title: Metabolic Network Prediction of Drug Side Effects.
abstract: Drug side effects levy a massive cost on society through drug failures, morbidity, and mortality cases every year, and their early detection is critically important. Here, we describe the array of model-based phenotype predictors (AMPP), an approach that leverages medical informatics resources and a human genome-scale metabolic model (GSMM) to predict drug side effects. AMPP is substantially predictive (AUC > 0.7) for >70 drug side effects, including very serious ones such as interstitial nephritis and extrapyramidal disorders. We evaluate AMPP's predictive signal through cross-validation, comparison across multiple versions of a side effects database, and co-occurrence analysis of drug side effect associations in scientific abstracts (hypergeometric p value = 2.2e-40). AMPP outperforms a previous biochemical structure-based method in predicting metabolically based side effects (aggregate AUC = 0.65 versus 0.59). Importantly, AMPP enables the identification of key metabolic reactions and biomarkers that are predictive of specific side effects. Taken together, this work lays a foundation for future detection of metabolically grounded side effects during early stages of drug development.
authors.affiliation: Blavatnik School of Computer Sciences, Tel Aviv University, Tel Aviv 69978, Israel.
authors.forename: Itay
authors.initials: I
authors.lastname: Shaked
authors.affiliation: Blavatnik School of Computer Sciences, Tel Aviv University, Tel Aviv 69978, Israel; Department of Molecular Microbiology and Biotechnology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel; Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel; Department of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA.
authors.forename: Matthew A
authors.initials: MA
authors.lastname: Oberhardt
authors.affiliation: Blavatnik School of Computer Sciences, Tel Aviv University, Tel Aviv 69978, Israel.
authors.forename: Nir
authors.initials: N
authors.lastname: Atias
authors.affiliation: Blavatnik School of Computer Sciences, Tel Aviv University, Tel Aviv 69978, Israel.
authors.forename: Roded
authors.initials: R
authors.lastname: Sharan
authors.affiliation: Blavatnik School of Computer Sciences, Tel Aviv University, Tel Aviv 69978, Israel; Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel; Department of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA. Electronic address: eyruppin@gmail.com.
authors.forename: Eytan
authors.initials: E
authors.lastname: Ruppin
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems; D019540:Area Under Curve; D015415:Biomarkers; D016208:Databases, Factual; D004347:Drug Interactions; D058492:Drug Repositioning; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D060847:Metabolic Engineering; D053858:Metabolic Networks and Pathways
chemical_list: D015415:Biomarkers