The Molecular and Cellular Biology graduate program MCB 7410 Seminar features Alison Brittain discussing “Using Big Data for Drug Discovery and Improved Therapeutics” on Tuesday, Nov. 28, at 4:35 p.m. in Porter 104.
Refreshments are provided.
Abstract: With the increasing popularity of various “omics” fields, a rising problem in biomedical sciences has become how to best utilize the vast amount of data available in various scientific databases. This issue is particularly relevant in drug targeting and therapeutics, as vast amounts of data are available to researchers and clinicians regarding drug efficacy, interaction and metabolism. Researchers at Stanford University have demonstrated a bioinformatics-based method to utilize complementary data sources, including electronic health records and gene expression databases, to predict potentially synergistic drug pairs that may improve outcomes in breast cancer patients[1]. These researchers utilized electronic health records from 9,945 patients in the Palo Alto area, as well as the Oncoshare database, to determine drug combinations associated with alterations in mortality. Using a type of gene-set enrichment analysis, they compared these drug combinations with other drug combinations predicted using 14 different gene and protein expression databases derived from breast cancer patients. Their analyses predicted 528 drug pairs derived from the electronic health records that fell into three different categories, with two of these categories specifically enriched after gene expression analysis: anti- inflammatory agents with lipid modifiers, and anti-inflammatory agents with hormone antagonists. This study demonstrates the usefulness of electronic medical records coupled with other bioinformatics databases to predict beneficial drug interactions, potentially improving cancer therapy.
- Low, Y.S., et al., Synergistic drug combinations from electronic health records and gene expression. J Am Med Inform Assoc, 2017. 24(3): p. 565-576.
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