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The pharmacokinetics (PK) of RO7049389, a new hepatitis B virus (HBV) core protein allosteric modulator of class I, and of its active metabolite M5 were studied in fasted and fed conditions after single and multiple once-a-day and twice-a-day doses in healthy subjects and patients with HBV. The nonlinearity of the pharmacokinetics, the large variability, the small sample size per dose arms, the higher plasma exposure in Asians, and the heterogeneity in patient baseline characteristics seen in phase I studies made the ethnic sensitivity assessment and the selection of the recommended phase II dose difficult. A population PK model, simultaneously modeling RO7049389 and M5, was developed to characterize the complex PK, quantify ethnicity (i.e., Asian vs. non-Asian) and gender effects on the PK of RO7049389 and M5, and infer the quantity of RO7049389 in liver relative to plasma. Exposures in the liver are of particular importance for dose selection since the liver is the site of action of the compound. The model described and reproduced the population PK profiles as well as the between-subject variability of RO7049389 and its metabolite. It could show that the PK is similar between healthy subjects and in HBV patients, once the ethnicity and gender effects are accounted for. The model predicts that, despite a large difference in the plasma exposure of RO7049389 between Asians and non-Asians, the exposure in the liver is comparable, allowing the use of the same dose to treat Asian and non-Asian patients. This model provides a valuable basis to develop this new anti-HBV drug and to define optimal dosing.

International audience; The paucity of experimental data makes both inference and prediction particularly challenging in viral dynamic models. In the presence of several candidate models, a common strategy is model selection (MS), in which models are fitted to the data but only results obtained with the "best model" are presented. However, this approach ignores model uncertainty, which may lead to inaccurate predictions. When several models provide a good fit to the data, another approach is model averaging (MA) that weights the predictions of each model according to its consistency to the data. Here, we evaluated by simulations in a nonlinear mixed-effect model framework the performances of MS and MA in two realistic cases of acute viral infection, i.e., (1) inference in the presence of poorly identifiable parameters, namely, initial viral inoculum and eclipse phase duration, (2) uncertainty on the mechanisms of action of the immune response. MS was associated in some scenarios with a large rate of false selection. This led to a coverage rate lower than the nominal coverage rate of 0.95 in the majority of cases and below 0.50 in some scenarios. In contrast, MA provided better estimation of parameter uncertainty, with coverage rates ranging from 0.72 to 0.98 and mostly comprised within the nominal coverage rate. Finally, MA provided similar predictions than those obtained with MS. In conclusion, parameter estimates obtained with MS should be taken with caution, especially when several models well describe the data. In this situation, MA has better performances and could be performed to account for model uncertainty.

BackgroundModelling HCV RNA decline kinetics under therapy has proven useful for characterizing treatment effectiveness.MethodsHere we model HCV viral kinetics (VK) in 72 patients given a combination of danoprevir, a protease inhibitor, and mericitabine, a nucleoside polymerase inhibitor, for 14 days in the INFORM-1 trial. A biphasic VK model with time-varying danoprevir and mericitabine effectiveness and Bliss independence for characterizing the interaction between both drugs provided the best fit to the VK data.ResultsThe average final antiviral effectiveness of the drug combination varied between 0.998 for 100 mg three times daily of danoprevir and 500 mg twice daily of mericitabine and 0.9998 for 600 mg twice daily of danoprevir and 1,000 mg twice daily of mericitabine. Using the individual parameters estimated from the VK data collected over 2 weeks, we were not able to reproduce the low sustained virological response rates obtained in a more recent study where patients were treated with a combination of mericitabine and ritonavir-boosted danoprevir for 24 weeks.ConclusionsThis suggests that drug-resistant viruses emerge after 2 weeks of treatment and that longer studies are necessary to provide accurate predictions of longer treatment outcomes.

