SynergyFinder Plus: Toward Better Interpretation and Annotation of Drug Combination Screening Datasets

Abstract

Combinatorial therapies have been recently proposed to improve the efficacy of anticancer treatment. The SynergyFinder R package is a software used to analyze pre-clinical drug combination datasets. Here, we report the major updates to the SynergyFinder R package for improved interpretation and annotation of drug combination screening results. Unlike the existing implementations, the updated SynergyFinder R package includes five main innovations. 1) We extend the mathematical models to higher-order drug combination data analysis and implement dimension reduction techniques for visualizing the synergy landscape. 2) We provide a statistical analysis of drug combination synergy and sensitivity with confidence intervals and P values. 3) We incorporate a synergy barometer to harmonize multiple synergy scoring methods to provide a consensus metric for synergy. 4) We evaluate drug combination synergy and sensitivity to provide an unbiased interpretation of the clinical potential. 5) We enable fast annotation of drugs and cell lines, including their chemical and target information. These annotations will improve the interpretation of the mechanisms of action of drug combinations. To facilitate the use of the R package within the drug discovery community, we also provide a web server at www.synergyfinderplus.org as a user-friendly interface to enable a more flexible and versatile analysis of drug combination data.

A MYC inhibitor selectively alters the MYC and MAX cistromes and modulates the epigenomic landscape to regulate target gene expression

“…The ENZ concentrations used in this study reflect this behavior, as can be seen in the percent viability matrices. Drug combination data were assessed using the SynergyFinder package using the Bliss independence model ( 38 ), which converts percent viability values to fraction affected ( F A ). The predicted fractional growth inhibition of the drug combination is calculated using the equation F A + F B − ( F A × F B ), where F A and F B are the fractional growth inhibitions of the drugs A and B at a given dose.…”

The Mitochondrial RNA Granule Modulates Manganese-Dependent Cell Toxicity

“…We reasoned that much like FASTKD2 Δ and DHX30 Δ cells, pharmacological disruption of mitochondrial protein synthesis could be protective for manganese exposed cells. We used the Zero Interaction Potency model to quantitative assess synergistic or antagonistic effects of drugs on manganese-induced cell mortality (48, 49). We used IMT1B, an inhibitor of the mitochondrial RNA polymerase (50); actinonin, an agent that induces degradation of mitochondrial rRNAs and mRNAs (51); and doxycycline, a mitochondrial ribosome protein synthesis inhibitor (Fig.…”

Evaluation of synergism in drug combinations and reference models for future orientations in oncology

“…Recently, there has been an increase in the number of publications related to the definition of synergism, such as the “lack-of-fit” model ( Lederer et al., 2019 ) or the rediscovered Hand model ( Hand, 2000 ; Sinzger et al., 2019 ). Novel ways to evaluate synergism in drug combination studies also appeared such as the ZIP model ( Yadav et al., 2015 ), Combenefit (SANE) ( Di Veroli et al., 2016 ), Bivariate Response to Additive Interacting Doses (BRAID) ( Twarog et al., 2016 ), Schindler's Hill partial differential equation ( Schindler, 2017 ), the SynergyFinder software ( Zheng et al., 2022 ), the Multi-dimensional Synergy of Combinations (MuSyC) ( Meyer et al., 2019 ), the effective dose model ( Zimmer et al., 2016 ) and the copula model ( Lambert and Dawson, 2019 ), for example. Next, we will describe some of the most common methods for directly quantifying and evaluating drug synergism ( Ma and Motsinger-Reif, 2019 ).…”