A community computational challenge to predict the activity of pairs of compounds

Abstract

Recent therapeutic successes have renewed interest in drug combinations, but experimental screening approaches are costly and often identify only small numbers of synergistic combinations. The DREAM consortium launched an open challenge to foster the development of in silico methods to computationally rank 91 compound pairs, from the most synergistic to the most antagonistic, based on gene-expression profiles of human B cells treated with individual compounds at multiple time points and concentrations. Using scoring metrics based on experimental dose-response curves, we assessed 32 methods (31 community-generated approaches and SynGen), four of which performed significantly better than random guessing. We highlight similarities between the methods. Although the accuracy of predictions was not optimal, we find that computational prediction of compound-pair activity is possible, and that community challenges can be useful to advance the field of in silico compound-synergy prediction.

A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction

“…A number of approaches have been developed to model drug synergy using chemical, biological, and molecular data from cancer cell lines 6,10 but with limited translatability to the clinic. A key bottleneck in the development of such models has been a lack of public data sets of sufficient size and variety to train computational approaches 4,7,8 , particularly considering the diversity of biological mechanisms that may influence drug response.…”

In-Silico Methodologies for Cancer Multidrug Optimization

“…Yang et al [9] hypothesized that if a cell is treated by compound (a) then compound (b), the first compound (a) would affect the cell transcriptome and modify the effect of compound (b). The transcriptional expression profiles after treatment by individual drugs were compared.…”

“…For IUPUI_CCBB [9], the differentially expressed genes were identified for each drug treatment. Genes that were significantly differentially expressed by both drugs with the same direction of regulation were recognized as synergistic.…”

“…The two best-performing methods in DREAM challenge [9] namely DIGRE and IUPUI_CCBB methods applied mathematical methods to rank drug combinations.…”

“…AstraZeneca-Sanger Drug Combination DREAM Challenge was launched using 85 cancer cell lines and 11,759 drug combination screening for 118 drugs [8]. The predictive models were designed to differentiate synergistic, additive and antagonistic combinations and predict new synergistic combinations in silico [9][10][11][12][13][14][15][16][17].…”