A population drug-disease model was developed to describe the time course of influenza virus with and without oseltamivir treatment and to investigate opportunities for antiviral combination therapy. Data included viral titers from 208 subjects, across 4 studies, receiving placebo and oseltamivir at 20 to 200 mg twice daily for 5 days. A 3-compartment mathematical model, comprising target cells infected at rate β, free virus produced at rate p and cleared at rate c , and infected cells cleared at rate δ, was implemented in NONMEM with an inhibitory Hill function on virus production ( p ), accounting for the oseltamivir effect. In congruence with clinical data, the model predicts that the standard 75-mg regimen initiated 2 days after infection decreased viral shedding duration by 1.5 days versus placebo; the 150-mg regimen decreased shedding by an additional average 0.25 day. The model also predicts that initiation of oseltamivir sooner postinfection, specifically at day 0.5 or 1, results in proportionally greater decreases in viral shedding duration of 5 and 3.5 days, respectively. Furthermore, the model suggests that combining oseltamivir (acting to subdue virus production rate) with an antiviral whose activity decreases viral infectivity (β) results in a moderate additive effect dependent on therapy initiation time. In contrast, the combination of oseltamivir with an antiviral whose activity increases viral clearance ( c ) shows significant additive effects independent of therapy initiation time. The utility of the model for investigating the pharmacodynamic effects of novel antivirals alone or in combination on emergent influenza virus strains warrants further investigation.

The aim of the study was to simplify the dosing regimen of peginterferon alfa-2a in paediatric patients with chronic hepatitis C.A population pharmacokinetic (PK) model was developed using PK data from 14 children aged 2-8 years and 402 adults. Simulations were produced to identify a simplified dosing regimen that would provide exposures similar to those observed in the paediatric clinical trials and in the range known to be safe/efficacious in adults. Model predictions were evaluated against observed adult and paediatric data to reinforce confidence of the proposed dosing regimen.The final model was a two compartment model with a zero order resorption process. Covariates included a linear influence of body surface area (BSA) on apparent oral clearance (CL/F) and a linear influence of body weight on apparent volume of distribution of the central compartment (V1 /F). A simplified dosing regimen was developed which is expected to provide exposures in children aged ≥5 years similar to the dosing formula used in the paediatric clinical trial and within the range that is safe/efficacious in adults. This simplified regimen is approved in the EU and in other countries for the treatment of chronic hepatitis C in treatment-naive children/adolescents aged ≥5 years in combination with ribavirin.Pre-existing adult PK data were combined with relatively limited paediatric PK data to develop a PK model able to predict exposure in both populations adequately. This provided increased confidence in characterizing PK in children and helped in the development of a simplified dosing regimen of peginterferon alfa-2a in paediatric patients.

AIMS: A descriptive survey of published population pharmacokinetic and/or pharmacodynamic (PK/PD) analyses from 2002 to 2004 was conducted and an evaluation made of how model building was performed and reported. METHODS: We selected 324 articles in Pubmed using defined keywords. A data abstraction form (DAF) was then built comprising two parts: general characteristics including article identification, context of the analysis, description of clinical studies from which the data arose, and model building, including description of the processes of modelling. The papers were examined by two readers, who extracted the relevant information and transmitted it directly to a MySQL database, from which descriptive statistical analysis was performed. RESULTS: Most published papers concerned patients with severe pathology and therapeutic classes suffering from narrow therapeutic index and/or high PK/PD variability. Most of the time, modelling was performed for descriptive purposes, with rich rather than sparse data and using NONMEM software. PK and PD models were rarely complex (one or two compartments for PK; E(max) for PD models). Covariate testing was frequently performed and essentially based on the likelihood ratio test. Based on a minimal list of items that should systematically be found in a population PK-PD analysis, it was found that only 39% and 8.5% of the PK and PD analyses, respectively, published from 2002 to 2004 provided sufficient detail to support the model-building methodology. CONCLUSIONS: This survey allowed an efficient description of recent published population analyses, but also revealed deficiencies in reporting information on model building.

