文章基本信息

标题：Metabolomics-based phenotypic screens for evaluation of drug synergy via direct-infusion mass spectrometry
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作者：Xiyuan Lu ; G. Lavender Hackman ; Achinto Saha 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2022
卷号：25
期号：5
页码：1-22
DOI：10.1016/j.isci.2022.104221
语种：English
出版社：Elsevier
摘要：SummaryDrugs 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 bothin vitro(three-dimensional model) andin vivo. The proposed unbiased metabolomics screening platform can be used to rapidly generate phenotype-informed datasets and quantify synergy for combinatorial drug discovery.Graphical abstractDisplay OmittedHighlights•High-content metabolomics screening platform with stable isotope tracers was developed•The platform allows for assignment to different library screens andin vitromodels•New algorithm is robust to apply on omics datasets for drug synergy evaluation•Novel drug combination to treat prostate cancer was discovered and validatedin vivoBioinformatics; Metabolomics; Omics; Pharmacoinformatics