Modeling the Bioactivation and Subsequent Reactivity of Drugs
| Autor: | Tyler B. Hughes, Na Le Dang, S. Joshua Swamidass, Noah R. Flynn |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Drug
media_common.quotation_subject Metabolite Thiophenes 010501 environmental sciences Toxicology Models Biological 01 natural sciences Article 03 medical and health sciences Benzbromarone chemistry.chemical_compound medicine Humans Nitrobenzenes 030304 developmental biology 0105 earth and related environmental sciences media_common chemistry.chemical_classification 0303 health sciences Molecular Structure Quinones General Medicine Metabolism Alclofenac Enzyme Biochemistry chemistry Astemizole Epoxy Compounds Oxidation-Reduction Sulfur Drug metabolism medicine.drug |
| Zdroj: | Chem Res Toxicol |
| ISSN: | 1520-5010 0893-228X |
| Popis: | Electrophilically reactive drug metabolites are implicated in many adverse drug reactions. In this mechanism—termed bioactivation—metabolic enzymes convert drugs into reactive metabolites that often conjugate to nucleophilic sites within biological macromolecules like proteins. Toxic metabolite-product adducts induce severe immune responses that can cause sometimes fatal disorders, most commonly in the form of liver injury, blood dyscrasia, or the dermatologic conditions toxic epidermal necrolysis and Stevens–Johnson syndrome. This study models four of the most common metabolic transformations that result in bioactivation: quinone formation, epoxidation, thiophene sulfur-oxidation, and nitroaromatic reduction, by synthesizing models of metabolism and reactivity. First, the metabolism models predict the formation probabilities of all possible metabolites among the pathways studied. Second, the exact structures of these metabolites are enumerated. Third, using these structures, the reactivity model predicts the reactivity of each metabolite. Finally, a feedfoward neural network converts the metabolism and reactivity predictions to a bioactivation prediction for each possible metabolite. These bioactivation predictions represent the joint probability that a metabolite forms and that this metabolite subsequently conjugates to protein or glutathione. Among molecules bioactivated by these pathways, we predicted the correct pathway with an AUC accuracy of 89.98%. Furthermore, the model predicts whether molecules will be bioactivated, distinguishing bioactivated and nonbioactivated molecules with 81.06% AUC. We applied this algorithm to withdrawn drugs. The known bioactivation pathways of alclofenac and benzbromarone were identified by the algorithm, and high probability bioactivation pathways not yet confirmed were identified for safrazine, zimelidine, and astemizole. This bioactivation model—the first of its kind that jointly considers both metabolism and reactivity—enables drug candidates to be quickly evaluated for a toxicity risk that often evades detection during preclinical trials. The XenoSite bioactivation model is available at http://swami.wustl.edu/xenosite/p/bioactivation. |
| Databáze: | OpenAIRE |
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