High-throughput omics technologies allow for measuring various biomolecules comprehensively and over the past decade have become exponentially less expensive. Coupling these developing emerging technologies with automation approaches and the phenotypic-based drug discovery paradigm allows for ?data-driven? drug discovery (D4). D4 focuses on a complete cellular readout, quantitatively measuring 100s to 100,000s of biomolecules, rather than focusing on a single protein, pathway, or physiological trait. The complexity of this data requires computational tools for proper analysis and interpretation. A pioneering project and dataset for D4 is the Broad Institute?s Connectivity Map, which is a transcriptomics screening and query platform for drug characterization, discovery, and repositioning. Though with many successes, the Connectivity Map is based on only a single biomolecule type, RNA, and downstream effects caused by chemical perturbations to proteins and metabolites are ignored. In this proposal, we combine the dual strengths of experts in LC-MS/MS based metabolomics (Omix Technologies) with leaders in metabolic network modeling and metabolomics data analysis (Sinopia Biosciences) to develop a metabolomics based high-throughput compound screening platform. Our preliminary data for two major drugs (doxorubicin and rapamycin) showcases that our approach has technical validity, accuracy, and provides potential biological utility that complements transcriptomics based approaches. This Phase I proposal will assess the biological utility of a metabolomics-based screening platform. First, we will profile ~250 FDA approved small molecules from a broad range of drug classes. Second, we will develop the necessary bioinformatics pipelines, mechanistic metabolic models, and machine learning algorithms to analyze and interpret these complex datasets. Finally, we will assess whether adding metabolomics data to the Connectivity Map boosts D4 predictions including assessing compound mechanisms of actions, compound similarity, identifying biomarkers for drug efficacy and safety, and identifying drug repurposing opportunities. After the biological utility of this approach is demonstrated in Phase I, Phase II will focus on profiling of novel chemical and genetic perturbations to further demonstrate the power of the platform and identify commercial opportunities for treating rare genetic diseases.

Public Health Relevance

Data-driven drug discovery provides the potential to accelerate drug development timelines, decrease costs, and ultimately provide better care to patients. Combining Sinopia Bioscience?s and Omix Technologies strengths in high-throughput data generation and analysis, we will build a unique platform focused on the impact of human metabolism in disease. This proposal will test the viability of this approach and focus on several rare genetic diseases with significant unmet needs.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Small Business Innovation Research Grants (SBIR) - Phase I (R43)
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Special Emphasis Panel (ZRG1)
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Ravichandran, Veerasamy
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Sinopia Biosciences, Inc.
San Diego
United States
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