Emmanuelle Comets

Population pharmacokinetic-pharmacogenetic study of nevirapine in HIV-infected Cambodian patients

ABSTRACT The aims of this ANRS12154 open-label, single-center, multiple-dose pharmacokinetic study were to characterize nevirapine pharmacokinetics in a Cambodian population of HIV-infected patients and to identify environmental and genetic factors of variability, focusing on the CYP2B6, CYP3A5, and ABCB1 (MDR1) genes. A total of 170 Cambodian HIV-infected patients were included. Nevirapine trough concentrations were measured after 18 and 36 months of starting antiretroviral treatment and in samples drawn during a dosing interval in a subset of 10 patients. All data were analyzed by nonlinear mixed-effects modeling. The effect of covariates was investigated using the population pharmacokinetic model. Patients carrying homozygous loss-of-function alleles CYP3A5 6986A>G, CYP2B6 516G>T, CYP2B6 1459C>T, and ABCB1 3435C>T represent 42.4%, 9.2%, 0%, and 18% of the population, respectively. The median nevirapine trough concentrations did not differ after 18 and 36 months of treatment (5,705 ng/ml [range, ≤50 to 13,871] and 5,709 ng/ml [range, ≤50 to 15,422], respectively). Interpatient and intrapatient variabilities of nevirapine apparent clearance were 28% and 17%, respectively. CYP2B6 516G>T and creatinine clearance were found to significantly affect nevirapine apparent clearance. The estimated nevirapine apparent clearances were 2.95 liters/h, 2.62 liters/h, and 1.86 liters/h for CYP2B6 516GG, CYP2B6 516GT, and CYP2B6 516TT genotypes, respectively. The impact of creatinine clearance was small. This study demonstrates that 95% of the patients had sustained nevirapine exposure well above the 3,000-ng/ml threshold. Nevirapine clearance was shown to be affected by CYP2B6 516G>T genetic polymorphism and creatinine clearance, although this explained only part of the interpatient variability, which remains low compared to that for other antiretroviral drugs.

A comparison of bootstrap approaches for estimating uncertainty of parameters in linear mixed-effects models.

A version of the nonparametric bootstrap, which resamples the entire subjects from original data, called the case bootstrap, has been increasingly used for estimating uncertainty of parameters in mixed-effects models. It is usually applied to obtain more robust estimates of the parameters and more realistic confidence intervals (CIs). Alternative bootstrap methods, such as residual bootstrap and parametric bootstrap that resample both random effects and residuals, have been proposed to better take into account the hierarchical structure of multi-level and longitudinal data. However, few studies have been performed to compare these different approaches. In this study, we used simulation to evaluate bootstrap methods proposed for linear mixed-effect models. We also compared the results obtained by maximum likelihood (ML) and restricted maximum likelihood (REML). Our simulation studies evidenced the good performance of the case bootstrap as well as the bootstraps of both random effects and residuals. On the other hand, the bootstrap methods that resample only the residuals and the bootstraps combining case and residuals performed poorly. REML and ML provided similar bootstrap estimates of uncertainty, but there was slightly more bias and poorer coverage rate for variance parameters with ML in the sparse design. We applied the proposed methods to a real dataset from a study investigating the natural evolution of Parkinsons disease and were able to confirm that the methods provide plausible estimates of uncertainty. Given that most real-life datasets tend to exhibit heterogeneity in sampling schedules, the residual bootstraps would be expected to perform better than the case bootstrap.

Extension of NPDE for evaluation of nonlinear mixed effect models in presence of data below the quantification limit with applications to HIV dynamic model

Data below the quantification limit (BQL data) are a common challenge in data analyses using nonlinear mixed effect models (NLMEM). In the estimation step, these data can be adequately handled by several reliable methods. However, they are usually omitted or imputed at an arbitrary value in most evaluation graphs and/or methods. This can cause trends to appear in diagnostic graphs, therefore, confuse model selection and evaluation. We extended in this paper two metrics for evaluating NLMEM, prediction discrepancies (pd) and normalised prediction distribution errors (npde), to handle BQL data. For a BQL observation, the pd is randomly sampled in a uniform distribution over the interval from 0 to the probability of being BQL predicted by the model, estimated using Monte Carlo (MC) simulation. To compute npde in presence of BQL observations, we proposed to impute BQL values in both validation dataset and MC samples using their computed pd and the inverse of the distribution function. The imputed dataset and MC samples contain original data and imputed values for BQL data. These data are then decorrelated using the mean and variance–covariance matrix to compute npde. We applied these metrics on a model built to describe viral load obtained from 35 patients in the COPHAR 3-ANRS 134 clinical trial testing a continued antiretroviral therapy. We also conducted a simulation study inspired from the real model. The proposed metrics show better behaviours than naive approaches that discard BQL data in evaluation, especially when large amounts of BQL data are present.

