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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical
Society
2018
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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. |
format | Online Article Text |
id | pubmed-6088356 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | American Chemical
Society |
record_format | MEDLINE/PubMed |
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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