In Vitro-to-In Vivo Extrapolation of Transporter Inhibition Data for Drugs Approved by the US Food and Drug Administration in 2018
The analysis focuses on inhibition transporter data from New Drug Applications approved by the FDA in 2018, involving 42 drugs. Key points include:
- In vitro-to-in vivo predictions were available for nine recommended transporters.
- 29 parent drugs and 16 metabolites inhibited at least one transporter, with P-gp and BCRP being the most inhibited.
- Oncology and anti-infective drugs were the most represented therapeutic areas, each accounting for 31%.
- 56% of drugs with predictions exceeding FDA cutoffs showed positive clinical interactions, though all were weak (AUCRs < 2).
- Seven out of nine interactions led to labeling recommendations.
- Over half of the drugs exceeding prediction cutoffs did not undergo further evaluations, indicating the usefulness of basic models as a preliminary assessment tool.
- Four drugs with predictions below cutoffs were evaluated clinically or via simulations, confirming no in vivo inhibition.
- Overall, drugs approved in 2018 had limited impact on drug transporters, with all victim AUCRs < 2.
The evaluation of the risk of transporter-based drug interactions is now an integral part of the drug development process, supporting the safe use of new treatments in the intended patient populations. A systematic, risk-based, integrated approach, including in vitro, in silico, and in vivo evaluations, has been recommended to evaluate transporter-mediated drug-drug interactions (DDIs) and recently has been updated by several regulatory agencies. As a perpetrator, in general, a new drug should be evaluated in vitro for its potential to inhibit the following transporters, which have been shown to play a major role in drug disposition and interact with drugs in clinical use: P-glycoprotein (P-gp), Breast Cancer Resistance Protein (BCRP), Organic Anion Transporting Polypeptide 1B1 (OATP1B1), OATP1B3, Organic Anion Transporter 1 (OAT1), OAT3, Organic Cation Transporter 2 (OCT2), Multidrug and Toxin Extrusion protein 1 (MATE1), and MATE2-K. Then following in vitro-to-in vivo model-based prediction using basic and/or mechanistic models like physiologically-based pharmacokinetic (PBPK) models, further clinical evaluations (drug interaction studies or in silico predictions) may be warranted based on likely concomitant medications that are known transporter substrates in the indicated patient populations. This report provides a summary of transporter-based inhibition data available in the 2018 US Food and Drug Administration (FDA) New Drug Applications (NDAs) and describes whether in vitro results are used to predict the risk of drug interactions and guide necessary clinical assessments.
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