Predicting Synergism of Cancer Drug Combinations Using NCI-ALMANAC Data

Frontiers in Chemistry
Pavel SidorovPedro J Ballester

Abstract

Drug combinations are of great interest for cancer treatment. Unfortunately, the discovery of synergistic combinations by purely experimental means is only feasible on small sets of drugs. In silico modeling methods can substantially widen this search by providing tools able to predict which of all possible combinations in a large compound library are synergistic. Here we investigate to which extent drug combination synergy can be predicted by exploiting the largest available dataset to date (NCI-ALMANAC, with over 290,000 synergy determinations). Each cell line is modeled using primarily two machine learning techniques, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), on the datasets provided by NCI-ALMANAC. This large-scale predictive modeling study comprises more than 5,000 pair-wise drug combinations, 60 cell lines, 4 types of models, and 5 types of chemical features. The application of a powerful, yet uncommonly used, RF-specific technique for reliability prediction is also investigated. The evaluation of these models shows that it is possible to predict the synergy of unseen drug combinations with high accuracy (Pearson correlations between 0.43 and 0.86 depending on the considered cell line, with XGBoost provi...Continue Reading

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May 6, 2020·Bioinformatics·Zexuan SunPingzhao Hu
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Methods Mentioned

BETA
xenografts

Clinical Trials Mentioned

NCT02211755
NCT02379416

Software Mentioned

RDkit
ComboScore
ALMANAC
Fragmentor
CompuSyn
Elastic
NCI
CalcuSyn
ISIDA
LODO

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