Drug interaction with UDP-Glucuronosyltransferase (UGT) enzymes is a predictor of drug-induced liver injury

Author:

Olubamiwa AyoOluwa O.1ORCID,Liao Tsung-Jen12,Zhao Jinwen3,Dehanne Patrice4,Noban Catherine4,Angin Yeliz4,Barberan Olivier4,Chen Minjun1ORCID

Affiliation:

1. Division of Bioinformatics and Biostatistics, National Center for Toxicological Research (NCTR), U.S. Food and Drug Administration, Jefferson, Arkansas, USA

2. Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA

3. Department of Information Science, University of Arkansas at Little Rock, Arkansas, USA

4. Life Sciences, Elsevier B.V Radarweg, Amsterdam, Netherlands

Abstract

Background and Aims: DILI frequently contributes to the attrition of new drug candidates and is a common cause for the withdrawal of approved drugs from the market. Although some noncytochrome P450 (non-CYP) metabolism enzymes have been implicated in DILI development, their association with DILI outcomes has not been systematically evaluated. Approach and Results: In this study, we analyzed a large data set comprising 317 drugs and their interactions in vitro with 42 non-CYP enzymes as substrates, inducers, and/or inhibitors retrieved from historical regulatory documents using multivariate logistic regression. We examined how these in vitro drug-enzyme interactions are correlated with the drugs’ potential for DILI concern, as classified in the Liver Toxicity Knowledge Base database. Our study revealed that drugs that inhibit non-CYP enzymes are significantly associated with high DILI concern. Particularly, interaction with UDP-glucuronosyltransferases (UGT) enzymes is an important predictor of DILI outcomes. Further analysis indicated that only pure UGT inhibitors and dual substrate inhibitors, but not pure UGT substrates, are significantly associated with high DILI concern. Conclusions: Drug interactions with UGT enzymes may independently predict DILI, and their combined use with the rule-of-two model further improves overall predictive performance. These findings could expand the currently available tools for assessing the potential for DILI in humans.

Publisher

Ovid Technologies (Wolters Kluwer Health)

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