Evaluation of bootstrap methods for estimating uncertainty of parameters in nonlinear mixed-effects models: a simulation study in population pharmacokinetics

Bootstrap methods are used in many disciplines to estimate the uncertainty of parameters, including multi-level or linear mixed-effects models. Residual-based bootstrap methods which resample both random effects and residuals are an alternative approach to case bootstrap, which resamples the individuals. Most PKPD applications use the case bootstrap, for which software is available. In this study, we evaluated the performance of three bootstrap methods (case bootstrap, nonparametric residual bootstrap and parametric bootstrap) by a simulation study and compared them to that of an asymptotic method (Asym) in estimating uncertainty of parameters in nonlinear mixed-effects models (NLMEM) with heteroscedastic error. This simulation was conducted using as an example of the PK model for aflibercept, an anti-angiogenic drug. As expected, we found that the bootstrap methods provided better estimates of uncertainty for parameters in NLMEM with high nonlinearity and having balanced designs compared to the Asym, as implemented in MONOLIX. Overall, the parametric bootstrap performed better than the case bootstrap as the true model and variance distribution were used. However, the case bootstrap is faster and simpler as it makes no assumptions on the model and preserves both between subject and residual variability in one resampling step. The performance of the nonparametric residual bootstrap was found to be limited when applying to NLMEM due to its failure to reflate the variance before resampling in unbalanced designs where the Asym and the parametric bootstrap performed well and better than case bootstrap even with stratification.

Population pharmacokinetic analysis of free and bound aflibercept in patients with advanced solid tumors

ObjectiveAflibercept (Zaltrap®) is a novel antiangiogenic agent that binds to vascular endothelial growth factor (VEGF) and inhibits VEGF-dependent tumor growth. We aimed to characterize the population pharmacokinetics (PK) of free and bound aflibercept in patients with solid tumors to examine the influence of covariates on their PK and to evaluate the proposed dosing regimens by simulation.MethodsData from 9 clinical trials with 1,506 cancer patients receiving aflibercept (2–9xa0mg/kg every 2 or 3xa0weeks; 1xa0h IV infusion) as a monotherapy or in combination with various chemotherapies were included. Free and bound aflibercept concentrations were analyzed using a non-linear mixed-effects modeling approach with MONOLIX 4.1.2.ResultsAn approximation of a target-mediated drug disposition model with irreversible binding of free aflibercept to VEGF adequately described the PK of free and bound aflibercept. The typical estimated clearances for free (CLf) and bound aflibercept (CLb) were 0.88 and 0.19xa0L/day, respectively. The volumes of distribution for free (Vp) and bound (Vb) aflibercept were similar (~4xa0L). CLf and Vp increased with body weight and were lower in women. Patients with low albumin (ALB) or high alkaline phosphatase (ALK) had faster CLf compared to a typical patient. Pancreatic cancer may be associated with changes in binding of aflibercept to VEGF. Simulations of different dosing regimens showed that adequate saturation of circulating VEGF was achieved with a dose of 4xa0mg/kg every 2xa0weeks.ConclusionsAflibercept kinetics was most affected by gender, body weight, ALB, ALK and pancreatic cancer. Simulations supported the rationale for the recommended dose of 4xa0mg/kg every 2xa0weeks for aflibercept.

Development of a Complex Parent-Metabolite Joint Population Pharmacokinetic Model

This study aimed to develop a joint population pharmacokinetic model for an antipsychotic agent in development (S33138) and its active metabolite (S35424) produced by reversible metabolism. Because such a model leads to identifiability problems and numerical difficulties, the model building was performed using the FOCE-I and the Stochastic Approximation Expectation Maximization (SAEM) estimation algorithms in NONMEM and MONOLIX, respectively. Four different structural models were compared based on Bayesian information criteria. Models were first written as ordinary differential equations systems and then in closed form (CF) to facilitate further analyses. The impact of polymorphisms on genes coding for the CYP2C19 and CYP2D6 enzymes, respectively involved in the parent drug and the metabolite elimination were investigated using permutation Wald test. The parent drug and metabolite plasma concentrations of 101 patients were analyzed on two occasions after 4 and 8xa0weeks of treatment at 1, 3, 6, and 24xa0h following daily oral administration. All configurations led to a two compartment model with back-transformation of the metabolite into the parent drug and a first-pass effect. The elimination clearance of the metabolite through other processes than back-transformation was decreased by 35% [9–53%] in CYP2D6 poor metabolizer. Permutation tests were performed to ensure the robustness of the analysis, using SAEM and CF. In conclusion, we developed a complex joint pharmacokinetic model adequately predicting the impact of CYP2D6 polymorphisms on the parent drug and its metabolite concentrations through the back-transformation mechanism.

