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

项目摘要 高通量组学技术允许全面和全面地测量各种生物分子 过去十年的成本已成指数级下降。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，定量测量生物分子或细胞的100到100,000秒 特征，而不是专注于单一的蛋白质、途径或生理特征。这些数据的复杂性 需要计算工具来进行适当的分析和解释。在本提案的第一阶段，我们结合了 基于LC-MS/MS的代谢组学(OMix Technologies)专家与代谢组学领先者的双重优势 数据分析(Sinopia Biosciences)，开发基于代谢组学的高通量筛选平台。我们 在两个细胞系上从广泛的药物类别中筛选出约250个FDA批准的小分子。此数据集 与D4的开创性项目-连通性地图-的匹配数据集进行了比较，这是一个 用于药物表征、发现和重新定位的转录组筛选和查询平台。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一个 具有与或相当的复合属性的信号保真度、敏感度和相关性的正交数据集 超越连接地图。此外，我们还观察到2型患者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体外代谢产物的变化及相关药物的应用 有迹象表明。因此，这些结果表明，基于代谢组学的高通量筛选平台不是 只有作为连通性地图的补充数据集才是可行的，但代谢组学数据甚至可以发挥作用 在药物发现中的主要作用。在这个第二阶段的提案中，我们将重点分析化学和遗传 在体外进行扰动，以进一步展示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，我们将分析这三个细胞系上的50个基因敲除，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，以确定 纠正那些罕见疾病中出现的代谢失调的候选人。成功的试管计划 将帮助播种一条早期发现管道，该管道将通过私人资助推进 投资、患者权益倡导团体和额外的联邦拨款。

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.