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以后的批准。最近，-组学数据，特别是转录组学数据，已经在细胞系统的药物治疗后产生了足够大的规模（对于bbb20万种化合物），使我们能够理解负责药物作用的药物-靶标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，这项工作将极大地增强我们对配体-靶标相互作用和转录组学变化之间联系的理解，并允许我们在实际水平上利用一种方便的读出类型进行系统范围的作用模式分析。该项目将特别开发新的和评估现有的因果推理和相关网络方法，以预测给定差异基因表达变化的最相关目标。为此，我们将使用ConnectivityMap和LINCS数据库，这些数据库将化合物结构与大规模的转录组变化联系起来（LINCS的bbb20 000个化合物），并通过使用细胞信号传导系统生物学信息和其他相关信息的机制建模，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，一旦这些关系得到充分理解，我们将在细胞系统中定义前瞻性实验，例如使用开发的算法选择化合物，这些算法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评估与模型预测的一致性。

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.