Drug Combination Signatures for Prediction and Mitigation of Toxicity

描述（由申请人提供）：DTSGC的总体目标是使用基因组和蛋白质组学的高通量测量加上蛋白质状态的中通量实验测量作为计算分析的基础，将网络分析与多种细胞类型的结构约束和动态模型集成在一起，以识别预测单个药物诱导的毒性和通过药物组合减轻这种毒性的特征。为了确定可观察到的人类疾病和治疗方法的特征，我们将利用我们最近研究中采用的策略，在该研究中，我们搜索了fda不良事件报告系统数据库（FAERS），并发现近数千种用于人类的药物组合，其中第二种药物减轻了与第一种药物相关的严重毒性。我们假设我们可以利用这些观察结果来提高我们预测药物毒性和药物对减轻毒性的能力。该中心有三个主要目标：1)通过实验获得约250种扰动原的mRNA、蛋白质和蛋白质状态的表达模式（例如磷酸化）；在FAERS中确定的120种双药组合，其中第二种药物减轻了第一种药物引起的严重毒性；在FAERS中显示的130种药物会引起三种严重毒性之一-心脏毒性；肝毒性或周围神经病变。我们将使用原代或已建立的人类细胞系和直接从人类诱导多能细胞（hIPSC）分化的细胞类型。对于每种药物组合和两种组成药物，我们将获得至少18个细胞系的mRNA，蛋白质组学数据和动态蛋白质状态。2)我们将利用实验数据进行多层分析，结合统计和网络模型，利用人类交互组和基因本体，基于结构模型的滤波和动态多室ODE模型，获得每个药物组合的关系特征集。为此，我们将结合摄动原诱导的mRNA水平和蛋白质水平的变化来开发受结构建模约束的网络，以利用蛋白质状态数据识别新的脱靶和动态模型。网络模型以有向符号指定图的形式提供毒性缓解机制假设，该假设将通过动态模型的全局敏感性分析进行定量加权。我们建议以每年约4000个签名的速度获得药物组合和单个药物的非加权和加权签名。3)我们将开发计算和可视化工具，与LINCS数据协调中心和更大的社区共享原始和处理过的数据。我们将i)为所有类型的研究人员开发基于网络的数据可视化和重新分析工具ii)使用Coursera运行基于网络的课程，用于数据利用和基于签名的研究项目的开发iii)举办4-6个个性化研讨会，使学术研究人员能够利用我们的签名开发研究项目，从而能够竞争个人研究资助资金。

DESCRIPTION (provided by applicant): The overall goal of the DTSGC is to use genomic and proteomic high-throughput measurements coupled with mid-throughput experimental measurement of protein states as the basis for computational analysis that integrates network analyses with structural constraints and dynamical models in multiple cell types to identify signatures that predict toxicity induced by individual drugs and mitigation of this toxicity by dru combinations. To anchor the signatures in observable human disease and therapeutics, we will leverage the strategy employed in our recent study, in which we searched the FDA-Adverse Event Reporting System Database (FAERS) and found nearly thousands of drug combinations used in humans where a second drug mitigates serious toxicity associated with first drug. We hypothesize that we can use these observations to improve our capability to predict toxicity of drugs and mitigation by drug pairs. The Center has three major goals: 1) experimentally obtain expression patterns of mRNA, proteins and protein states (e.g. phosphorylation) for around 250 perturbagens: 120 two-drug combinations identified in the FAERS whereby the second drug mitigates serious toxicities induced by the first drug and 130 individual drugs that have been shown in FAERS to cause one of three serious toxicities-cardiotoxicity; hepatic toxicity or peripheral neuropathy. We will use primary or established human cell lines and cell types directly differentiated from human induced pluripotent cells (hIPSC). For each drug combination and the two constituent drugs we will obtain mRNA, proteomic data, and dynamic protein state from least 18 cell lines. 2) We will utilize the experimental data for multi-tier analyses that combines statistical and network models using the human interactome and Gene Ontology with structural model based filtering and dynamical multi-compartment ODE models to obtain sets of relational signatures for each drug combination. For this we will combine the perturbagen induced changes in mRNA levels and protein levels to develop networks that will be constrained by structural modeling to identify new off-targets and dynamical models using the protein state data. The network models provide toxicity mitigation mechanism hypotheses in the form of a directed sign-specified graph which will be quantitatively weighted by global sensitivity analysis of the dynamical models. We propose to obtain non-weighted and weighted signatures for both drug combinations and individual drugs at the rate of around 4000 signatures per year. 3) We will develop computational and visualization tools for sharing the raw and processed data with the LINCS Data Coordinating Center and the larger community. We will i) develop web-based tools for data visualization and de novo analysis for all types of researchers ii) run web-based courses using Coursera for data utilization and development of signature-based research projects iii) conduct 4-6 personalized workshops to enable academic researchers to utilize our signatures to develop research projects that can compete for individual research grant funding.