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ORIGINAL RESEARCH article

Front. Pharmacol.
Sec. Drug Metabolism and Transport
Volume 15 - 2024 | doi: 10.3389/fphar.2024.1451164
This article is part of the Research Topic New Drugs and Future Challenges in Drug Metabolism and Transport View all 15 articles

Novel (Q)SAR Models for Prediction of Reversible and Time-Dependent Inhibition of Cytochrome P450 Enzymes Authors

Provisionally accepted
Sadegh Faramarzi Sadegh Faramarzi 1Arianna Bassan Arianna Bassan 2Kevin P Cross Kevin P Cross 2Xinning Yang Xinning Yang 1Glenn J Myatt Glenn J Myatt 2Donna Volpe Donna Volpe 1Lidiya Stavitskaya Lidiya Stavitskaya 1*
  • 1 United States Food and Drug Administration, Silver Spring, United States
  • 2 LeadScope (United States), Columbus, Ohio, United States

The final, formatted version of the article will be published soon.

    The 2020 FDA drug-drug interaction (DDI) guidance includes a consideration for metabolites with structural alerts for potential mechanism-based inhibition (MBI) and describes how this information may be used to determine whether in vitro studies need to be conducted to evaluate the inhibitory potential of a metabolite on CYP enzymes. To facilitate identification of structural alerts, an extensive literature search was performed and alerts for mechanism-based inhibition of cytochrome P450 enzymes (CYP) were collected. Furthermore, five quantitative structure-activity relationship (QSAR) models were developed to predict not only time-dependent inhibition of CYP3A4, an enzyme that metabolizes approximately 50% of all marketed drugs, but also reversible inhibition of 3A4, 2C9, 2C19 and 2D6. The non-proprietary training database for the QSAR models contains data for 10,129 chemicals harvested from FDA drug approval packages and published literature. The cross-validation performance statistics for the new CYP QSAR models range from 78% to 84% sensitivity and 79% to 84% normalized negative predictivity. Additionally, the performance of the newly developed QSAR models was assessed using external validation sets. Overall performance statistics showed up to 75% in sensitivity and up to 80% in normalized negative predictivity. The newly developed models will provide a faster and more effective evaluation of potential drug-drug interaction caused by metabolites.

    Keywords: CYP = cytochrome P450, Reversible inhibition, Time dependent inhibition, QSAR, SAR, computational model

    Received: 18 Jun 2024; Accepted: 27 Dec 2024.

    Copyright: © 2024 Faramarzi, Bassan, Cross, Yang, Myatt, Volpe and Stavitskaya. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Lidiya Stavitskaya, United States Food and Drug Administration, Silver Spring, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.