Faculty Opinions recommendation of Large-scale prediction and testing of drug activity on side-effect targets.
John A. Lowe III · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · 2012
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. PMID: 22722194 Funding information This work was supported by: NIGMS NIH HHS, United States Grant ID: GM93456 NIGMS NIH HHS, United States Grant ID: GM71896 NIGMS NIH HHS, United States Grant ID: R44 GM093456 NIGMS NIH HHS, United States Grant ID: R01 GM071896 NIGMS NIH HHS, United States Grant ID: R43 GM093456 NIA NIH HHS, United States Grant ID: P01 AG002132 NIA NIH HHS, United States Grant ID: AG002132 More Less keyboard_arrow_down