Abstract
Growing evidence suggests that drugs interact with diverse molecular targets mediating both therapeutic and toxic effects. Prediction of these complex interactions from chemical structures alone remains challenging, as compounds with different structures may possess similar toxicity profiles. In contrast, predictions based on systems-level measurements of drug effect may reveal pharmacologic similarities not evident from structure or known therapeutic indications. Here we utilized drug-induced transcriptional responses in the Connectivity Map (CMap) to discover such similarities among diverse antagonists of the human ether-à-go-go related (hERG) potassium channel, a common target of promiscuous inhibition by small molecules. Analysis of transcriptional profiles generated in three independent cell lines revealed clusters enriched for hERG inhibitors annotated using a database of experimental measurements (hERGcentral) and clinical indications. As a validation, we experimentally identified novel hERG inhibitors among the unannotated drugs in these enriched clusters, suggesting transcriptional responses may serve as predictive surrogates of cardiotoxicity complementing existing functional assays.
Highlights
While the single-target approach to drug discovery seeks “silver bullets” that selectively modulate disease-related proteins, recent work has emphasized the often promiscuous interactions of both marketed and candidate therapeutics [1,2,3]
The Connectivity Map (CMap) [9] is a collection of AffymetrixTM microarray gene expression profiles representing the responses of three cancer cell lines to small molecule treatments in comparison to dimethyl sulfoxide (DMSO) treated samples used as vehicle-treatment controls for these studies because most drugs are dissolved using DMSO as a solvent
In this study we identified commonalities in the transcriptional responses of structurally diverse human ether-à-go-go related (hERG) inhibitors, suggesting microarrays as a novel proxy measurement correlated with conduction of potassium currents by hERG and liability of channel block
Summary
While the single-target approach to drug discovery seeks “silver bullets” that selectively modulate disease-related proteins, recent work has emphasized the often promiscuous interactions of both marketed and candidate therapeutics [1,2,3]. While existing quantitative structure activity relationship (QSAR) methods have leveraged structural features of small molecules to predict toxicity, the difficulty of applying such techniques to chemicals that vary substantially from the model inputs has been described, in cases where toxicity is linked to the metabolic by-products of a compound [7,8]. Alternative descriptors, such as measurements of drug effects that probe the complex physiology of the cell, may potentially reveal commonalities aiding the prediction of toxicity independent of chemical structure as represented, for example, by conventional chemical fingerprints. We hypothesized that this data might be used to predict and verify novel toxicities, which we demonstrate by integrating the CMap with experimentally measured inhibition data for the human ether-à-go-go related (hERG) potassium channel and literature annotations to identify novel antagonists of this important anti-target of many drugs
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