Marylore Chenel

Predictions of metabolic drug-drug interactions using physiologically based modelling : Two cytochrome P450 3A4 substrates coadministered with ketoconazole or verapamil

AbstractBackground and Objective: Nowadays, evaluation of potential risk of metabolic drug-drug interactions (mDDIs) is of high importance within the pharmaceutical industry, in order to improve safety and reduce the attrition rate of new drugs. Accurate and early prediction of mDDIs has become essential for drug research and development, and in vitro experiments designed to evaluate potential mDDIs are systematically included in the drug development plan prior to clinical assessment. The aim of this study was to illustrate the value and limitations of the classical and new approaches available to predict risks of DDIs in the research and development processes. Methods: The interaction of cytochrome P450 (CYP) 3A4 inhibitors (ketoconazole and verapamil) with midazolam was predicted using the inhibitor concentration/inhibition constant ([I]/Ki) approach, the static approach with added variability (Simcyp®), and whole-body physiologically based pharmacokinetic (WB-PBPK) modelling (acslXtreme®). Then an in-house reference drug was used to challenge the different approaches based on the midazolam experience. Predicted values (pharmacokinetic parameters, the area under the plasma concentration-time curve [AUC] ratio and plasma concentrations) were compared with observed values obtained after intravenous and oral administration in order to assess the accuracy of the prediction methods. Results: With the [I]/Ki approach, the interaction risk was always overpredicted for the midazolam substrate, regardless of its route of administration and the coadministered inhibitor. However, the predictions were always satisfactory (within 2-fold) for the reference drug. For the Simcyp® calculations, two of the three interaction results for midazolam were overpredicted, both when midazolam was given orally, whereas the prediction obtained when midazolam was administered intravenously was satisfactory. For the reference drug, all predictions could be considered satisfactory. For the WB-PBPK approach, all predictions were satisfactory, regardless of the substrate, route of administration, dose and coadministered inhibitor. Conclusions: DDI risk predictions are performed throughout the research and development processes and are now fully integrated into decision-making processes. The regulatory approach is useful to provide alerts, even at a very early stage of drug development. The ‘steady state’ approach in Simcyp® improves the prediction by using physiological knowledge and mechanistic assumptions. The DDI predictions are very useful, as they provide a range of AUC ratios that include individuals at the extremes of the population, in addition to the ‘average tendency’. Finally, the WB-PBPK approach improves the predictions by simulating the concentration-time profiles and calculating the related pharmacokinetic parameters, taking into account the time of administration of each drug — but it requires a good understanding of the absorption, distribution, metabolism and excretion properties of the compound.

Pharmacometrics Markup Language (PharmML): Opening New Perspectives for Model Exchange in Drug Development

The lack of a common exchange format for mathematical models in pharmacometrics has been a long‐standing problem. Such a format has the potential to increase productivity and analysis quality, simplify the handling of complex workflows, ensure reproducibility of research, and facilitate the reuse of existing model resources. Pharmacometrics Markup Language (PharmML), currently under development by the Drug Disease Model Resources (DDMoRe) consortium, is intended to become an exchange standard in pharmacometrics by providing means to encode models, trial designs, and modeling steps.

Pharmacokinetic/pharmacodynamic modelling-based optimisation of administration schedule for the histone deacetylase inhibitor abexinostat (S78454/PCI-24781) in phase I.

