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

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.

Selenium Induces Pancreatic Cancer Cell Death Alone and in Combination with Gemcitabine

“…Drug combination synergy was quantified using two different reference models: MuSyC [ 32 , 33 ] and HSA [ 34 ]. Calculations were performed using the synergy Python package (v0.5.1) [ 35 ].…”

“…For synergistic efficacy, beta < 0 indicates antagonism, while beta > 0 indicates synergy. Synergistic efficacy is generally more important at high doses, while synergistic potency is generally more important at intermediate doses [ 33 ]. HSA synergy was calculated using the raw combination response data, compared to the mean monotherapy response value for each drug.…”

“…The interaction of drugs in a combination is commonly quantified using a synergy reference model [ 30 , 31 ]. A recently developed synergy model, multi-dimensional synergy of combinations (MuSyC), distinguishes distinct types of synergy, allowing for prioritization of, e.g., synergistic efficacy or synergistic potency [ 32 , 33 ]. The MuSyC model has many parameters, which, however, may not be well-constrained by available data.…”

Organoids as a Systems Platform for SCLC Brain Metastasis

“…Systems-level knowledge can come to the rescue by identifying sensible nodes that could anchor large combinatorial screens. In this respect, the algorithm termed Multidimensional Synergy of Combinations (MuSyC) accurately quantifies drug synergy from high-throughput screening of drug combinations ( 42 – 44 ). MuSyC fulfills an unmet need in the field – as previous methods to measure drug synergy are often biased and fail to separate drug potency from drug efficacy.…”

Metabolomics-based phenotypic screens for evaluation of drug synergy via direct-infusion mass spectrometry

“…Typical preclinical drug combination studies rely on the application of dose-response methods for synergy quantification, including Loewe (1953) , Bliss independence ( Bliss, 1939 ), or Chou-Talalay ( Chou, 2010 ) models, that utilize single biochemical readouts, such as in vitro cell viability assays ( Folkesson et al., 2020 ; Tomska et al., 2018 ; Wood et al., 2017 ). While the Bliss independence and Loewe models allow for the assessment of hundreds to thousands of drug combinations and can easily be applied to multiple cell lines simultaneously, they can often produce inconsistent results ( Meyer et al., 2020 ), and thus more recent attempts have been made to unify the drug synergy principles ( Wooten et al., 2021 ). The Chou-Talalay model has been considered the gold standard for evaluating synergism, but due to its requirement of a large number of data points to assess a wide range of concentrations between only two drugs, it is not applicable to high-throughput platforms ( Amzallag et al., 2019 ).…”