Using Transcriptomics Data to Understand Drug Mode-of-Action

了解化合物的作用模式在药物发现和开发过程中至关重要，无论是为了设计和优化目的，还是为了预测副作用和后来的批准。最近，-组学数据，特别是这里的转录组数据，已经在足够大的规模(对于&gt；20,000个化合物)的细胞系统的药物处理后产生，使我们能够了解负责药物作用的药物-靶相互作用与下游系统测量的基因表达变化之间的联系。因此，这项工作将极大地提高我们对配体-靶相互作用与转录组变化之间在基础水平上的联系的理解，并使我们能够在实际水平上利用一种方便的读出类型来进行系统范围的作用模式分析。这个项目将特别开发新的和评估现有的因果推理和相关的网络方法，以预测与给定差异基因表达变化最相关的目标。为此，我们将使用ConnectivityMap和Lincs数据库，它们将化合物结构与大规模转录变化(Lincs为20,000种化合物)联系起来，并通过使用关于细胞信号和其他相关信息的系统生物学信息通过机械建模将这些变化与药物的已知蛋白质靶点(从DrugBank和ChEMBL等数据库获得)联系起来。随后，一旦充分理解了这些关系，我们将定义细胞系统中的前瞻性实验，例如使用开发的算法选择的化合物，这些化合物将与礼来公司合作在小胶质细胞系中进行筛选。基因表达的变化将被测量并与预测进行比较，表型反应将被测量并与模型预测的一致性进行评估。

Understanding the mode of action of compounds is crucial during drug discovery and development, both for design and optimization purposes, but also, for instance, to anticipate side effects and for later approval. Recently, -omics data, and here in particular transcriptomics data, has been generated after drug treatments of cellular systems on a sufficiently large scale (for >20,000 compounds) that allows us to understand connections between drug-target interactions responsible for drug action and gene expression changes measured downstream systematically.Hence, this work will greatly enhance our understanding of links between ligand-target interaction and transcriptomics changes on the fundamental level, and allow us to utilize a convenient type of readout for systems-wide mode-of-action analysis on the practical level. This project will in particular develop new and evaluate existing causal reasoning and related network approaches to predict the most relevant targets to a given differential gene expression change. To this end, we will employ the ConnectivityMap and LINCS databases which link compound structures to transcriptomic changes on a large scale (>20,000 compounds for LINCS), and link those changes to the known protein targets of drugs (obtained from databases such as DrugBank and ChEMBL) via mechanistic modelling using systems biology information on cellular signaling and other relevant information.Subsequently, once those relationships have been sufficiently well understood, we will define prospective experiments in cellular systems, such as compounds selected using the developed algorithms which will be screened in microglial cell lines in collaboration with Eli Lilly. Changes in gene expression will be measured and compared to prediction, and phenotypic responses will be measured and evaluated with respect to agreement with the model predictions.