Data Generation Core

数据生成核心将专注于衡量药物组合和个别药物对 与肝毒性、心脏毒性和周围神经病不良事件相关的细胞类型 在生物体水平上观察。这些都是严重的不良事件，会改变药物使用和 发展。我们实验设计的一个新特点是我们选择了药物组合。多数 美国人服用多种药物。彻底测试所有潜在的药物组合是困难的，因为 组合爆炸的可能性和以前没有做过的任何大规模的努力。我们的 根据FAERS报告的严重药物不良事件分析的初步数据显示，120 涉及130种单独药物的药物组合，通过添加第二种药物显著缓解 第一种药物所致的严重毒性。我们将使用一组人类心肌细胞，肝细胞， 以及来自原代/已建立系的神经细胞系，以及来自人类诱导的神经细胞系 多能干细胞。细胞总数为18个。实验数据将是高通量的， 每年产生超过100万个数据点。它将在全基因组的全细胞水平上进行 包括信使核糖核酸水平的变化(微阵列)和蛋白质水平的变化(质谱仪)。我们预计会有一个 信使核糖核酸和蛋白质水平数据的结合将在签名生成中发挥协同作用。我们使用已建立的 然而，具有成本效益的方法是可取的，因为它可以立即生产并促进更多 广泛的吸收和繁殖。这些数据将一起用于开发用于 数据分析和签名生成核心中的药物或药物组合。签名将 通过以下方式突出显示为有针对性的中等吞吐量蛋白质状态测量提供信息的关键途径 微韦斯特阵列。这些有针对性的测量将表征剂量和动态响应 与我们的药物和药物组合相关的生物状态。在整个过程中，我们都专注于仔细的注释 和质量控制将这些数据传播到社区，并向社区开放 在改变我们的初步计划方面的反馈。

The Data Generation Core will focus on measuring the effect of drug combinations and individual drugs on cell types associated with hepatic toxicity, cardiac toxicity and peripheral neuropathy adverse events observed at the organism level. These are all serious adverse events that alter drug usage and development. A novel feature of our experimental design is our selection of drug combinations. Most Americans take multiple drugs. Exhaustively testing all potential drug combinations is intractable due to the combinatorial explosion of possibilities and has not been done by any previous large scale effort. Our preliminary data based on analysis of serious drug adverse events as reported in FAERS has revealed 120 drug combinations involving 130 individual drugs whereby addition of a second drug significantly mitigates the serious toxicity induced by the first drug. We will use a panel of human cardiomyocytes, hepatocytes, and neuronal cell lines from primary/established lines, as well as those derived from human induced pluripotent stem cells. The total of number of cell lines is 18. The experimental data will be high-throughput, producing greater than 1 million data points per year. It will be on the genome-wide whole cell level and include changes in mRNA levels (microarray) and changes in protein levels (mass spec). We expect that a combination of mRNA and protein level data will synergize in signature generation. Our use of established yet cost effective methodologies is desirable since it enables immediate production and facilitates more widespread uptake and reproduction. These data together will be used to develop cellular signatures for the drugs or drug combinations in the Data Analysis and Signature Generation Core. The signatures will highlight key pathways that inform targeted, medium-throughput measurements of protein states by microwestern array. These targeted measurements will characterize dose and dynamic responses of biological states relevant to our drugs and drug combinations. Throughout we focus on careful annotation and quality control for dissemination of these data to the community, and also are open to community feedback in altering our preliminary plan.