The uncertainty associated with parameter estimations is essential for population model building, evaluation, and simulation. Summarized by the standard error (SE), its estimation is sometimes questionable. Herein, we evaluate SEs provided by different non linear mixed-effect estimation methods associated with their estimation performances. Methods based on maximum likelihood (FO and FOCE in NONMEMTM, nlme in SplusTM, and SAEM in MONOLIX) and Bayesian theory (WinBUGS) were evaluated on datasets obtained by simulations of a one-compartment PK model using 9 different designs. Bootstrap techniques were applied to FO, FOCE, and nlme. We compared SE estimations, parameter estimations, convergence, and computation time. Regarding SE estimations, methods provided concordant results for fixed effects. On random effects, SAEM and WinBUGS, tended respectively to under or over-estimate them. With sparse data, FO provided biased estimations of SE and discordant results between bootstrapped and original datasets. Regarding parameter estimations, FO showed a systematic bias on fixed and random effects. WinBUGS provided biased estimations, but only with sparse data. SAEM and WinBUGS converged systematically while FOCE failed in half of the cases. Applying bootstrap with FOCE yielded CPU times too large for routine application and bootstrap with nlme resulted in frequent crashes. In conclusion, FO provided bias on parameter estimations and on SE estimations of random effects. Methods like FOCE provided unbiased results but convergence was the biggest issue. Bootstrap did not improve SEs for FOCE methods, except when confidence interval of random effects is needed. WinBUGS gave consistent results but required long computation times. SAEM was in-between, showing few under-estimated SE but unbiased parameter estimations.

To be able to estimate accurately parameters entering a non-linear mixed effects model taking into account that one or more subpopulations of patients can exist rather than assuming that the entire population is best described by unimodal distributions for the random effects, we proposed a methodology based on the likelihood approximation using the Gauss–Hermite quadrature. The idea is to combine the estimation of the model parameters and the detection of homogeneous subgroups of patients in a given population using a Gaussian mixture for the distribution of the random effects. As the accuracy of the likelihood approximation is likely to govern the quality of the estimation of the different parameters entering the nonlinear mixed effects model, we based this approximation on the use of an adjustable Gauss– Hermite quadrature. Moreover, to complete this methodology, we propose a strategy allowing the detection and explanation of heterogeneity based on the Kullback–Leibler test, which was used to estimate the number of components in the Gaussian mixture. In order to evaluate the capability of the method to take into account heterogeneity, this strategy was performed in a PK/PD analysis using the database and the structural model selected in a previous analysis. In this analysis, non-responders were found out using NONMEM [Beal and Sheiner. NONMEM Users Guides. NONMEM Project Group, University of California, San Francisio, 1992] in a population of diabetic patients treated with a once-a-day new formulation of an antidiabetic drug. The authors looked for a subpopulation of patients for whom the therapeutic effect would vanish. In this paper, we looked for subpopulations of patients exhibiting specificities with respect to different parameters entering the description of the effect. The results obtained with our approach are compared in terms of parameter estimation and heterogeneity detection to those obtained in the previous analysis.

In this paper, we are interested in estimating parameters entering nonlinear mixed effects models using a likelihood maximization approach. As the accuracy of the likelihood approximation is likely to govern the quality of the derived estimates of both the distribution of the random effects and the fixed parameters, we propose a methodological approach based on the adaptive Gauss Hermite quadrature to better approximate the likelihood function. This work presents improvements of this quadrature that render it accurate and computationally efficient in the problem of likelihood approximation with, an application to mixture models, models which allow the description of coexistence of several different homogeneous subpopulations specifying the distribution of random effects as a mixture of Gaussian distributions. These improvements are based on a new choice of the scaling matrix followed by its optimisation. An application to a phase III clinical trial of an anticoagulant molecule is proposed and estimation results are compared to those obtained with the most frequently used method in population pharmacokinetic analysis. Moreover, in order to evaluate the accuracy of the estimations, an analysis of simulated pharmacokinetic data derived from the model and the a priori values of population parameters of the previous study are presented. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)