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MuSyC is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery
Drug combination discovery depends on reliable synergy metrics but no consensus exists on the correct synergy criterion to characterize combined interactions. The fragmented state of the field confounds analysis, impedes reproducibility, and delays clinical translation of potential combination treat...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8322415/ https://www.ncbi.nlm.nih.gov/pubmed/34326325 http://dx.doi.org/10.1038/s41467-021-24789-z |
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author | Wooten, David J. Meyer, Christian T. Lubbock, Alexander L. R. Quaranta, Vito Lopez, Carlos F. |
author_facet | Wooten, David J. Meyer, Christian T. Lubbock, Alexander L. R. Quaranta, Vito Lopez, Carlos F. |
author_sort | Wooten, David J. |
collection | PubMed |
description | Drug combination discovery depends on reliable synergy metrics but no consensus exists on the correct synergy criterion to characterize combined interactions. The fragmented state of the field confounds analysis, impedes reproducibility, and delays clinical translation of potential combination treatments. Here we present a mass-action based formalism to quantify synergy. With this formalism, we clarify the relationship between the dominant drug synergy principles, and present a mapping of commonly used frameworks onto a unified synergy landscape. From this, we show how biases emerge due to intrinsic assumptions which hinder their broad applicability and impact the interpretation of synergy in discovery efforts. Specifically, we describe how traditional metrics mask consequential synergistic interactions, and contain biases dependent on the Hill-slope and maximal effect of single-drugs. We show how these biases systematically impact synergy classification in large combination screens, potentially misleading discovery efforts. Thus the proposed formalism can provide a consistent, unbiased interpretation of drug synergy, and accelerate the translatability of synergy studies. |
format | Online Article Text |
id | pubmed-8322415 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-83224152021-08-03 MuSyC is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery Wooten, David J. Meyer, Christian T. Lubbock, Alexander L. R. Quaranta, Vito Lopez, Carlos F. Nat Commun Article Drug combination discovery depends on reliable synergy metrics but no consensus exists on the correct synergy criterion to characterize combined interactions. The fragmented state of the field confounds analysis, impedes reproducibility, and delays clinical translation of potential combination treatments. Here we present a mass-action based formalism to quantify synergy. With this formalism, we clarify the relationship between the dominant drug synergy principles, and present a mapping of commonly used frameworks onto a unified synergy landscape. From this, we show how biases emerge due to intrinsic assumptions which hinder their broad applicability and impact the interpretation of synergy in discovery efforts. Specifically, we describe how traditional metrics mask consequential synergistic interactions, and contain biases dependent on the Hill-slope and maximal effect of single-drugs. We show how these biases systematically impact synergy classification in large combination screens, potentially misleading discovery efforts. Thus the proposed formalism can provide a consistent, unbiased interpretation of drug synergy, and accelerate the translatability of synergy studies. Nature Publishing Group UK 2021-07-29 /pmc/articles/PMC8322415/ /pubmed/34326325 http://dx.doi.org/10.1038/s41467-021-24789-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Wooten, David J. Meyer, Christian T. Lubbock, Alexander L. R. Quaranta, Vito Lopez, Carlos F. MuSyC is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery |
title | MuSyC is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery |
title_full | MuSyC is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery |
title_fullStr | MuSyC is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery |
title_full_unstemmed | MuSyC is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery |
title_short | MuSyC is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery |
title_sort | musyc is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8322415/ https://www.ncbi.nlm.nih.gov/pubmed/34326325 http://dx.doi.org/10.1038/s41467-021-24789-z |
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