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Ebola Virus Bayesian Machine Learning Models Enable New in Vitro Leads
[Image: see text] We have previously described the first Bayesian machine learning models from FDA-approved drug screens, for identifying compounds active against the Ebola virus (EBOV). These models led to the identification of three active molecules in vitro: tilorone, pyronaridine, and quinacrine...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2019
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6356859/ https://www.ncbi.nlm.nih.gov/pubmed/30729228 http://dx.doi.org/10.1021/acsomega.8b02948 |
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author | Anantpadma, Manu Lane, Thomas Zorn, Kimberley M. Lingerfelt, Mary A. Clark, Alex M. Freundlich, Joel S. Davey, Robert A. Madrid, Peter B. Ekins, Sean |
author_facet | Anantpadma, Manu Lane, Thomas Zorn, Kimberley M. Lingerfelt, Mary A. Clark, Alex M. Freundlich, Joel S. Davey, Robert A. Madrid, Peter B. Ekins, Sean |
author_sort | Anantpadma, Manu |
collection | PubMed |
description | [Image: see text] We have previously described the first Bayesian machine learning models from FDA-approved drug screens, for identifying compounds active against the Ebola virus (EBOV). These models led to the identification of three active molecules in vitro: tilorone, pyronaridine, and quinacrine. A follow-up study demonstrated that one of these compounds, tilorone, has 100% in vivo efficacy in mice infected with mouse-adapted EBOV at 30 mg/kg/day intraperitoneal. This suggested that we can learn from the published data on EBOV inhibition and use it to select new compounds for testing that are active in vivo. We used these previously built Bayesian machine learning EBOV models alongside our chemical insights for the selection of 12 molecules, absent from the training set, to test for in vitro EBOV inhibition. Nine molecules were directly selected using the model, and eight of these molecules possessed a promising in vitro activity (EC(50) < 15 μM). Three further compounds were selected for an in vitro evaluation because they were antimalarials, and compounds of this class like pyronaridine and quinacrine have previously been shown to inhibit EBOV. We identified the antimalarial drug arterolane (IC(50) = 4.53 μM) and the anticancer clinical candidate lucanthone (IC(50) = 3.27 μM) as novel compounds that have EBOV inhibitory activity in HeLa cells and generally lack cytotoxicity. This work provides further validation for using machine learning and medicinal chemistry expertize to prioritize compounds for testing in vitro prior to more costly in vivo tests. These studies provide further corroboration of this strategy and suggest that it can likely be applied to other pathogens in the future. |
format | Online Article Text |
id | pubmed-6356859 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-63568592019-02-04 Ebola Virus Bayesian Machine Learning Models Enable New in Vitro Leads Anantpadma, Manu Lane, Thomas Zorn, Kimberley M. Lingerfelt, Mary A. Clark, Alex M. Freundlich, Joel S. Davey, Robert A. Madrid, Peter B. Ekins, Sean ACS Omega [Image: see text] We have previously described the first Bayesian machine learning models from FDA-approved drug screens, for identifying compounds active against the Ebola virus (EBOV). These models led to the identification of three active molecules in vitro: tilorone, pyronaridine, and quinacrine. A follow-up study demonstrated that one of these compounds, tilorone, has 100% in vivo efficacy in mice infected with mouse-adapted EBOV at 30 mg/kg/day intraperitoneal. This suggested that we can learn from the published data on EBOV inhibition and use it to select new compounds for testing that are active in vivo. We used these previously built Bayesian machine learning EBOV models alongside our chemical insights for the selection of 12 molecules, absent from the training set, to test for in vitro EBOV inhibition. Nine molecules were directly selected using the model, and eight of these molecules possessed a promising in vitro activity (EC(50) < 15 μM). Three further compounds were selected for an in vitro evaluation because they were antimalarials, and compounds of this class like pyronaridine and quinacrine have previously been shown to inhibit EBOV. We identified the antimalarial drug arterolane (IC(50) = 4.53 μM) and the anticancer clinical candidate lucanthone (IC(50) = 3.27 μM) as novel compounds that have EBOV inhibitory activity in HeLa cells and generally lack cytotoxicity. This work provides further validation for using machine learning and medicinal chemistry expertize to prioritize compounds for testing in vitro prior to more costly in vivo tests. These studies provide further corroboration of this strategy and suggest that it can likely be applied to other pathogens in the future. American Chemical Society 2019-01-30 /pmc/articles/PMC6356859/ /pubmed/30729228 http://dx.doi.org/10.1021/acsomega.8b02948 Text en Copyright © 2019 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 | Anantpadma, Manu Lane, Thomas Zorn, Kimberley M. Lingerfelt, Mary A. Clark, Alex M. Freundlich, Joel S. Davey, Robert A. Madrid, Peter B. Ekins, Sean Ebola Virus Bayesian Machine Learning Models Enable New in Vitro Leads |
title | Ebola Virus Bayesian Machine Learning Models Enable
New in Vitro Leads |
title_full | Ebola Virus Bayesian Machine Learning Models Enable
New in Vitro Leads |
title_fullStr | Ebola Virus Bayesian Machine Learning Models Enable
New in Vitro Leads |
title_full_unstemmed | Ebola Virus Bayesian Machine Learning Models Enable
New in Vitro Leads |
title_short | Ebola Virus Bayesian Machine Learning Models Enable
New in Vitro Leads |
title_sort | ebola virus bayesian machine learning models enable
new in vitro leads |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6356859/ https://www.ncbi.nlm.nih.gov/pubmed/30729228 http://dx.doi.org/10.1021/acsomega.8b02948 |
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