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

Abstract

Summary 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 to prioritize 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.

Secondary pharmacology data to assess potential off-target activity of new drugs: a regulatory perspective

“…Although some of the more general considerations involved in target selection are discussed here, several recent publications are available that provide additional rationale for target selection under specific circumstances [3][4][5][6][7][8][9][10] .…”

Structure‐based protein‐protein interaction networks and drug design

“…A number of methods have used clustering of proteins into families [98,99], global structure similarity measurement [100,101] and interface similarity measurement [102][103][104][105] to predict "Off-Target" binding or to redesign drugs to enhance efficacy. Lounkine et al have used a similarity ensemble approach to predict off-targets, based on whether a molecule will bind to a target with similar chemical features to those of known targets [106]. They further linked the off-targets to adverse drug reactions (ADR) by using a guilt-byassociation pipeline that linked off-targets to the ADRs of drugs for which the off-targets were primary targets [106].…”

“…Lounkine et al have used a similarity ensemble approach to predict off-targets, based on whether a molecule will bind to a target with similar chemical features to those of known targets [106]. They further linked the off-targets to adverse drug reactions (ADR) by using a guilt-byassociation pipeline that linked off-targets to the ADRs of drugs for which the off-targets were primary targets [106]. They computationally screened 656 drugs approved for human use against 73 target proteins, and verified their predictions by either searching in proteinligand databases or performing binding and functional assays [106].…”

“…They further linked the off-targets to adverse drug reactions (ADR) by using a guilt-byassociation pipeline that linked off-targets to the ADRs of drugs for which the off-targets were primary targets [106]. They computationally screened 656 drugs approved for human use against 73 target proteins, and verified their predictions by either searching in proteinligand databases or performing binding and functional assays [106]. Moreover, they were able to construct a three-way Drug-Target-ADR network that could be an extremely useful starting point for future off-target and ADR predictions [106].…”

“…They computationally screened 656 drugs approved for human use against 73 target proteins, and verified their predictions by either searching in proteinligand databases or performing binding and functional assays [106]. Moreover, they were able to construct a three-way Drug-Target-ADR network that could be an extremely useful starting point for future off-target and ADR predictions [106]. The traditional approach to drug design is to focus on the molecular level, while the phenotypic outcomes in the clinical trial are measured at the organism level.…”