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Large Scale Prediction and Testing of Drug Activity on Side-Effect Targets

Lounkine, Eugen and Keiser, Michael J and Whitebread, Steven and Mikhailov, Dmitri and Hamon, Jacques and Jenkins, Jeremy and Lavan, Paul and Weber, Eckhard and Doak, Allison K and Cote, Serge and Shoichet, Brian K and Urban, Laszlo (2012) Large Scale Prediction and Testing of Drug Activity on Side-Effect Targets. Nature, 486 (7403). pp. 361-367. ISSN 0028-0836

Abstract

Discovering the unintended “off-targets” that predict adverse drug reactions (ADRs) is daunting by empirical methods alone. Drugs can act on multiple protein targets, some of which can be unrelated by traditional molecular metrics, and hundreds of proteins have been implicated in side effects. We therefore explored a computational strategy to predict the activity of 656 marketed drugs on 73 unintended “side effect” targets. Approximately half of the predictions were confirmed, either from proprietary databases unknown to the method or by new experimental assays. Affinities for these new off-targets ranged from 1 nM to 30 µM. To explore relevance, we developed an association metric that prioritized those new off-targets that explained side effects better than any known target of a given drug, creating a Drug-Target-ADR network. Among these new associations was the prediction that the abdominal pain side effect of the synthetic estrogen chlorotrianisene was mediated through its newly discovered inhibition of the enzyme COX-1. The clinical relevance of this inhibition was borne-out in whole human blood platelet aggregation assays. This approach may have wide application to de-risking toxicological liabilities in drug discovery.

Item Type: Article
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Date Deposited: 13 Oct 2015 13:15
Last Modified: 13 Oct 2015 13:15
URI: https://oak.novartis.com/id/eprint/5675

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