Physiologically based pharmacokinetic modeling for assessing the clinical drug–drug interaction of alisporivir

Abstract

Alisporivir is a novel cyclophilin-binding molecule with potent anti-hepatitis C virus (HCV) activity. In vitro data from human liver microsomes suggest that alisporivir is a substrate and a time-dependent inhibitor (TDI) of CYP3A4. The aim of the current work was to develop a novel physiologically based pharmacokinetic (PBPK) model to quantitatively assess the magnitude of CYP3A4 mediated drug–drug interactions with alisporivir as the substrate or victim drug. Towards that, a Simcyp PBPK model was developed by integrating in vitro data with in vivo clinical findings to characterize the clinical pharmacokinetics of alisporivir and further assess the magnitude of drug–drug interactions. Incorporated with absorption, distribution, elimination, and TDI data, the model accurately predicted AUC, C_max, and t_max values after single or multiple doses of alisporivir with a prediction deviation within ±32%. The model predicted an alisporivir AUC increase by 9.4-fold and a decrease by 86% when alisporivir was co-administrated with ketoconazole (CYP3A4 inhibitor) or rifampin (CYP3A4 inducer), respectively. Predictions were within ±20% of the observed changes. In conclusion, the PBPK model successfully predicted the alisporivir PK and the magnitude of drug–drug interactions.

Introduction

Allometry has long been used in predicting human pharmacokinetics (PK) utilizing data from preclinical species (Mahmood, 1999) However, due to notable interspecies differences, especially with metabolizing enzymes and drug transporters, alternative approaches have been proposed over the years to obtain improved predictions. Unlike allometry and other empirical pharmacokinetic models, physiologically based pharmacokinetic (PBPK) models provide mechanistic time-based profiles by integrating drug-dependent and physiology-dependent parameters, in the process of predicting human pharmacokinetics. In addition to allowing human PK predictions at the discovery stage, PBPK modeling is also useful in early and late development to predict drug exposure for various purposes including drug–drug interactions (DDIs), PK in organ dysfunction, different age and genotype populations (Sinha et al., 2012). The US FDA recommends the use of PBPK models to quantitatively predict the magnitude of DDIs in various clinical situations; furthermore, this approach may offer useful information to facilitate dedicated clinical study designs (US Food and Drug Administration, 2012).

Pharmacokinetic DDIs, an important issue in healthcare, mostly result from the modulations of the drug-metabolizing enzymes, particularly P450 enzymes, which are present in the liver and extra-hepatic tissues. CYP3A4 is the most abundantly expressed P450 enzyme in the liver and gut, and it is involved in the metabolism of more than half of the drugs used clinically (Wienkers and Heath, 2005). A number of important drugs have been identified as substrates, inducers, and/or inhibitors of CYP3A4, and the assessment of the potential for CYP3A4-mediated DDIs is an important part of the clinical development program for any new chemical entity. The Simcyp™ population based ADME simulator, which is commercially available software for PBPK modeling, integrates inter-individual variability into PBPK modeling for the prediction of drug disposition and DDIs in virtual populations. By combining information on physiology, genetic and demography/ethnicity with in vitro data, Simcyp™ simulator can perform extrapolation to in vivo situations and virtual populations (Jamei et al., 2009).

Alisporivir (alisporivir), a hepatitis C antiviral agent, is currently in phase III clinical development. Alisporivir, a structural analog of cyclosporine, has physicochemical properties similar to those of cyclosporine. In vitro data described in this manuscript suggested that alisporivir is a CYP3A4 substrate and a time-dependent inhibitor (TDI) of CYP3A4. Hence, there is a potential for clinically significant DDI when alisporivir is co-administered with inhibitors or inducers of CYP3A4 or other drugs that are substrates of CYP3A4. The objective of the present study is to develop a PBPK model to characterize PK of alisporivir following single and multiple oral administration and assess the DDI effects of a CYP3A4 inhibitor (ketoconazole) and a CYP3A4 inducer (rifampin) on its exposure. Specifically, the metabolic and DDI properties for alisporivir, observed in in vitro results, were incorporated in the model for quantitative prediction of mechanism-based drug disposition and interactions.