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

iScience

John DiGiovanni¹  S. Stephen Yi¹  G. Lavender Hackman¹  Atul Singh Rathore¹  Fumio Matsuda²  Achinto Saha²  Xiyuan Lu²  Chelsea Friedman²  Stefano Tiziani³  Alessia Lodi⁴  Meghan Collins⁴ 

[1] Department of Pediatrics, Dell Medical School, The University of Texas at Austin, Austin, TX 78723, USA;Department of Nutritional Sciences, College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712, USA;Department of Oncology, Dell Medical School, Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78723, USA;Division of Pharmacology and Toxicology, College of Pharmacy, The University of Texas at Austin, Austin,TX 78712, USA;

关键词: bioinformatics;    metabolomics;    omics;    pharmacoinformatics;   

DOI  :  

来源: DOAJ

【 摘 要 】

Summary: Drugs used in combination can synergize to increase efficacy, decrease toxicity, and prevent drug resistance. While conventional high-throughput screens that rely on univariate data are incredibly valuable to identify promising drug candidates, phenotypic screening methodologies could be beneficial to provide deep insight into the molecular response of drug combination with a likelihood of improved clinical outcomes. We developed a high-content metabolomics drug screening platform using stable isotope-tracer direct-infusion mass spectrometry that informs an algorithm to determine synergy from multivariate phenomics 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 verified the synergy between CB-839 and docetaxel both in vitro (three-dimensional model) and in vivo. The proposed unbiased metabolomics screening platform can be used to rapidly generate phenotype-informed datasets and quantify synergy for combinatorial drug discovery.

【 授权许可】

Unknown