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Cited by 97Computational Drug Discovery Methodsthermodynamics and calorimetric analysesInterdisciplinary Research and Collaboration

MuSyC is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery

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Wooten, David J., Meyer, Christian T., Lubbock, Alexander L. R., Quaranta, Vito, Lopez, Carlos F.·2021·Nature Communications
DOI 10.1038/s41467-021-24789-zPMC PMC8322415
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Abstract

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.

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  • The MuSyC framework unifies a variety of synergy metrics and separates potency synergy and efficacy synergy such that the two synergies are objective metrics purely determined by the interactions of two drugs, and are neither dose nor time dependent.

    “The MuSyC framework unifies a variety of synergy metrics and separates potency synergy and efficacy synergy such that the two synergies are objective metrics purely determined by the interactions of two drugs, and are neither dose nor time dependent (13).”

    From Statistical Assessment of Drug Synergy from <i>In Vivo</i> Combination Studies Using Mouse Tumor Models (2023) · cites this paper as [13]

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Sorted by citations

Authors
Wooten, D. J., Meyer, C. T., Lubbock, A. L. R., Quaranta, V., & Lopez, C. F.
Affiliations
  • Pennsylvania State University (US)
  • Vanderbilt University (US)
Corresponding author
  • Quaranta, Vito
  • Lopez, Carlos F.
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2021-07-29
Volume
12
Issue
1
Pages
4607-4607
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-021-24789-z
PMC
PMC8322415
OpenAlex
W3183139760
Cited by
97
References
3
Citations by year
2026: 21 · 2025: 14 · 2024: 20 · 2023: 20 · 2022: 19 · 2021: 2 · 2020: 1
FWCI
8.04
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • U.S. Department of Health & Human Services | NIH | National Cancer Institute (U01-CA215845, U54-CA217450)
  • NSF | BIO | Division of Molecular and Cellular Biosciences (1411482)
  • U.S. Department of Health & Human Services | National Institutes of Health (R01-186193)
  • National Science Foundation (GRFP-1445197)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Chemistry › Physical and Theoretical Chemistry › thermodynamics and calorimetric analyses
  • Social Sciences › Decision Sciences › Information Systems and Management › Interdisciplinary Research and Collaboration
Keywords
drug synergy, hillslopes, reproducibility
MeSH terms
Drug Combinations, Drug Synergism, Humans, Models, Theoretical, Software, Guidelines as Topic, Computational Biology, Benchmarking, Consensus, Drug Discovery

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