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Predicting resistance of clinical Abl mutations to targeted kinase inhibitors using alchemical free-energy calculations
The therapeutic effect of targeted kinase inhibitors can be significantly reduced by intrinsic or acquired resistance mutations that modulate the affinity of the drug for the kinase. In cancer, the majority of missense mutations are rare, making it difficult to predict their impact on inhibitor affi...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6110136/ https://www.ncbi.nlm.nih.gov/pubmed/30159405 http://dx.doi.org/10.1038/s42003-018-0075-x |
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author | Hauser, Kevin Negron, Christopher Albanese, Steven K. Ray, Soumya Steinbrecher, Thomas Abel, Robert Chodera, John D. Wang, Lingle |
author_facet | Hauser, Kevin Negron, Christopher Albanese, Steven K. Ray, Soumya Steinbrecher, Thomas Abel, Robert Chodera, John D. Wang, Lingle |
author_sort | Hauser, Kevin |
collection | PubMed |
description | The therapeutic effect of targeted kinase inhibitors can be significantly reduced by intrinsic or acquired resistance mutations that modulate the affinity of the drug for the kinase. In cancer, the majority of missense mutations are rare, making it difficult to predict their impact on inhibitor affinity. We examine the potential for alchemical free-energy calculations to predict how kinase mutations modulate inhibitor affinities to Abl, a major target in chronic myelogenous leukemia (CML). These calculations have useful accuracy in predicting resistance for eight FDA-approved kinase inhibitors across 144 clinically identified point mutations, with a root mean square error in binding free-energy changes of [Formula: see text] kcal mol(−1) (95% confidence interval) and correctly classifying mutations as resistant or susceptible with [Formula: see text] % accuracy. This benchmark establishes the potential for physical modeling to collaboratively support the assessment and anticipation of patient mutations to affect drug potency in clinical applications. |
format | Online Article Text |
id | pubmed-6110136 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-61101362018-08-27 Predicting resistance of clinical Abl mutations to targeted kinase inhibitors using alchemical free-energy calculations Hauser, Kevin Negron, Christopher Albanese, Steven K. Ray, Soumya Steinbrecher, Thomas Abel, Robert Chodera, John D. Wang, Lingle Commun Biol Article The therapeutic effect of targeted kinase inhibitors can be significantly reduced by intrinsic or acquired resistance mutations that modulate the affinity of the drug for the kinase. In cancer, the majority of missense mutations are rare, making it difficult to predict their impact on inhibitor affinity. We examine the potential for alchemical free-energy calculations to predict how kinase mutations modulate inhibitor affinities to Abl, a major target in chronic myelogenous leukemia (CML). These calculations have useful accuracy in predicting resistance for eight FDA-approved kinase inhibitors across 144 clinically identified point mutations, with a root mean square error in binding free-energy changes of [Formula: see text] kcal mol(−1) (95% confidence interval) and correctly classifying mutations as resistant or susceptible with [Formula: see text] % accuracy. This benchmark establishes the potential for physical modeling to collaboratively support the assessment and anticipation of patient mutations to affect drug potency in clinical applications. Nature Publishing Group UK 2018-06-13 /pmc/articles/PMC6110136/ /pubmed/30159405 http://dx.doi.org/10.1038/s42003-018-0075-x Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Hauser, Kevin Negron, Christopher Albanese, Steven K. Ray, Soumya Steinbrecher, Thomas Abel, Robert Chodera, John D. Wang, Lingle Predicting resistance of clinical Abl mutations to targeted kinase inhibitors using alchemical free-energy calculations |
title | Predicting resistance of clinical Abl mutations to targeted kinase inhibitors using alchemical free-energy calculations |
title_full | Predicting resistance of clinical Abl mutations to targeted kinase inhibitors using alchemical free-energy calculations |
title_fullStr | Predicting resistance of clinical Abl mutations to targeted kinase inhibitors using alchemical free-energy calculations |
title_full_unstemmed | Predicting resistance of clinical Abl mutations to targeted kinase inhibitors using alchemical free-energy calculations |
title_short | Predicting resistance of clinical Abl mutations to targeted kinase inhibitors using alchemical free-energy calculations |
title_sort | predicting resistance of clinical abl mutations to targeted kinase inhibitors using alchemical free-energy calculations |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6110136/ https://www.ncbi.nlm.nih.gov/pubmed/30159405 http://dx.doi.org/10.1038/s42003-018-0075-x |
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