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

By Eugen Lounkine, Michael J. Keiser, S. Whitebread, D. Mikhailov, J. Hamon, J. Jenkins, Paul Lavan, E. Weber, Allison K. Doak, S. Côté and 2 more. 2012

817 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Pharmacology

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Citations in the corpus

SWEETLEAD: an In Silico Database of Approved Drugs, Regulated Chemicals, and Herbal Isolates for Computer-Aided Drug Discovery (2013, neutral): “promising new therapeutic strategies[15-20].”
Artificial intelligence in drug discovery and development (2020, positive): “Web applications, such as ChemMapper and the similarity ensemble approach (SEA), are available for predicting drug–target interactions [43].”
Accurate and efficient target prediction using a potency-sensitive influence-relevance voter (2015, neutral): “[1–12]).”
SwissTargetPrediction: a web server for target prediction of bioactive small molecules (2014, background): “Chlorotrianisene is a known inhibitor of COX-1 (13), although the interaction is not present in ChEMBL.”
Analysis of chemical and biological features yields mechanistic insights into drug side effects. (2013, background): “However, unfortunately, only a few relationships between targets and SEs are well documented (Vedani et al., 2012), and despite remarkable recent progress (Lounkine et al., 2012), the mechanistic details for most undesired events remain unknown (Bauer-Mehren et al., 2012).”
ProTox-II: a webserver for the prediction of toxicity of chemicals (2018, methodology): “The prediction of toxicity targets is based on 15 different targets from the Novartis in vitro safety panels of the protein targets linked to adverse drug-reactions (15) as reported in our previous work (12).”