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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.
Afiliación
  • 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.
  • Lopez CF; Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, TN, USA. vito.quaranta@vanderbilt.edu.
Nat Commun ; 12(1): 4607, 2021 07 29.
Article en En | MEDLINE | ID: mdl-34326325
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.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Biología Computacional / Descubrimiento de Drogas Tipo de estudio: Clinical_trials / Guideline / Prognostic_studies Límite: Humans Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Biología Computacional / Descubrimiento de Drogas Tipo de estudio: Clinical_trials / Guideline / Prognostic_studies Límite: Humans Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos
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