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 高通量的OMICS技术允许全面测量各种生物分子 过去十年的成倍便宜。将这些新兴技术与自动化耦合 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秒至100,000次生物分子或细胞 特征，而不是专注于单个蛋白质，途径或物理特征。这些数据的复杂性 需要计算工具进行正确的分析和解释。在本提案的第一阶段，我们结合了 基于LC-MS/MS的代谢组学专家的双重优势与代谢组学领导者的代谢组学（OMIX技术） 数据分析（Sinopia Biosciences）以开发基于代谢组学的高通量筛查平台。我们 筛选的〜250 FDA批准了两种细胞系上广泛药物类别的小分子。此数据集 将其与D4开创性项目的匹配数据集进行了比较，连接图，这是一个 转录组学筛查和药物表征，发现和重新定位的查询平台。在第一阶段， 我们观察到，从技术和生物效用的角度来看，代谢组学数据提供了 具有信号保真度，敏感性和与与或相关性的相关性的正交数据集 超过连接图。此外，我们看到了2型血浆代谢物变化的高度一致性 糖尿病和类风湿关节炎患者患有体外代谢物的相关药物变化用于 适应症。这些结果表明，基于代谢组学的高通量筛查平台不是 仅作为连接图的完整数据集可行，但是代谢组学数据甚至可以播放 在药物发现中的主要作用。在此II阶段提案中，我们将专注于化学和遗传分析 体外扰动进一步证明平台的力量并确定商业机会 治疗遗传定义的稀有疾病。我们将将数据生成扩展到〜3300个生物活性化合物 三个细胞系。此外，我们将在这三个细胞系上介绍50个遗传敲除，以在体外对 相关的稀有疾病。使用Sinopia的平台，我们将选择用于后续评估的化合物以识别 在这些罕见疾病中看到的代谢失调的候选人。成功的体外计划 将有助于播种早期发现管道，该管道将通过私人资助来提升 投资，患者倡导团体和额外的联邦赠款。