Extension of the SAEM algorithm for nonlinear mixed models with two levels of random effects

This article focuses on parameter estimation of multi-levels nonlinear mixed effects models (MNLMEMs). These models are used to analyze data presenting multiple hierarchical levels of grouping (cluster data, clinical trials with several observation periods,...). The variability of the individual parameters of the regression function is thus decomposed as a between-sub ject variability and higher levels of variability (for example within-sub ject variability). We propose maximum likelihood estimates of parameters of those MNLMEMs with two levels of random effects, using an extension of the SAEM-MCMC algorithm. The extended SAEM algorithm is split into an explicit direct EM algorithm and a stochastic EM part. Compared to the original algorithm, additional sufficient statistics have to be approximated by relying on the conditional distribution of the second level of random effects. This estimation method is evaluated on pharmacokinetic cross-over simulated trials, mimicking theophyllin concentration data. Results obtained on those datasets with either the SAEM algorithm or the FOCE algorithm (implemented in the nlme function of R software) are compared: biases and RMSEs of almost all the SAEM estimates are smaller than the FOCE ones. Finally, we apply the extended SAEM algorithm to analyze the pharmacokinetic interaction of tenofovir on atazanavir, a novel protease inhibitor, from the ANRS 107-Puzzle 2 study. A significant decrease of the area under the curve of atazanavir is found in patients receiving both treatments

Modèles non linéaires à effets mixtes pour les tests d'interactions médicamenteuses (application aux interactions pharmacocinétiques entre inhibiteurs de protéase et analogues nucléosidiques)

PARIS-BIUSJ-Thèses (751052125) / SudocPARIS-BIUSJ-Physique recherche (751052113) / SudocSudocFranceF

Soins palliatifs chez les patients présentant un trouble psychiatrique sévère et persistant

PARIS7-Xavier Bichat (751182101) / SudocSudocFranceF

Impact of modelling intra-subject variability on tests based on non-linear mixed-effects models in cross-over pharmacokinetic trials with application to the interaction of tenofovir on atazanavir in HIV patients.

We evaluated the impact of modelling intra-subject variability on the likelihood ratio test (LRT) and the Wald test based on non-linear mixed effects models in pharmacokinetic interaction and bioequivalence cross-over trials. These tests were previously found to achieve a good power but an inflated type I error when intra-subject variability was not taken into account. Trials were simulated under H0 and several H1 and analysed with the NLME function. Different configurations of the number of subjects n and of the number of samples per subject J were evaluated for pharmacokinetic interaction and bioequivalence trials. Assuming intra-subject variability in the model dramatically improved the type I error of both interaction tests. For the Wald test, the type I error decreased from 22, 14 and 7.7 per cent for the original (n = 12, J = 10), intermediate (n = 24, J = 5) and sparse (n = 40, J = 3) designs, respectively, down to 7.5, 6.4 and 3.5 per cent when intra-subject variability was modelled. The LRT achieved very similar results. This improvement seemed mostly due to a better estimation of the standard error of the treatment effect. For J = 10, the type I error was found to be closer to 5 per cent when n increased when modelling intra-subject variability. Power was satisfactory for both tests. For bioequivalence trials, the type I error of the Wald test was 6.4, 5.7 and 4.2 per cent for the original, intermediate and sparse designs, respectively, when modelling intra-subject variability. We applied the Wald test to the pharmacokinetic interaction of tenofovir on atazanavir, a novel protease inhibitor. A significant decrease of the area under the curve of atazanavir was found when patients received tenofovir

Emergence of Resistance in Normal Human Aerobic Commensal Flora during Telithromycin and Amoxicillin-Clavulanic Acid Treatments

Mean fecal global yeast counts increased similarly during 7 days of treatment with telithromycin (800 mg once daily) or amoxicillin-clavulanic acid (amoxiclav) (1 g of amoxicillin and 125 mg of clavulanic acid 3 times daily) in human volunteers and decreased slowly thereafter. On skin, coagulase-negative staphylococci of decreased susceptibility (DS) to telithromycin increased in the telithromycin group, whereas those with DS to methicillin increased in the amoxiclav group. A similar antibiotic-related shift towards homologous DS was observed for oral nongroupable streptococci (NGS), but in addition, the prevalence of NGS resistant to both classes of antibiotics was significantly greater in the amoxiclav group at days 8 (P < 0.01) and 45 (P < 0.015)

Population pharmacokinetic analysis of lamivudine, stavudine and zidovudine in controlled HIV-infected patients on HAART.

