Large-scale prediction and testing of drug activity on side-effect targets

Eugen Lounkine, Michael J. Keiser, Steven Whitebread, Dmitri Mikhailov, Jacques Hamon, Jeremy L. Jenkins (), Paul Lavan, Eckhard Weber, Allison K. Doak, Serge Côté, Brian K. Shoichet () and Laszlo Urban ()
Additional contact information
Eugen Lounkine: Novartis Institutes for Biomedical Research
Michael J. Keiser: SeaChange Pharmaceuticals Inc, 409 Illinois Street
Steven Whitebread: Novartis Institutes for Biomedical Research
Dmitri Mikhailov: Novartis Institutes for Biomedical Research
Jacques Hamon: Novartis Institutes for Biomedical Research
Jeremy L. Jenkins: Novartis Institutes for Biomedical Research
Paul Lavan: Novartis Institutes for Biomedical Research
Eckhard Weber: Novartis Institutes for Biomedical Research
Allison K. Doak: University of California, San Francisco, 1700 4th Street, Byers Hall Suite 508D, California 94158-2550, USA
Serge Côté: Novartis Institutes for Biomedical Research
Brian K. Shoichet: University of California, San Francisco, 1700 4th Street, Byers Hall Suite 508D, California 94158-2550, USA
Laszlo Urban: Novartis Institutes for Biomedical Research

Nature, 2012, vol. 486, issue 7403, 361-367

Abstract: Abstract Discovering the unintended ‘off-targets’ that predict adverse drug reactions is daunting by empirical methods alone. Drugs can act on several protein targets, some of which can be unrelated by conventional molecular metrics, and hundreds of proteins have been implicated in side effects. Here we use 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 to prioritize those new off-targets that explained side effects better than any known target of a given drug, creating a drug–target–adverse drug reaction network. Among these new associations was the prediction that the abdominal pain side effect of the synthetic oestrogen chlorotrianisene was mediated through its newly discovered inhibition of the enzyme cyclooxygenase-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.

Date: 2012
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DOI: 10.1038/nature11159

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