marion en Margr
Page 259 and 260: BIBLI OGRAPHY Bibliography 257 1. K
Page 261 and 262: Bibliography 259 Interferon-ribavir
Page 263 and 264: Bibliography 261 Long term clinical
Page 265 and 266: Bibliography 263 Flares in chronic
Page 267 and 268: Bibliography 265 Genetic characteri
Page 269: Bibliography 267 106. Reijnders JG,
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