Abexinostat, an oral pan-histone deacetylase inhibitor (HDACi), was evaluated in patients with advanced solid tumours in two single agent phase I studies (PCYC-402 and CL1-78454-002). In PCYC-402 study testing four different administration schedules, the maximum tolerated dose (MTD) was established at 75 mg/m(2) BID (twice daily) and the recommended dose at 60 mg/m(2) BID regardless of the schedule tested. The dose limiting toxicity (DLT), consistently observed across all these schedules, was reversible thrombocytopenia. The CL1-78454-002 study was initially investigating an additional schedule of 14 days on/7 days off. While testing two first cohorts, thrombocytopenia was observed without reaching DLT. To address this issue, a pharmacokinetic/pharmacodynamic (PK/PD) model was used to predict the optimal schedule allowing higher doses with minimal thrombocytopenia. Several administration schedules were simulated using this model. A 4 days on/3 days off schedule was associated with the smallest platelet decrease. Accordingly, the CL1-78454-002 study was amended. After reaching MTD1 (75 mg/m(2) BID) with the initial schedule, subsequent cohorts received abexinostat on a revised schedule of 4 days on/3 days off, starting at one dose level below MTD1 (60 mg/m(2) BID). As expected, the dose-escalation continued for two more dose levels beyond MTD1. The MTD2 reached for this optimised schedule was 105 mg/m(2) BID and the recommended dose 90 mg/m(2) BID. In conclusion, early understanding of toxicities and PK determination allowed us to build a PK/PD model of thrombocytopenia, which predicted the optimal administration schedule. This optimised schedule is currently used in the trials in solid tumours with abexinostat.

Current Use and Developments Needed for Optimal Design in Pharmacometrics: A Study Performed Among DDMoRe's European Federation of Pharmaceutical Industries and Associations Members

Methods and software tools for optimal design in nonlinear mixed effect models, based on the Fisher information matrix, have been developed for a decade.1,2 Academic groups regularly proposed new versions.3–5 Present tools do not incorporate adaptive designs for these models although prior information is needed and adaptive designs are increasingly used in drug development.6 We conducted a study among drug companies of the Drug and Disease Model Resources consortium7 to identify current practices and expectations.

Some Alternatives to Asymptotic Tests for the Analysis of Pharmacogenetic Data Using Nonlinear Mixed Effects Models

Nonlinear mixed effects models allow investigating individual differences in drug concentration profiles (pharmacokinetics) and responses. Pharmacogenetics focuses on the genetic component of this variability. Two tests often used to detect a gene effect on a pharmacokinetic parameter are (1) the Wald test, assessing whether estimates for the gene effect are significantly different from 0 and (2) the likelihood ratio test comparing models with and without the genetic effect. Because those asymptotic tests show inflated type I error on small sample size and/or with unevenly distributed genotypes, we develop two alternatives and evaluate them by means of a simulation study. First, we assess the performance of the permutation test using the Wald and the likelihood ratio statistics. Second, for the Wald test we propose the use of the F-distribution with four different values for the denominator degrees of freedom. We also explore the influence of the estimation algorithm using both the first-order conditional estimation with interaction linearization-based algorithm and the stochastic approximation expectation maximization algorithm. We apply these methods to the analysis of the pharmacogenetics of indinavir in HIV patients recruited in the COPHAR2-ANRS 111 trial. Results of the simulation study show that the permutation test seems appropriate but at the cost of an additional computational burden. One of the four F-distribution-based approaches provides a correct type I error estimate for the Wald test and should be further investigated.

Influence of Covariance Between Random Effects in Design for Nonlinear Mixed-Effect Models with an Illustration in Pediatric Pharmacokinetics

Nonlinear mixed-effect models are used increasingly during drug development. For design, an alternative to simulations is based on the Fisher information matrix. Its expression was derived using a first-order approach, was then extended to include covariance and implemented into the R function PFIM. The impact of covariance on standard errors, amount of information, and optimal designs was studied. It was also shown how standard errors can be predicted analytically within the framework of rich individual data without the model. The results were illustrated by applying this extension to the design of a pharmacokinetic study of a drug in pediatric development.

