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

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

doi:10.3390/metabo11060390

Cited by 3 publications

(3 citation statements)

References 71 publications

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“…Cytochrome P450s (CYPs) and peroxidases metabolically oxidize drugs into highly reactive electrophilic quinone species that are primarily responsible for idiosyncratic adverse drug reactions (IADRs). − IDARs are the most common adverse reactions of an approved drug and lead to severe mortality and morbidity in more than 70% of cases . CNN has been used for accurate predictions of quinone formation by modeling both one- and two-step quinone formation reactions .…”

Section: Deep Learning In Predictive Drug Toxicity Studiesmentioning

confidence: 99%

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Sinha¹,

Ghosh²,

Sil

2023

Chem. Res. Toxicol.

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Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug–drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.

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Section: Deep Learning In Predictive Drug Toxicity Studiesmentioning

confidence: 99%

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Sinha¹,

Ghosh²,

Sil

2023

Chem. Res. Toxicol.

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“…In modeling work on activation of certain 3,5-dimethylisoxazole compounds, for example (OXFBD02, 100 , Chart ), as bromodomain and extra-terminal (BET) inhibitors, the activation of the methyl groups played a prominent role since several GSH adducts on these were identified …”

Section: Selected Examples Of Methide and Sulfate Formationmentioning

confidence: 99%

“…In modeling work on activation of certain 3,5-dimethylisoxazole compounds, for example (OXFBD02,100, Chart 3), as bromodomain and extra-terminal (BET) inhibitors, the activation of the methyl groups played a prominent role since several GSH adducts on these were identified. 96 Flucloxacillin (Chart 4) is another 5-methylisoxazole. It has been reported to cause in vitro effects on gallbladder-derived biliary epithelial cells, associated with its 5′-hydroxy metabolite (144).…”

Section: Selected Examples Of Methide and Sulfate Formationmentioning

confidence: 99%

Non-innocuous Consequences of Metabolic Oxidation of Alkyls on Arenes

Claesson¹,

Parkes²

2022

J. Med. Chem.

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Reactive metabolite (RM) formation is widely accepted as playing a pivotal role in causing adverse idiosyncratic drug reactions, with most attention paid to drug-induced liver injury. Mechanisms of RM formation are determined by the drug’s properties in relation to human enzymes transforming the drug. This Perspective focuses on enzymatic oxidation of alkyl groups on aromatics leading to quinone methides and benzylic alcohol sulfates as RMs, a topic that has not received very much attention. Unlike previous overviews, we will include in our Perspective several fulvene-like methides such as 3-methyleneindole. We also speculate that a few older drugs may form non-reported methides of this class. In addition, we report a few guiding DFT calculations of changes in free energy on going from a benzylic alcohol to the corresponding methide. Particularly facile reactions of 2-aminothiazole-5-methanol and 4-aminobenzyl alcohol are noted.

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Bioactivation and reactivity research advances – 2022 year in review‡

Wang,

Argikar,

Cheruzel

et al. 2023

Drug Metabolism Reviews

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This year's review on bioactivation and reactivity began as a part of the annual review on biotransformation and bioactivation led by Cyrus Khojasteh (see references). Increased contributions from experts in the field led to the development of a stand alone edition for the first time this year focused specifically on bioactivation and reactivity. Our objective for this review is to highlight and share articles which we deem influential and significant regarding the development of covalent inhibitors, mechanisms of reactive metabolite formation, enzyme inactivation, and drug safety. Based on the selected articles, we created two sections: (1) reactivity and enzyme inactivation, and (2) bioactivation mechanisms and safety (Table 1). Several biotransformation experts have contributed to this effort from academic and industry settings.

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

Cited by 3 publications

References 71 publications

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Non-innocuous Consequences of Metabolic Oxidation of Alkyls on Arenes

Bioactivation and reactivity research advances – 2022 year in review‡

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