Metabolic-based drug-drug interactions prediction, recent approaches for risk assessment along drug development

Abstract

Prediction of in vivo drug-drug interactions (DDIs) from in vitro and in vivo data, also named in vitro in vivo extrapolation (IVIVE), is of interest to scientists involved in the discovery and development of drugs. To avoid detrimental DDIs in humans, new drug candidates should be evaluated for their possible interaction with other drugs as soon as possible, not only as an inhibitor or inducer (perpetrator) but also as a substrate (victim). DDI risk assessment is addressed along the drug development program through an iterative process as the features of the new compound entity are revealed. Both in vitro and preclinical/clinical outcomes are taken into account to better understand the behavior of the developed compound and to refine DDI predictions. During the last decades, several equations have been proposed in the literature to predict DDIs, from a quantitative point of view, showing a substantial improvement in the ability to predict metabolism-based in vivo DDIs. Mechanistic and dynamic approaches have been proposed to predict the magnitude of metabolic-based DDIs. The purpose of this article is to provide an overview of the current equations and methods, the pros and cons of each method, the required input data for each of them, as well as the mechanisms (i.e., reversible inhibition, mechanism-based inhibition, induction) underlying metabolic-based DDIs. In particular, this review outlines how the methods (static and dynamic) can be used in a complementary manner during drug development. The discussion of the limitations and advantages associated with the various approaches, as well as regulatory requirements in that field, can give the reader a helpful overview of this growing area.

Prediction of Crizotinib-Midazolam Interaction Using the Simcyp Population-Based Simulator: Comparison of CYP3A Time-Dependent Inhibition between Human Liver Microsomes versus Hepatocytes

“…Accordingly, we intended to use crizotinib C sys,u as the inhibitor concentration for DDI prediction by adjusting hepatic k p value (i.e., unity), as described in Materials and Methods. Consistent with our findings, other investigators (Obach et al, 2007;Fahmi et al, 2009;Boulenc and Barberan, 2011) reported that the use of C sys,u (e.g., C max,u or C ave,u ) as the inhibitor concentrations yielded the most accurate DDI predictions for TDI and EI by the static models. When the projected/calculated C inlet,u was used, the DDI predictions for TDI and EI were generally overpredicted, while those for RI were more accurately predicted.…”

“…This dynamic approach is increasingly being employed in drug discovery and development setting to predict pharmacokinetics (PK) and DDI potential in the clinic. Traditionally, DDI predictions have been performed with static mathematical models using various inhibitor concentrations such as hepatic inlet or outlet concentrations in total (protein bound plus unbound) or unbound form (Mayhew et al, 2000;Obach et al, 2007;Fahmi et al, 2009;Boulenc and Barberan, 2011;Mao et al, 2012). In these reports, hepatic DDI magnitudes for RI were reasonably predicted using the projected unbound portal vein (or hepatic inlet) concentration (C inlet,u ), whereas the use of unbound systemic (or hepatic outlet) concentration (C sys,u ) yielded better prediction for TDI and EI compared with C inlet,u .…”

Prediction of Drug-Drug Interactions with Crizotinib as the CYP3A Substrate Using a Physiologically Based Pharmacokinetic Model

“…The US Food and Drug Administration (FDA) and the European Medicines Agency have recently issued DDI guidances (CDER, 2012;CHMP, 2012), that emphasize the use of an integrated mechanistic approach, such as a physiologically based pharmacokinetic (PBPK) model, to quantitatively predict the magnitude of DDIs in the clinic. The dynamic modeling approach is being employed increasingly in all phases of drug discovery and development to evaluate potential DDI risks for NMEs (Boulenc and Barberan, 2011;Zhao et al, 2011;Huang and Rowland, 2012;Peters et al, 2012;Huang et al, 2013). Additionally, regulatory agencies express keen interest in the use of mechanistic dynamic models to provide a deeper understanding of complex DDIs, including simultaneous effects of two or more interacting drugs (e.g., inhibitors and inducers) on exposures of substrate drugs, as well as drug-disease interactions in patients with hepatic or renal impairment (Zhao et al, 2011;Huang and Rowland, 2012;Huang et al, 2013;Varma et al, 2015).…”

Evaluation of Normalization Methods To Predict CYP3A4 Induction in Six Fully Characterized Cryopreserved Human Hepatocyte Preparations and HepaRG Cells

“…Over the last 15 years, drug-drug interactions (DDI) have become one of the emerging topics in clinical drug development process (Boulenc and Barberan, 2011). In the late 1990s health authorities issued dedicated guidelines, which have been recently updated, related to the detection and consequences of DDIs (CDER 2011;CHMP 2012).…”