Metabolic Network Prediction of Drug Side Effects

Itay Shaked, Matthew A. Oberhardt, Nir Atias, Roded Sharan, Eytan Ruppin

Research output: Contribution to journalArticlepeer-review

Abstract

Summary 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.

Original languageEnglish
Pages (from-to)209-213
Number of pages5
JournalCell Systems
Volume2
Issue number3
DOIs
StatePublished - 23 Mar 2016

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