International audienceOBJECTIVE: This work aimed at building a population pharmacokinetic (PK) model for lamivudine (LMV), stavudine (STV) and zidovudine (ZDV), estimating their inter and intraindividual PK variability and investigating the influence of different covariates. METHODS: Population PK of LMV, STV and ZDV was separately evaluated from plasma concentrations obtained in 54, 39 and 27 HIV1-infected patients, respectively, enrolled in the COPHAR1-ANRS102 trial. The primary objective of this trial was to study the pharmacokinetics of indinavir (IDV) and nelfinavir (NFV) in treated patients with a sustained virological response. Concentrations of nucleoside analogs (NA) were measured in plasma as a secondary objective. A one-compartment model with first-order elimination was used, with zero-order absorption for LMV and first-order absorption for STV and ZDV. RESULTS: Mean parameters [interpatient variability in coefficient of variation (CV%)] of LMV, STV and ZDV were: oral volume of distribution (V/F) 145 l (52%), 24 l (81%) and 248 l (80%), oral clearance (Cl/F) 32 l/h, 16 l/h (74%) and 124 l/h (51%), respectively. For LMV, absorption duration (Ta) was 1.46 h (64%). For STV and ZDV, ka was 0.46 h(-1) and 2.9 h(-1), respectively. We found a systematic effect of combination with NFV vs. IDV. We found that intrapatient variability was greater than interpatient variability (except for STV) and greater than 55% for the three drugs. CONCLUSION: This trial enabled the estimation of the population PK parameters of three NA in patients with a sustained virological response, and the median curves could be used as references for concentration-controlled strategies. We observed, as for the protease inhibitors, a great variability of PK parameters

Influence of pharmacogenetics on indinavir disposition and short-term response in HIV patients initiating HAART.

International audienceAIMS: To assess the relationship between genetic polymorphisms and indinavir pharmacokinetic variability and to study the link between concentrations and short-term response or metabolic safety. METHODS: Forty protease inhibitor-naive patients initiating highly active antiretroviral therapy (HAART) including indinavir/ritonavir and enrolled in the COPHAR 2-ANRS 111 trial were studied. At week 2, four blood samples were taken before and up to 6 h following drug intake. A population pharmacokinetic analysis was performed using the stochastic approximation expectation maximization (SAEM) algorithm implemented in MONOLIX software. The area under the concentration-time curve (AUC) and maximum (C(max)) and trough concentrations (C(trough)) of indinavir were derived from the population model and tested for their correlation with short-term viral response and safety measurements, while for ritonavir, these same three parameters were tested for their correlation with short-term biochemical safety RESULTS: A one-compartment model with first-order absorption and elimination best described both indinavir and ritonavir concentrations. For indinavir, the estimated clearance and volume of distribution were 22.2 L/h and 97.3 L, respectively. The eight patients with the *1B/*1B genotype for the CYP3A4 gene showed a 70% decrease in absorption compared to those with the *1A/*1B or *1A/*1A genotypes (0.5 vs. 2.1, P = 0.04, likelihood ratio test by permutation). The indinavir AUC and C(trough) were positively correlated with the decrease in human immunodeficiency virus RNA between week 0 and week 2 (r = 0.4, P = 0.03 and r = -0.4, P = 0.03, respectively). Patients with the *1B/*1B genotype also had a significantly lower indinavir C(max) (median 3.6, range 2.1-5.2 ng/mL) than those with the *1A/*1B or *1A/*1A genotypes (median 4.4, range 2.2-8.3 ng/mL) (P = 0.04) and a lower increase in triglycerides during the first 4 weeks of treatment (median 0.1, range -0.7 to 1.4 vs. median 0.6, range -0.5 to 1.7 mmol/L, respectively; P = 0.02). For ritonavir, the estimated clearance and volume of distribution were 8.3 L/h and 60.7 L, respectively, and concentrations were not found to be correlated to biochemical safety. Indinavir and ritonavir absorption rate constants were found to be correlated, as well as their apparent volumes of distribution and clearances, indicating correlated bioavailability of the two drugs. CONCLUSION: The CYP3A4*1B polymorphism was found to influence the pharmacokinetics of indinavir and, to some extent, the biochemical safety of indinavir