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Characterizing ABC-Transporter Substrate-Likeness Using a Clean-Slate Genetic Background
Mutations in ATP Binding Cassette (ABC)-transporter genes can have major effects on the bioavailability and toxicity of the drugs that are ABC-transporter substrates. Consequently, methods to predict if a drug is an ABC-transporter substrate are useful for drug development. Such methods traditionall...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6494965/ https://www.ncbi.nlm.nih.gov/pubmed/31105571 http://dx.doi.org/10.3389/fphar.2019.00448 |
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author | Sokolov, Artem Ashenden, Stephanie Sahin, Nil Lewis, Richard Erdem, Nurdan Ozaltan, Elif Bender, Andreas Roth, Frederick P. Cokol, Murat |
author_facet | Sokolov, Artem Ashenden, Stephanie Sahin, Nil Lewis, Richard Erdem, Nurdan Ozaltan, Elif Bender, Andreas Roth, Frederick P. Cokol, Murat |
author_sort | Sokolov, Artem |
collection | PubMed |
description | Mutations in ATP Binding Cassette (ABC)-transporter genes can have major effects on the bioavailability and toxicity of the drugs that are ABC-transporter substrates. Consequently, methods to predict if a drug is an ABC-transporter substrate are useful for drug development. Such methods traditionally relied on literature curated collections of ABC-transporter dependent membrane transfer assays. Here, we used a single large-scale dataset of 376 drugs with relative efficacy on an engineered yeast strain with all ABC-transporter genes deleted (ABC-16), to explore the relationship between a drug’s chemical structure and ABC-transporter substrate-likeness. We represented a drug’s chemical structure by an array of substructure keys and explored several machine learning methods to predict the drug’s efficacy in an ABC-16 yeast strain. Gradient-Boosted Random Forest models outperformed all other methods with an AUC of 0.723. We prospectively validated the model using new experimental data and found significant agreement with predictions. Our analysis expands the previously reported chemical substructures associated with ABC-transporter substrates and provides an alternative means to investigate ABC-transporter substrate-likeness. |
format | Online Article Text |
id | pubmed-6494965 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-64949652019-05-17 Characterizing ABC-Transporter Substrate-Likeness Using a Clean-Slate Genetic Background Sokolov, Artem Ashenden, Stephanie Sahin, Nil Lewis, Richard Erdem, Nurdan Ozaltan, Elif Bender, Andreas Roth, Frederick P. Cokol, Murat Front Pharmacol Pharmacology Mutations in ATP Binding Cassette (ABC)-transporter genes can have major effects on the bioavailability and toxicity of the drugs that are ABC-transporter substrates. Consequently, methods to predict if a drug is an ABC-transporter substrate are useful for drug development. Such methods traditionally relied on literature curated collections of ABC-transporter dependent membrane transfer assays. Here, we used a single large-scale dataset of 376 drugs with relative efficacy on an engineered yeast strain with all ABC-transporter genes deleted (ABC-16), to explore the relationship between a drug’s chemical structure and ABC-transporter substrate-likeness. We represented a drug’s chemical structure by an array of substructure keys and explored several machine learning methods to predict the drug’s efficacy in an ABC-16 yeast strain. Gradient-Boosted Random Forest models outperformed all other methods with an AUC of 0.723. We prospectively validated the model using new experimental data and found significant agreement with predictions. Our analysis expands the previously reported chemical substructures associated with ABC-transporter substrates and provides an alternative means to investigate ABC-transporter substrate-likeness. Frontiers Media S.A. 2019-04-25 /pmc/articles/PMC6494965/ /pubmed/31105571 http://dx.doi.org/10.3389/fphar.2019.00448 Text en Copyright © 2019 Sokolov, Ashenden, Sahin, Lewis, Erdem, Ozaltan, Bender, Roth and Cokol. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Pharmacology Sokolov, Artem Ashenden, Stephanie Sahin, Nil Lewis, Richard Erdem, Nurdan Ozaltan, Elif Bender, Andreas Roth, Frederick P. Cokol, Murat Characterizing ABC-Transporter Substrate-Likeness Using a Clean-Slate Genetic Background |
title | Characterizing ABC-Transporter Substrate-Likeness Using a Clean-Slate Genetic Background |
title_full | Characterizing ABC-Transporter Substrate-Likeness Using a Clean-Slate Genetic Background |
title_fullStr | Characterizing ABC-Transporter Substrate-Likeness Using a Clean-Slate Genetic Background |
title_full_unstemmed | Characterizing ABC-Transporter Substrate-Likeness Using a Clean-Slate Genetic Background |
title_short | Characterizing ABC-Transporter Substrate-Likeness Using a Clean-Slate Genetic Background |
title_sort | characterizing abc-transporter substrate-likeness using a clean-slate genetic background |
topic | Pharmacology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6494965/ https://www.ncbi.nlm.nih.gov/pubmed/31105571 http://dx.doi.org/10.3389/fphar.2019.00448 |
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