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Modeling the Bioactivation and Subsequent Reactivity of Drugs.

Tyler B Hughes1, Noah Flynn1, Na Le Dang1

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This study developed a novel computational model to predict drug bioactivation, a key factor in adverse drug reactions. The model accurately identifies potential toxicity risks from drug metabolites, aiding in safer drug development.

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Area of Science:

  • Computational chemistry
  • Pharmacology
  • Toxicology

Background:

  • Electrophilically reactive drug metabolites are a major cause of adverse drug reactions, leading to severe health issues like liver injury and skin disorders.
  • Bioactivation, the process where metabolic enzymes convert drugs into reactive metabolites that bind to macromolecules, underlies these toxic effects.

Purpose of the Study:

  • To develop and validate a computational model that predicts drug bioactivation by jointly considering metabolic transformations and metabolite reactivity.
  • To assess the model's accuracy in identifying known bioactivation pathways and predicting bioactivation potential in withdrawn drugs.

Main Methods:

  • Synthesized models for four common bioactivation pathways: quinone formation, epoxidation, thiophene sulfur-oxidation, and nitroaromatic reduction.
  • Employed a feedforward neural network to integrate metabolism and reactivity predictions for a comprehensive bioactivation assessment.
  • Utilized the developed algorithm to analyze withdrawn drugs, identifying known and predicting novel bioactivation pathways.

Main Results:

  • The model achieved 89.98% AUC accuracy in predicting the correct bioactivation pathway among known bioactivated molecules.
  • It distinguished bioactivated from nonbioactivated molecules with 81.06% AUC accuracy.
  • The algorithm successfully identified known bioactivation pathways for alclofenac and benzbromarone and predicted high-probability pathways for safrazine, zimelidine, and astemizole.

Conclusions:

  • This novel bioactivation model, the first to integrate metabolism and reactivity, offers a powerful tool for early toxicity risk assessment of drug candidates.
  • The model can help identify potential toxicity risks that may be missed during preclinical drug trials, improving drug safety.
  • The XenoSite bioactivation model is publicly available for evaluating drug candidates and advancing pharmaceutical safety research.