SBIR-STTR Award

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 Statement:
Project Narrative 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.

Project Terms:
Adverse effects; Age; Algorithms; Animal Model; Automation; base; Biochemical; Biochemical Pathway; Bioinformatics; Biological; Biological Assay; Biological Markers; Biological Sciences; Cancer cell line; Caring; cell growth; Cell Line; Cells; chemical genetics; Chemicals; Classification; Collaborations; commercialization; Complement; Complex; computerized tools; Computing Methodologies; cost; cost efficient; Coupling; Data; data acquisition; Data Analyses; data integration; Data Quality; Data Set; Development; Dimensions; Disease; Doxorubicin; drug development; drug discovery; drug efficacy; Emerging Technologies; Evolution; Exposure to; Expression Profiling; FDA approved; Functional disorder; Gene Expression; Generations; Genetic; Genetic Diseases; Genomics; high throughput screening; Human; improved; In Vitro; in vivo; inhibitor/antagonist; innovation; Institutes; Intervention; Luminescent Proteins; Machine Learning; Maps; Mass Spectrum Analysis; MCF7 cell; Measurement; Measures; Metabolic; Metabolism; metabolomics; Methodology; Microarray Analysis; Modeling; molecular phenotype; Molecular Profiling; multiple omics; network models; novel; novel therapeutics; Pathway Analysis; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmaceutical Services; Pharmacologic Substance; Phase; Phenotype; Physiological; Physiological Processes; protein metabolite; Proteins; Research; RNA; Safety; Sampling; screening; Sirolimus; Small Business Innovation Research Grant; small molecule; success; synergism; Technology; Testing; Therapeutic; Time; TimeLine; tool; trait; transcriptomics

High-throughput omics technologies allow for measuring various biomolecules comprehensively and over thepast decade have become exponentially less expensive. Coupling these emerging technologies with automationapproaches and the phenotypic-based drug discovery paradigm allows for data-driven drug discovery (D4). D4focuses on a complete cellular readout, quantitatively measuring 100s to 100,000s of biomolecules or cellularfeatures, rather than focusing on a single protein, pathway, or physiological trait. The complexity of this datarequires computational tools for proper analysis and interpretation. In Phase I of this proposal, we combined thedual strengths of experts in LC-MS/MS based metabolomics (Omix Technologies) with leaders in metabolomicsdata analysis (Sinopia Biosciences) to develop a metabolomics based high-throughput screening platform. Wescreened ~250 FDA approved small molecules from a broad range of drug classes on two cell lines. This datasetwas compared to a matching dataset from the pioneering project for D4, the Connectivity Map, which is atranscriptomics screening and query platform for drug characterization, discovery, and repositioning. In Phase I,we observed that from both a technical and biological utility standpoint, the metabolomics data provided anorthogonal dataset with signal fidelity, sensitivity, and relevance to compound properties comparable to orexceeding the Connectivity Map. Further, we saw high concordance of plasma metabolite changes in type 2diabetes and rheumatoid arthritis patients with in vitro metabolite changes of related drugs used for thoseindications. Thus, these results suggest that a metabolomics based high-throughput screening platform is notonly viable as a complementary dataset to the Connectivity Map, but that metabolomics data can even play aprimary role in drug discovery. In this Phase II proposal, we will focus on profiling chemical and geneticperturbations in vitro to further demonstrate the power of the platform and identify commercial opportunities fortreating genetically defined rare diseases. We will expand data generation to ~3300 bioactive compounds acrossthree cell lines. Further, we will profile 50 genetic knockouts on those three cell lines to model in vitro theassociated rare diseases. Using Sinopia's platform, we will select compounds for follow-up evaluation to identifycandidates that correct for metabolic dysregulations seen in those rare diseases. Successful in vitro programswill aid in seeding of an early stage discovery pipeline that will be advanced through funding by privateinvestment, patient advocacy groups, and additional federal grants.

Public Health Relevance Statement:
Project Narrative 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 have built a unique platform focused on understanding the use of metabolomics in drug discovery. This proposal will apply the discovery engine on several rare genetic diseases with significant unmet needs.

Project Terms: