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

[Image: see text] 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 w...

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Autores principales: Vidler, Lewis R., Watson, Ian A., Margolis, Brandon J., Cummins, David J., Brunavs, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2018
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6088356/
https://www.ncbi.nlm.nih.gov/pubmed/30128069
http://dx.doi.org/10.1021/acsmedchemlett.8b00097
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author Vidler, Lewis R.
Watson, Ian A.
Margolis, Brandon J.
Cummins, David J.
Brunavs, Michael
author_facet Vidler, Lewis R.
Watson, Ian A.
Margolis, Brandon J.
Cummins, David J.
Brunavs, Michael
author_sort Vidler, Lewis R.
collection PubMed
description [Image: see text] 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.
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spelling pubmed-60883562018-08-20 Investigating the Behavior of Published PAINS Alerts Using a Pharmaceutical Company Data Set Vidler, Lewis R. Watson, Ian A. Margolis, Brandon J. Cummins, David J. Brunavs, Michael ACS Med Chem Lett [Image: see text] 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. American Chemical Society 2018-07-10 /pmc/articles/PMC6088356/ /pubmed/30128069 http://dx.doi.org/10.1021/acsmedchemlett.8b00097 Text en Copyright © 2018 American Chemical Society This is an open access article published under an ACS AuthorChoice License (http://pubs.acs.org/page/policy/authorchoice_termsofuse.html) , which permits copying and redistribution of the article or any adaptations for non-commercial purposes.
spellingShingle Vidler, Lewis R.
Watson, Ian A.
Margolis, Brandon J.
Cummins, David J.
Brunavs, Michael
Investigating the Behavior of Published PAINS Alerts Using a Pharmaceutical Company Data Set
title Investigating the Behavior of Published PAINS Alerts Using a Pharmaceutical Company Data Set
title_full Investigating the Behavior of Published PAINS Alerts Using a Pharmaceutical Company Data Set
title_fullStr Investigating the Behavior of Published PAINS Alerts Using a Pharmaceutical Company Data Set
title_full_unstemmed Investigating the Behavior of Published PAINS Alerts Using a Pharmaceutical Company Data Set
title_short Investigating the Behavior of Published PAINS Alerts Using a Pharmaceutical Company Data Set
title_sort investigating the behavior of published pains alerts using a pharmaceutical company data set
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6088356/
https://www.ncbi.nlm.nih.gov/pubmed/30128069
http://dx.doi.org/10.1021/acsmedchemlett.8b00097
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