View PDF Version - RePub - Erasmus Universiteit Rotterdam

More documents

Recommendations

Info

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
Page 1:
Statistical Models of Treatment Eff
Page 4 and 5:
ISBN: 978-90-8559-069-9 © Bettina
Page 6 and 7:
PROMOTIECOMMISSIE Promotor: Prof.dr
Page 9 and 10:
CONTENTS Chapter 1. Introduction 11
Page 13:
Cha pter 1 Introduction
Page 16 and 17:
Chapter 1 14 Epidemiology Worldwide
Page 18 and 19:
Chapter 1 16 Prediction models with
Page 20 and 21:
Chapter 1 18 standard method fails
Page 22 and 23:
Chapter 1 20 The extended non-linea
Page 24 and 25:
Chapter 1 22 References 1. Blumberg
Page 27:
Cha pter 2 Prediction of response t
Page 30 and 31:
Chapter 2.1 28 ABSTRACT The aim of
Page 32 and 33:
Chapter 2.1 30 been hepatitis B sur
Page 34 and 35:
Chapter 2.1 32 HBV DNA decline The
Page 36 and 37:
Chapter 2.1 34 Figure 2 continued.
Page 38 and 39:
Chapter 2.1 36 Figure 4. Estimated
Page 40 and 41:
Chapter 2.1 38 DNA was observed in
Page 42:
Chapter 2.1 40 15. Kao JH. Role of
Page 46 and 47:
Chapter 2.2 44 ABSTRACT Background:
Page 48 and 49:
Chapter 2.2 46 low baseline HBV DNA
Page 50 and 51:
Chapter 2.2 48 patient subgroups wa
Page 52 and 53:
Chapter 2.2 50 p=0.002). Previous I
Page 54 and 55:
Chapter 2.2 52 Application of the m
Page 56 and 57:
Chapter 2.2 54 PEG-IFN, the proport
Page 58 and 59:
Chapter 2.2 56 References 1. Lavanc
Page 60 and 61:
Chapter 2.2 58 26. Bonino F, Lau GK
Page 63 and 64:
Chap ter 2.3 Prediction of the resp
Page 65 and 66:
INTRODUCTION Dynamic prediction of
Page 67 and 68:
Statistical analysis Dynamic predic
Page 69 and 70:
Dynamic prediction of response to P
Page 71 and 72:
Page 73 and 74:
Page 75 and 76:
Page 77 and 78:
References Dynamic prediction of re
Page 81 and 82:
Cha pter 2.4 Dynamic prediction of
Page 83 and 84:
INTRODUCTION Dynamic prediction of
Page 85 and 86:
For the indirect approach we rewrit
Page 87 and 88:
logit pi,j = logit Pr(Ri =1|visit =
Page 89 and 90:
Dynamic prediction of response usin
Page 91 and 92:
Page 93 and 94:
Page 95 and 96:
Page 97 and 98:
Page 99 and 100:
Page 101 and 102:
Table 1. Results of different stopp
Page 103 and 104:
Page 105:
Page 108 and 109:
Chapter 2.5 106 ABSTRACT Peginterfe
Page 110 and 111:
Chapter 2.5 108 and placebo daily.
Page 112 and 113:
Chapter 2.5 110 for patients with a
Page 114 and 115:
Chapter 2.5 112 Mean HBV DNA declin
Page 116 and 117:
Chapter 2.5 114 peginterferon and n
Page 118 and 119:
Chapter 2.5 116 hepatocellular carc
Page 120 and 121:
Chapter 2.5 118 13. Werle-Lapostoll
Page 122:
Chapter 2.5 120 Greece - G Germanid
Page 127 and 128:
Chap ter 3.1 Long-term clinical out
Page 129 and 130:
INTRODUCTION Glycyrrhizin for IFN-n
Page 131 and 132:
Statistics Glycyrrhizin for IFN-non
Page 133 and 134:
Glycyrrhizin for IFN-non responders
Page 135 and 136:
Table 2. Time dependent Cox regress
Page 137 and 138:
Page 139 and 140:
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 151 and 152:
Page 153 and 154:
where wi,k(y i, θ) = (k =1,...,K).
Page 155 and 156:
Page 157 and 158:
Page 159 and 160:
Page 161 and 162:
Page 163 and 164:
Page 165 and 166:
Page 167 and 168:
Page 169 and 170:
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 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,
show all

View PDF Version - RePub - Erasmus Universiteit Rotterdam

Create successful ePaper yourself

Delete template?

Save as template?