Principal component analysis-based Euclidean Distance Synergy quantification (PEDS)

Drugs used in combination to treat diseases such as cancer can synergize to increase efficacy, decrease toxicity, and prevent drug resistance. Traditionally, high-throughput screens for drug discovery and synergy evaluation have relied on univariate data assessed in two-dimensional in vitro models. While this approach is incredibly valuable to identify promising novel drug candidates, phenotypic screening methodologies could be beneficial to provide deep insight into the molecular response of drug combination with an increased likelihood of improved clinical outcomes. We have developed a high-content metabolomics drug screening platform using stable isotope tracer direct infusion mass spectrometry that informs a novel algorithm called Principal component analysis-based Euclidean Distance Synergy quantification (PEDS) to determine synergy from multivariate phenotypic data. Using a cancer drug library, we validated the drug screening integrating isotope enriched metabolomics data and computational data mining on a panel of prostate cell lines and we verified the synergy between CB-839 and docetaxel in a three-dimensional in vitro model as wells as an in vivo prostate cancer mouse model. The proposed unbiased metabolomics screening platform can be used to rapidly generate phenotype-informed datasets to quantify synergy and prioritize the most promising drug combinations for drug discovery.