A Physiologically Based Pharmacokinetic Model for Strontium Exposure in Rat

PurposeTo develop a physiologically based pharmacokinetic (PBPK) model to describe the disposition of Strontium—a bone seeking agent approved in 2004 (as its Ranelate salt) for treatment of osteoporosis in post-menopausal women.MethodsThe model was developed using plasma and bone exposure data obtained from ovariectomised (OVX) female rats—a preclinical model for post-menopausal osteoporosis. The final PBPK model incorporated elements from literature models for bone seeking agents allowing for description of the heterogeneity of bone tissue and also for a physiological description of bone remodelling processes. The model was implemented in MATLAB in open and closed loop configurations, and fittings of the model to exposure data to estimate certain model parameters were carried out using nonlinear regression, treating data with a naïve-pooled approach.ResultsThe PBPK model successfully described plasma and bone exposure of Strontium in OVX rats with parameter estimates and model behaviour in keeping with known aspects of the distribution and incorporation of Strontium into bone.ConclusionsThe model describes Strontium exposure in a physiologically rationalized manner and has the potential for future uses in modelling the PK-PD of Strontium, and/or other bone seeking agents, and for scaling to model human Strontium bone exposure.

Model-based approaches for ivabradine development in paediatric population, part I: study preparation assessment

Abstract The main objective was to help design a paediatric study for ivabradine, a compound already marketed in adults, focusing on: the paediatric formulation evaluation, the doses to be administered, the sampling design and the sampling technique. A secondary objective was to perform a comparison of the prediction of ivabradine pharmacokinetics (PK) in children using a physiologically-based pharmacokinetic (PBPK) approach and allometric scaling of a population pharmacokinetic (PPK) model. A study was conducted in order to assess the relative bioavailability (Frel) of the paediatric formulation and a similar Frel was observed between the paediatric formulation and the adult marketed tablet. PBPK modelling was used to predict initial doses to be administered in the paediatric study and to select the most appropriate sample time collections. The dried blood spot technique was recommended in the clinical trial in children. Simulations obtained by both the PBPK approach and allometric scaling of a PPK model were compared a posteriori to the paediatric study observations. Both PPK and PBPK approaches allowed an adequate prediction of the PK of ivabradine and its metabolite in children.

Two-stage Adaptive Designs in Nonlinear Mixed Effects Models: Application to Pharmacokinetics in Children

Nonlinear mixed effects models (NLMEM) are used in pharmacokinetics to analyse concentrations of patients during drug development, particularly for pediatric studies. Approaches based on the Fisher information matrix can be used to optimize their design. Local design needs some a priori parameter values which might be difficult to guess. Therefore, two-stage adaptive designs are useful to provide some flexibility. We implemented in the R function PFIM the Fisher matrix for two-stage designs in NLMEM. We evaluated, with simulations, the impact of one-stage and two-stage designs on the precision of parameter estimation when the true and a priori parameters are different.

Comparison of Nonlinear Mixed Effects Models and Noncompartmental Approaches in Detecting Pharmacogenetic Covariates

Genetic data is now collected in many clinical trials, especially in population pharmacokinetic studies. There is no consensus on methods to test the association between pharmacokinetics and genetic covariates. We performed a simulation study inspired by real clinical trials, using the pharmacokinetics (PK) of a compound under development having a nonlinear bioavailability along with genotypes for 176 single nucleotide polymorphisms (SNPs). Scenarios included 78 subjects extensively sampled (16 observations per subject) to simulate a phase I study, or 384 subjects with the same rich design. Under the alternative hypothesis (H1), six SNPs were drawn randomly to affect the log-clearance under an additive linear model. For each scenario, 200 PK data sets were simulated under the null hypothesis (no gene effect) and H1. We compared 16 combinations of four association tests, a stepwise procedure and three penalised regressions (ridge regression, Lasso, HyperLasso), applied to four pharmacokinetic phenotypes, two observed concentrations, area under the curve estimated by noncompartmental analysis and model-based clearance. The different combinations were compared in terms of true and false positives and probability to detect the genetic effects. In presence of nonlinearity and/or variability in bioavailability, model-based phenotype allowed a higher probability to detect the SNPs than other phenotypes. In a realistic setting with a limited number of subjects, all methods showed a low ability to detect genetic effects. Ridge regression had the best probability to detect SNPs, but also a higher number of false positives. No association test showed a much higher power than the others.