Investigating ABCB1-Mediated Drug-Drug Interactions: Considerations for In Vitro and In Vivo Assay Design

Abstract

Background

ABCB1 is a key ABC efflux transporter modulating the pharmacokinetics of a large percentage of drugs. ABCB1 is also a site of transporter mediated drug-drug interactions (tDDI). It is the transporter most frequently tested for tDDIs both in vitro and in the clinic.

Objective

Understanding the limitations of various in vitro and in vivo models, therefore, is crucial. In this review we cover regulatory aspects of ABCB1 mediated drug transport as well as inhibition and the available models and methods. We also discuss protein structure and mechanistic aspects of transport as ABCB1 displays complex kinetics that involves multiple binding sites, potentiation of transport and probe-dependent IC50 values.

Results

Permeability of drugs both passive and mediated by transporters is also a covariate that modulates apparent kinetic values. Levels of expression as well as lipid composition of the expression system used in in vitro studies have also been acknowledged as determinates of transporter activity. ABCB1-mediated clinical tDDIs are often complex as multiple transporters as well as metabolic enzymes may play a role. This complexity often masks the role of ABCB1 in tDDIs.

Conclusion

It is expected that utilization of in vitro data will further increase with the refinement of simulations. It is also anticipated that transporter humanized preclinical models have a significant impact and utility.

Key Findings From Preclinical and Clinical Drug Interaction Studies Presented in New Drug and Biological License Applications Approved by the Food and Drug Administration in 2014

Drug Metab Dispos. 2016 Jan; 44(1); 83-101.
Published online 2015 Sep 30

Abstract

Regulatory approval documents contain valuable information, often not published, to assess the drug-drug interaction (DDI) profile of newly marketed drugs. This analysis aimed to systematically review all drug metabolism, transport, pharmacokinetics, and DDI data available in the new drug applications and biologic license applications approved by the U.S. Food and Drug Administration in 2014, using the University of Washington Drug Interaction Database, and to highlight the significant findings. Among the 30 new drug applications and 11 biologic license applications reviewed, 35 new molecular entities (NMEs) were well characterized with regard to drug metabolism, transport, and/or organ impairment and were fully analyzed in this review. In vitro, a majority of the NMEs were found to be substrates or inhibitors/inducers of at least one drug metabolizing enzyme or transporter. In vivo, when NMEs were considered as victim drugs, 16 NMEs had at least one in vivo DDI study with a clinically significant change in exposure (area under the time-plasma concentration curve or Cmax ratio ≥2 or ≤0.5), with 6 NMEs shown to be sensitive substrates of cytochrome P450 enzymes (area under the time-plasma concentration curve ratio ≥5 when coadministered with potent inhibitors): paritaprevir and naloxegol (CYP3A), eliglustat (CYP2D6), dasabuvir (CYP2C8), and tasimelteon and pirfenidone (CYP1A2). As perpetrators, seven NMEs showed clinically significant inhibition involving both enzymes and transporters, although no clinically significant induction was observed. Physiologically based pharmacokinetic modeling and pharmacogenetics studies were used for six and four NMEs, respectively, to optimize dosing recommendations in special populations and/or multiple impairment situations. In addition, the pharmacokinetic evaluations in patients with hepatic or renal impairment provided useful quantitative information to support drug administration in these fragile populations.

Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification

Drug Metab Dispos. 2015 Nov; 43(11); 1823-37.
Published online 2015 Aug 21

Abstract

Modeling and simulation of drug disposition has emerged as an important tool in drug development, clinical study design and regulatory review, and the number of physiologically based pharmacokinetic (PBPK) modeling related publications and regulatory submissions have risen dramatically in recent years. However, the extent of use of PBPK modeling by researchers, and the public availability of models has not been systematically evaluated. This review evaluates PBPK-related publications to 1) identify the common applications of PBPK modeling; 2) determine ways in which models are developed; 3) establish how model quality is assessed; and 4) provide a list of publically available PBPK models for sensitive P450 and transporter substrates as well as selective inhibitors and inducers. PubMed searches were conducted using the terms “PBPK” and “physiologically based pharmacokinetic model” to collect published models. Only papers on PBPK modeling of pharmaceutical agents in humans published in English between 2008 and May 2015 were reviewed. A total of 366 PBPK-related articles met the search criteria, with the number of articles published per year rising steadily. Published models were most commonly used for drug-drug interaction predictions (28%), followed by interindividual variability and general clinical pharmacokinetic predictions (23%), formulation or absorption modeling (12%), and predicting age-related changes in pharmacokinetics and disposition (10%). In total, 106 models of sensitive substrates, inhibitors, and inducers were identified. An in-depth analysis of the model development and verification revealed a lack of consistency in model development and quality assessment practices, demonstrating a need for development of best-practice guidelines.

