Metabolomics-based phenotypic screens for evaluation of drug synergy via direct-infusion mass spectrometry
Xiyuan Lu,
G. Lavender Hackman,
Achinto Saha,
Atul Singh Rathore,
Meghan Collins,
Chelsea Friedman,
S. Stephen Yi,
Fumio Matsuda,
John DiGiovanni,
Alessia Lodi,
Stefano Tiziani
Affiliations
Xiyuan Lu
Department of Nutritional Sciences, College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Department of Pediatrics, Dell Medical School, The University of Texas at Austin, Austin, TX 78723, USA
G. Lavender Hackman
Department of Nutritional Sciences, College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Department of Pediatrics, Dell Medical School, The University of Texas at Austin, Austin, TX 78723, USA
Achinto Saha
Division of Pharmacology and Toxicology, College of Pharmacy, The University of Texas at Austin, Austin,TX 78712, USA
Atul Singh Rathore
Department of Nutritional Sciences, College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Department of Pediatrics, Dell Medical School, The University of Texas at Austin, Austin, TX 78723, USA
Meghan Collins
Department of Nutritional Sciences, College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Department of Pediatrics, Dell Medical School, The University of Texas at Austin, Austin, TX 78723, USA
Chelsea Friedman
Division of Pharmacology and Toxicology, College of Pharmacy, The University of Texas at Austin, Austin,TX 78712, USA
S. Stephen Yi
Department of Oncology, Dell Medical School, Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78723, USA; Institute for Cellular and Molecular Biology (ICMB), College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Department of Biomedical Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, TX 78712, USA; Oden Institute for Computational Engineering and Sciences (ICES), The University of Texas at Austin, Austin, TX 78712, USA
Fumio Matsuda
Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
John DiGiovanni
Department of Pediatrics, Dell Medical School, 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; Department of Oncology, Dell Medical School, Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78723, USA
Alessia Lodi
Department of Nutritional Sciences, College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Department of Pediatrics, Dell Medical School, The University of Texas at Austin, Austin, TX 78723, USA; Corresponding author
Stefano Tiziani
Department of Nutritional Sciences, College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Department of Pediatrics, Dell Medical School, The University of Texas at Austin, Austin, TX 78723, USA; Department of Oncology, Dell Medical School, Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78723, USA; Institute for Cellular and Molecular Biology (ICMB), College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Corresponding author
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.
