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Investigating the Behavior of Published PAINS Alerts Using a Pharmaceutical Company Data Set.

ABSTRACT: Biochemical assay interference is becoming increasingly recognized as a significant waste of resource in drug discovery, both in industry and academia. A seminal publication from Baell and Holloway raised the awareness of this issue, and they published a set of alerts to identify what they described as PAINS (pan-assay interference compounds). These alerts have been taken up by drug discovery groups, even though the original paper had a somewhat limited data set. Here, we have taken Lilly's far larger internal data set to assess the PAINS alerts on four criteria: promiscuity (over six assay formats including AlphaScreen), compound stability, cytotoxicity, and presence of a high Hill slope as a surrogate for non-1:1 protein-ligand binding. It was found that only three of the alerts show pan-assay promiscuity, and the alerts appear to encode primarily AlphaScreen promiscuous molecules. Although not enriching for pan-assay promiscuity, many of the alerts do encode molecules that are unstable, show cytotoxicity, and increase the prevalence of high Hill slopes.

SUBMITTER: Vidler LR

PROVIDER: S-EPMC6088356 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Investigating the Behavior of Published PAINS Alerts Using a Pharmaceutical Company Data Set.

Vidler Lewis R LR Watson Ian A IA Margolis Brandon J BJ Cummins David J DJ Brunavs Michael M

ACS medicinal chemistry letters 20180710 8

Biochemical assay interference is becoming increasingly recognized as a significant waste of resource in drug discovery, both in industry and academia. A seminal publication from Baell and Holloway raised the awareness of this issue, and they published a set of alerts to identify what they described as PAINS (pan-assay interference compounds). These alerts have been taken up by drug discovery groups, even though the original paper had a somewhat limited data set. Here, we have taken Lilly's far ...[more]

PMID: 30128069

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Investigating the Behavior of Published PAINS Alerts Using a Pharmaceutical Company Data Set.

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Investigating the Behavior of Published PAINS Alerts Using a Pharmaceutical Company Data Set.

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