Organ Impairment-Drug-Drug Interaction Database: A Tool for Evaluating the Impact of Renal or Hepatic Impairment and Pharmacologic Inhibition on the Systemic Exposure of Drugs

CPT Pharmacometrics Syst Pharmacol. 2015 Aug; 4(8); 489-94.
Published online 2015 Jul 14

Abstract

The organ impairment and drug-drug interaction (OI-DDI) database is the first rigorously assembled database of pharmacokinetic drug exposure data from publicly available renal and hepatic impairment studies presented together with the maximum change in drug exposure from drug interaction inhibition studies. The database was used to conduct a systematic comparison of the effect of renal/hepatic impairment and pharmacologic inhibition on drug exposure. Additional applications are feasible with the public availability of this database.

Drug Disposition and Drug-Drug Interaction Data in 2013 FDA New Drug Applications: A Systematic Review

Drug Metab Dispos. 2014 Dec; 42(12); 1991-2001.
Published online 2014 Sep 30

Abstract

The aim of the present work was to perform a systematic review of drug metabolism, transport, pharmacokinetics, and DDI data available in the NDAs approved by the FDA in 2013, using the University of Washington Drug Interaction Database, and to highlight significant findings. Among 27 NMEs approved, 22 (81%) were well characterized with regard to drug metabolism, transport, or organ impairment, in accordance with the FDA drug interaction guidance (2012) and were fully analyzed in this review. In vitro, a majority of the NMEs were found to be substrates or inhibitors/inducers of at least one drug metabolizing enzyme or transporter. However, in vivo, only half (n = 11) showed clinically relevant drug interactions, with most related to the NMEs as victim drugs and CYP3A being the most affected enzyme. As perpetrators, the overall effects for NMEs were much less pronounced, compared with when they served as victims. In addition, the pharmacokinetic evaluation in patients with hepatic or renal impairment provided useful information for further understanding of the drugs’ disposition.

Importance of multi-p450 Inhibition in Drug-Drug Interactions: Evaluation of Incidence, Inhibition Magnitude, and Prediction From in Vitro Data

Chem Res Toxicol. 2012 Nov 19;25(11; 2285-300. Published online 2013 Sep 27

Abstract

Drugs that are mainly cleared by a single enzyme are considered more sensitive to drug-drug interactions (DDIs) than drugs cleared by multiple pathways. However, whether this is true when a drug cleared by multiple pathways is coadministered with an inhibitor of multiple P450 enzymes (multi-P450 inhibition) is not known. Mathematically, simultaneous equipotent inhibition of two elimination pathways that each contribute half of the drug clearance is equal to equipotent inhibition of a single pathway that clears the drug. However, simultaneous strong or moderate inhibition of two pathways by a single inhibitor is perceived as an unlikely scenario. The aim of this study was (i) to identify P450 inhibitors currently in clinical use that can inhibit more than one clearance pathway of an object drug in vivo and (ii) to evaluate the magnitude and predictability of DDIs caused by these multi-P450 inhibitors. Multi-P450 inhibitors were identified using the Metabolism and Transport Drug Interaction Database. A total of 38 multi-P450 inhibitors, defined as inhibitors that increased the AUC or decreased the clearance of probes of two or more P450s, were identified. Seventeen (45%) multi-P450 inhibitors were strong inhibitors of at least one P450, and an additional 12 (32%) were moderate inhibitors of one or more P450s. Only one inhibitor (fluvoxamine) was a strong inhibitor of more than one enzyme. Fifteen of the multi-P450 inhibitors also inhibit drug transporters in vivo, but such data are lacking on many of the inhibitors. Inhibition of multiple P450 enzymes by a single inhibitor resulted in significant (>2-fold) clinical DDIs with drugs that are cleared by multiple pathways such as imipramine and diazepam, while strong P450 inhibitors resulted in only weak DDIs with these object drugs. The magnitude of the DDIs between multi-P450 inhibitors and diazepam, imipramine, and omeprazole could be predicted using in vitro data with similar accuracy as probe substrate studies with the same inhibitors. The results of this study suggest that inhibition of multiple clearance pathways in vivo is clinically relevant, and the risk of DDIs with object drugs may be best evaluated in studies using multi-P450 inhibitors.

A Useful Tool for Drug Interaction Evaluation: The University of Washington Metabolism and Transport Drug Interaction Database

Abstract

The Metabolism and Transport Drug Interaction Database (http://www.druginteractioninfo.org) is a web-based research and analysis tool developed in the Department of Pharmaceutics at the University of Washington. The database has the largest manually curated collection of data related to drug interactions in humans. The tool integrates information from the literature, public repositories, reference textbooks, guideline documents, product prescribing labels and clinical review sections of new drug approval (NDA) packages. The database’s easy-to-use web portal offers tools for visualisation, reporting and filtering of information. The database helps scientists to mine kinetics information for drug-metabolising enzymes and transporters, to assess the extent of in vivo drug interaction studies, as well as case reports for drugs, therapeutic proteins, food products and herbal derivatives. This review provides a brief description of the database organisation, its search functionalities and examples of use.