Bioactivation of Isoxazole-Containing Bromodomain and Extra-Terminal Domain (BET) Inhibitors.

Noah R Flynn,Corentine M C Laurin,Michael D Ward,Stuart J Conway,Mary A Schleiff,Grover P Miller,S Joshua Swamidass,Rohit Farmer,Gunnar Boysen

doi:10.3390/metabo11060390

Abstract

The 3,5-dimethylisoxazole motif has become a useful and popular acetyl-lysine mimic employed in isoxazole-containing bromodomain and extra-terminal (BET) inhibitors but may introduce the potential for bioactivations into toxic reactive metabolites. As a test, we coupled deep neural models for quinone formation, metabolite structures, and biomolecule reactivity to predict bioactivation pathways for 32 BET inhibitors and validate the bioactivation of select inhibitors experimentally. Based on model predictions, inhibitors were more likely to undergo bioactivation than reported non-bioactivated molecules containing isoxazoles. The model outputs varied with substituents indicating the ability to scale their impact on bioactivation. We selected OXFBD02, OXFBD04, and I-BET151 for more in-depth analysis. OXFBD’s bioactivations were evenly split between traditional quinones and novel extended quinone-methides involving the isoxazole yet strongly favored the latter quinones. Subsequent experimental studies confirmed the formation of both types of quinones for OXFBD molecules, yet traditional quinones were the dominant reactive metabolites. Modeled I-BET151 bioactivations led to extended quinone-methides, which were not verified experimentally. The differences in observed and predicted bioactivations reflected the need to improve overall bioactivation scaling. Nevertheless, our coupled modeling approach predicted BET inhibitor bioactivations including novel extended quinone methides, and we experimentally verified those pathways highlighting potential concerns for toxicity in the development of these new drug leads.

Highlights

IntroductionIntroduction published maps and institutional affilTargeted cancer therapeutics boast superior health outcomes relative to traditional chemotherapy drugs; hepatotoxicity poses a major clinical concern for patients undergoing treatment with those drugs [1,2,3,4]
Introduction published maps and institutional affilTargeted cancer therapeutics boast superior health outcomes relative to traditional chemotherapy drugs; hepatotoxicity poses a major clinical concern for patients undergoing treatment with those drugs [1,2,3,4]
We evaluated the model ability to discriminate between the bioactivated and non-bioactivated isoxazole-containing molecules

Summary

Introduction

Introduction published maps and institutional affilTargeted cancer therapeutics boast superior health outcomes relative to traditional chemotherapy drugs; hepatotoxicity poses a major clinical concern for patients undergoing treatment with those drugs [1,2,3,4]. Assessment of the drug bioactivation risk traditionally relies on experimental studies incurring high costs in time, effort, and resources as well as expertise. To mitigate this expense, we have developed metabolism and bioactivation models to increase the efficiency of drug development. We designed deep neural models to predict the formation of specific reactive metabolites including epoxides [8] and quinone species [9], and the bioactivation of structural alerts such as furans, phenols, nitroaromatics, and thiophenes [10]. The quinone model is capable of predicting the formation of other reactive metabolites within the broader class of conjugated electrophiles, despite its name.

Methods

Results

Discussion

Conclusion