Variations of hepcidin and iron‐status parameters during the menstrual cycle in healthy women

Hepcidin is the central regulator of systemic iron metabolism (Ganz, 2013). In clinical practice, measurements of serum hepcidin (SH) could help to determine the cause of anaemia, iron deficiency and iron overload, to predict iron absorption from food, to optimize the treatment of haemochromatosis and to manage erythropoietin therapy. Interpretation of SH measurement in practice will be dependent on understanding physiological variations of this hormone. Previous studies have suggested that SH varied according to a diurnal cycle (Ganz et al, 2008) and was modified by meals (Schaap et al, 2013). Gender should also be taken into account, as women tend to have lower hepcidin values than men. Moreover, in women, age should be also considered, because values of hepcidin are higher in postthan in pre-menopausal women. Nevertheless, iron status is the main determinant of SH concentration (Galesloot et al, 2011). Blood loss during menses, the main cause of iron deficiency in young women, varies between 20 and 80 ml during a period, representing a loss of 10–40 mg of iron in women with regular menstrual cycles (MC) (Higham et al, 1990). This is significant compared to the unregulated 1 mg eliminated daily through skin, intestinal and urinary cell desquamation, the only other physiological way to eliminate iron. To date, no study has examined whether menses induce significant variations in SH values. The present study (Clinical Trial.gov NCT01764412) was conducted in four French hospitals. Ninety healthy women, aged 18-45 years, with normal iron parameters, regular cycles and normal duration of menses (4 1 days) were included. Fifty-four used oral contraception. Transferrin saturation (TS), serum ferritin (SF), haemoglobin (Hb), serum iron (SI) and SH were measured at six visits distributed throughout the MC. All visits were planned on fasting subjects, between 8am and 9am. Day 0 was defined as the day after menses began. The following 3 visits were scheduled during menses and the following days. The last two visits took place respectively at the middle and at the end of the cycle. Serum hepcidin-25 was quantified using a CE-marked Enzyme Immunoassay (Bachem Inc., Torrance, CA, USA).

Norfloxacin Blood-Brain Barrier Transport in Rats Is Not Affected by Probenecid Coadministration

ABSTRACT The effect of probenecid (PRO) on norfloxacin (NOR) blood-brain barrier transport was investigated with rats by microdialysis. Maximum brain drug concentrations were rapidly attained, and the brain penetration factor was close to 5% in the absence and presence of PRO. In conclusion, PRO has no effect on NOR blood-brain barrier transport.

Model Description Language (MDL): A Standard for Modeling and Simulation

Recent work on Model Informed Drug Discovery and Development (MID3) has noted the need for clarity in model description used in quantitative disciplines such as pharmacology and statistics. 1-3 Cur ...

Joint Model of Iron and Hepcidin During the Menstrual Cycle in Healthy Women

Hepcidin regulates serum iron levels, and its dosage is used in differential diagnostic of iron-related pathologies. We used the data collected in the HEPMEN (named after HEPcidin during MENses) study to investigate the joint dynamics of serum hepcidin and iron during the menstrual cycle in healthy women. Ninety menstruating women were recruited after a screening visit. Six fasting blood samples for determination of iron-status variables were taken in the morning throughout the cycle, starting on the second day of the period. Non-linear mixed effect models were used to describe the evolution of iron and hepcidin. Demographic and medical covariates were tested for their effect on model parameters. Parameter estimation was performed using the SAEM algorithm implemented in the Monolix software. A general pattern was observed for both hepcidin and iron, consisting of an initial decrease during menstruation, followed by a rebound and stabilising during the second half of the cycle. We developed a joint model including a menstruation-induced decrease of both molecules at the beginning of the menses and a rebound effect after menses. Iron stimulated the release of hepcidin. Several covariates, including contraception, amount of blood loss and ferritin, were found to influence the parameters. The joint model of iron and hepcidin was able to describe the fluctuations induced by blood loss from menstruation in healthy non-menopausal women and the subsequent regulation. The HEPMEN study showed fluctuations of iron-status variables during the menstrual cycle, which should be considered when using hepcidin measurements for diagnostic purposes in women of child-bearing potential.