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Published on: May 15, 2019
Bioactivation of Isoxazole-Containing Bromodomain and Extra-Terminal Domain (BET) Inhibitors
Noah R Flynn1, Michael D Ward1, Mary A Schleiff2
1Department of Pathology and Immunology, Washington University-St. Louis, St. Louis, MO 63130, USA.
Researchers developed a computational model to predict toxic metabolite formation in bromodomain and extra-terminal (BET) inhibitors. The model identified potential bioactivation pathways, guiding safer drug development.
Area of Science:
- Medicinal Chemistry
- Computational Toxicology
- Drug Discovery
Background:
- The 3,5-dimethylisoxazole motif is frequently used in bromodomain and extra-terminal (BET) inhibitors as an acetyl-lysine mimic.
- This motif may lead to bioactivation into toxic reactive metabolites, posing a safety concern for drug development.
Purpose of the Study:
- To develop and validate a computational approach for predicting bioactivation pathways of BET inhibitors.
- To assess the potential for toxic reactive metabolite formation in isoxazole-containing BET inhibitors.
Main Methods:
- Coupled deep neural models for predicting quinone formation, metabolite structures, and biomolecule reactivity.
- Prediction of bioactivation pathways for 32 BET inhibitors.
- Experimental validation of bioactivation for selected inhibitors (OXFBD02, OXFBD04, I-BET151).
Main Results:
- The computational model predicted that BET inhibitors are more likely to undergo bioactivation than non-bioactivated isoxazole-containing molecules.
- OXFBD inhibitors showed bioactivation into both traditional quinones and novel extended quinone-methides, with traditional quinones being dominant experimentally.
- Modeled bioactivations for I-BET151 predicted extended quinone-methides, which were not experimentally verified, indicating areas for model improvement.
Conclusions:
- The coupled modeling approach successfully predicted BET inhibitor bioactivations, including novel pathways.
- Experimental validation confirmed predicted bioactivation pathways, highlighting potential toxicity concerns for drug development.
- The study underscores the need for improved computational models to accurately predict bioactivation and ensure the safety of new drug leads.
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