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

Autor: Wooten DJ; Department of Physics, Pennsylvania State University, University Park, PA, USA., Meyer CT; Program in Chemical and Physical Biology, Vanderbilt University School of Medicine, Nashville, TN, USA., Lubbock ALR; Department of Biochemistry, Vanderbilt University Nashville, Nashville, TN, USA., Quaranta V; Department of Biochemistry, Vanderbilt University Nashville, Nashville, TN, USA. vito.quaranta@vanderbilt.edu.; Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, TN, USA. vito.quaranta@vanderbilt.edu., Lopez CF; Department of Biochemistry, Vanderbilt University Nashville, Nashville, TN, USA. c.lopez@vanderbilt.edu.; Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, TN, USA. c.lopez@vanderbilt.edu.; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA. c.lopez@vanderbilt.edu.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2021 Jul 29; Vol. 12 (1), pp. 4607. Date of Electronic Publication: 2021 Jul 29.
DOI: 10.1038/s41467-021-24789-z
Abstrakt: 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.
(© 2021. The Author(s).)
Databáze: MEDLINE
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