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Modeling the impact of drug interactions on therapeutic selectivity
Combination therapies that produce synergistic growth inhibition are widely sought after for the pharmacotherapy of many pathological conditions. Therapeutic selectivity, however, depends on the difference between potency on disease-causing cells and potency on non-target cell types that cause toxic...
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/PMC6110842/ https://www.ncbi.nlm.nih.gov/pubmed/30150706 http://dx.doi.org/10.1038/s41467-018-05954-3 |
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author | Weinstein, Zohar B. Kuru, Nurdan Kiriakov, Szilvia Palmer, Adam C. Khalil, Ahmad S. Clemons, Paul A. Zaman, Muhammad H. Roth, Frederick P. Cokol, Murat |
author_facet | Weinstein, Zohar B. Kuru, Nurdan Kiriakov, Szilvia Palmer, Adam C. Khalil, Ahmad S. Clemons, Paul A. Zaman, Muhammad H. Roth, Frederick P. Cokol, Murat |
author_sort | Weinstein, Zohar B. |
collection | PubMed |
description | Combination therapies that produce synergistic growth inhibition are widely sought after for the pharmacotherapy of many pathological conditions. Therapeutic selectivity, however, depends on the difference between potency on disease-causing cells and potency on non-target cell types that cause toxic side effects. Here, we examine a model system of antimicrobial compound combinations applied to two highly diverged yeast species. We find that even though the drug interactions correlate between the two species, cell-type-specific differences in drug interactions are common and can dramatically alter the selectivity of compounds when applied in combination vs. single-drug activity—enhancing, diminishing, or inverting therapeutic windows. This study identifies drug combinations with enhanced cell-type-selectivity with a range of interaction types, which we experimentally validate using multiplexed drug-interaction assays for heterogeneous cell cultures. This analysis presents a model framework for evaluating drug combinations with increased efficacy and selectivity against pathogens or tumors. |
format | Online Article Text |
id | pubmed-6110842 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-61108422018-08-29 Modeling the impact of drug interactions on therapeutic selectivity Weinstein, Zohar B. Kuru, Nurdan Kiriakov, Szilvia Palmer, Adam C. Khalil, Ahmad S. Clemons, Paul A. Zaman, Muhammad H. Roth, Frederick P. Cokol, Murat Nat Commun Article Combination therapies that produce synergistic growth inhibition are widely sought after for the pharmacotherapy of many pathological conditions. Therapeutic selectivity, however, depends on the difference between potency on disease-causing cells and potency on non-target cell types that cause toxic side effects. Here, we examine a model system of antimicrobial compound combinations applied to two highly diverged yeast species. We find that even though the drug interactions correlate between the two species, cell-type-specific differences in drug interactions are common and can dramatically alter the selectivity of compounds when applied in combination vs. single-drug activity—enhancing, diminishing, or inverting therapeutic windows. This study identifies drug combinations with enhanced cell-type-selectivity with a range of interaction types, which we experimentally validate using multiplexed drug-interaction assays for heterogeneous cell cultures. This analysis presents a model framework for evaluating drug combinations with increased efficacy and selectivity against pathogens or tumors. Nature Publishing Group UK 2018-08-27 /pmc/articles/PMC6110842/ /pubmed/30150706 http://dx.doi.org/10.1038/s41467-018-05954-3 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 Weinstein, Zohar B. Kuru, Nurdan Kiriakov, Szilvia Palmer, Adam C. Khalil, Ahmad S. Clemons, Paul A. Zaman, Muhammad H. Roth, Frederick P. Cokol, Murat Modeling the impact of drug interactions on therapeutic selectivity |
title | Modeling the impact of drug interactions on therapeutic selectivity |
title_full | Modeling the impact of drug interactions on therapeutic selectivity |
title_fullStr | Modeling the impact of drug interactions on therapeutic selectivity |
title_full_unstemmed | Modeling the impact of drug interactions on therapeutic selectivity |
title_short | Modeling the impact of drug interactions on therapeutic selectivity |
title_sort | modeling the impact of drug interactions on therapeutic selectivity |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6110842/ https://www.ncbi.nlm.nih.gov/pubmed/30150706 http://dx.doi.org/10.1038/s41467-018-05954-3 |
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