Development of a metabolomics and machine learning based high-throughput screening platform for data-driven drug discovery

开发基于代谢组学和机器学习的高通量筛选平台，用于数据驱动的药物发现

• 批准号: 10343786
• 负责人: Aarash Bordbar
• 金额: $ 87.16万
• 依托单位: SINOPIA BIOSCIENCES, INC.
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10343786
• 关键词: Age; Autoimmune; Automation; Biological; Biological Assay; Biological Sciences; Capital; Caring; Cell Line; Cells; Chemicals; Clinical; Clustered Regularly Interspaced Short Palindromic Repeats; Companions; Complex; Computing Methodologies; Coupling; Data; Data Analyses; Data Set; Development; Dimensions; Disease; Dose; Drug usage; Emerging Technologies; Evaluation; Exposure to; FDA approved; Funding; Gene Expression; Generations; Genetic; Grant; In Vitro; Institutes; Investments; Knock-out; Libraries; MCF7 cell; Machine Learning; Maps; Measures; Metabolic; Metabolic dysfunction; Molecular Profiling; Non-Insulin-Dependent Diabetes Mellitus; Parkinson Disease; Pathogenesis; Pathway interactions; Patients; Pharmaceutical Preparations; Phase; Phenotype; Physiological; Plasma; Play; Pre-Clinical Model; Privatization; Property; Proteins; Rare Diseases; Rheumatoid Arthritis; Role; Sampling; Seeds; Signal Transduction; Statistical Models; Technology; Time; TimeLine; Transcript; base; chemical genetics; computerized tools; cost; data complexity; drug development; drug discovery; drug mechanism; drug repurposing; follow-up; high throughput screening; in vitro Model; insulin sensitizing drugs; interest; metabolomics; molecular phenotype; patient advocacy group; pre-clinical; programs; rare genetic disorder; response; screening; small molecule; success; trait; transcriptomics

Project Summary High-throughput omics technologies allow for measuring various biomolecules comprehensively and over the past decade have become exponentially less expensive. Coupling these 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 or cellular features, rather than focusing on a single protein, pathway, or physiological trait. The complexity of this data requires computational tools for proper analysis and interpretation. In Phase I of this proposal, we combined the dual strengths of experts in LC-MS/MS based metabolomics (Omix Technologies) with leaders in metabolomics data analysis (Sinopia Biosciences) to develop a metabolomics based high-throughput screening platform. We screened ~250 FDA approved small molecules from a broad range of drug classes on two cell lines. This dataset was compared to a matching dataset from the pioneering project for D4, the Connectivity Map, which is a transcriptomics 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 an orthogonal dataset with signal fidelity, sensitivity, and relevance to compound properties comparable to or exceeding the Connectivity Map. Further, we saw high concordance of plasma metabolite changes in type 2 diabetes and rheumatoid arthritis patients with in vitro metabolite changes of related drugs used for those indications. Thus, these results suggest that a metabolomics based high-throughput screening platform is not only viable as a complementary dataset to the Connectivity Map, but that metabolomics data can even play a primary role in drug discovery. In this Phase II proposal, we will focus on profiling chemical and genetic perturbations in vitro to further demonstrate the power of the platform and identify commercial opportunities for treating genetically defined rare diseases. We will expand data generation to ~3300 bioactive compounds across three cell lines. Further, we will profile 50 genetic knockouts on those three cell lines to model in vitro the associated rare diseases. Using Sinopia’s platform, we will select compounds for follow-up evaluation to identify candidates that correct for metabolic dysregulations seen in those rare diseases. Successful in vitro programs will aid in seeding of an early stage discovery pipeline that will be advanced through funding by private investment, patient advocacy groups, and additional federal grants.

项目摘要 高通量组学技术允许全面地测量各种生物分子， 在过去的十年里，成本已经成倍下降。将这些新兴技术与自动化相结合 方法和基于表型的药物发现范式允许数据驱动的药物发现（D4）。D4 专注于完整的细胞读数，定量测量100到100，000个生物分子或细胞 功能，而不是专注于单一的蛋白质，途径或生理特性。这些数据的复杂性 需要计算工具进行适当的分析和解释。在本提案的第一阶段， LC-MS/MS代谢组学专家（Omix Technologies）与代谢组学领导者的双重优势 数据分析（Sinopia Biosciences），以开发基于代谢组学的高通量筛选平台。我们 在两种细胞系上筛选了约250种FDA批准的小分子药物。此数据集 与D4的开创性项目中的匹配数据集进行了比较，即连通性地图， 转录组学筛选和查询平台，用于药物表征、发现和重新定位。在第一阶段， 我们观察到，从技术和生物实用性的角度来看，代谢组学数据提供了一个 正交数据集，信号保真度，灵敏度和化合物性质的相关性与或 超过了连通图。此外，我们发现2型糖尿病患者的血浆代谢物变化具有高度一致性， 糖尿病和类风湿性关节炎患者体外代谢产物变化的相关药物使用情况 迹象。因此，这些结果表明，代谢组学为基础的高通量筛选平台， 作为连接图的补充数据集，代谢组学数据甚至可以发挥重要作用。 在药物发现中的主要作用。在第二阶段的提案中，我们将重点关注化学和遗传分析， 在体外扰动，以进一步证明该平台的力量，并确定商业机会， 治疗基因定义的罕见疾病我们将把数据生成扩展到约3300种生物活性化合物， 三个细胞系此外，我们将在这三种细胞系上分析50种基因敲除，以在体外模拟 相关的罕见疾病。利用Sinopia的平台，我们将选择化合物进行后续评估， 这些候选人可以纠正这些罕见疾病中出现的代谢失调。成功的体外项目 将帮助播种早期阶段的发现管道，这将通过私人资助来推进。 投资、患者权益团体和额外的联邦赠款。