Challenges with the precise prediction of ABC-transporter interactions for improved drug discovery

ABSTRACT

Introduction: Given that membrane efflux transporters can influence a drug’s pharmacokinetics, efficacy and safety, identifying potential substrates and inhibitors of these transporters is a critical element in the drug discovery and development process. Additionally, it is important to predict the inhibition potential of new drugs to avoid clinically significant drug interactions. The goal of preclinical studies is to characterize a new drug as a substrate or inhibitor of efflux transporters.

Areas covered: This article reviews preclinical systems that are routinely utilized to determine whether a new drug is substrate or inhibitor of efflux transporters including in silico models, in vitro membrane and cell assays, and animal models. Also included is an examination of studies comparing in vitro inhibition data to clinical drug interaction outcomes.

Expert opinion: While a number of models are employed to classify a drug as an efflux substrate or inhibitor, there are challenges in predicting clinical drug interactions. Improvements could be made in these predictions through a tier approach to classify new drugs, validation of preclinical assays, and refinement of threshold criteria for clinical interaction studies.

KEYWORDS:

• Efflux
• transporters
• drug interactions
• predictions

Article highlights

• Efflux transporters are involved in the absorption, distribution and elimination of drugs making them targets of potential drug interactions.

• In silico, in vitro and animal preclinical models are available to determine whether a new drug is a substrate or inhibitor of the efflux transporters each with their own advantages and limitations.

• Several studies have examined the use of inhibition data from in vitro models to predict clinical P-gp interactions with digoxin as the probe substrate.

• These studies highlight the challenges in prediction of in vivo outcomes from in vitro data using basic models.

• Improvements in predictions may be accomplished by optimizing and validating the in vitro assays that generate IC₅₀ values with additional data and probe substrates and applying a tier approach using basic, mechanistic and dynamic models throughout drug discovery and development.

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Acknowledgments

The findings and conclusions in this article have not been formally disseminated by the Food and Drug Administration and should not be construed to represent any Agency endorsement, determination or policy.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer Disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This manuscript was funded by the Food and Drug Administration.