Premium
Modeling the Metabolism and Subsequent Reactivity of Drugs
Author(s) -
Swamidass S. Joshua,
Matlock Matthew,
Le Dang Na,
Hughes Tyler,
Barnette Dustyn A,
Miller Grover P.
Publication year - 2017
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.31.1_supplement.822.10
Subject(s) - drug metabolism , drug , drug reaction , drug discovery , computational biology , reactive intermediate , drug development , pharmacology , medicine , chemistry , computer science , bioinformatics , biochemistry , biology , catalysis
Adverse drug reactions (ADRs) are dangerous and expensive. Idiosyncratic ADRs, especially the difficult to predict, rare and severe hypersensitivity‐driven ADRs, are the leading cause of medicine withdrawal and termination of clinical development. At the same time, a large proportion of drugs are not associated with hypersensitivity driven ADRs, offering hope that new medicines could avoid them entirely with reliable predictors of risk. Hypersensitivity driven ADRs are caused by the formation of chemically reactive metabolites by metabolic enzymes. These reactive metabolites covalently attach to proteins to become immunogenic and provoke an ADR. Unfortunately, current computational and experimental approaches do not reliably identify drug candidates that form reactive metabolites. These approaches are limited because they inadequately model metabolism, which can both render toxic molecules safe and safe molecules toxic. Mathematical models of both metabolism and reactivity overcome this limitation. The models are constructed using machine‐learning algorithms that quantitatively summarize the knowledge from thousands of published studies. This approach more accurately models whether metabolism renders drugs toxic or safe. For example, it predicts the mechanisms of drug toxicity much more accurately than structural alerts. Applying this modeling approach to the literature, it identifies errors and corrects errors in literature‐reported bioactivation pathways, and can identify bioactivation events that are missed by standard experimental approaches. This demonstrates how modeling can be used alongside biochemical studies to more reliably determine how and when reactive metabolites are generated by drug candidates. Support or Funding Information Research reported in this publication was supported by the National Library Of Medicine of the National Institutes of Health under Award Numbers R01LM012222 and R01LM012482. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Computations were performed using the facilities of the Washington University Center for High Performance Computing, which were partially funded by NIH grants nos. 1S10RR022984‐01A1 and 1S10OD018091‐01. We also thank both the Department of Immunology and Pathology at the Washington University School of Medicine and the Washington University Center for Biological Systems Engineering for their generous support of this work.