Drug Combination Signatures for Prediction and Mitigation of Toxicity

用于预测和减轻毒性的药物组合特征

• 批准号: 8787833
• 负责人: Marc R. Birtwistle
• 金额: $ 209.97万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-10 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8787833
• 关键词: Academia; Adverse event; Cardiac Myocytes; Cardiotoxicity; Cell Line; Cells; Chronic Myeloid Leukemia; Clinical Research; Communities; Computer Analysis; Coupled; Data; Data Coordinating Center; Databases; Development; Development Plans; Differential Equation; Dose; Drug Combinations; Drug Targeting; Drug effect disorder; Drug toxicity; Educational workshop; Ensure; Event; Functional disorder; Funding; Gene Expression; Generations; Genes; Genomics; Goals; Graph; Hepatic; Hepatocyte; Histocompatibility Testing; Human; Human Cell Line; Imagery; Individual; Industry; Knowledge; Measurement; Measures; Messenger RNA; Modeling; Molecular Target; Neurons; Office of Administrative Management; Online Systems; Ontology; Pathway Analysis; Pathway interactions; Pattern; Peripheral Nervous System Diseases; Pharmaceutical Preparations; Phase; Phase III Clinical Trials; Phosphorylation; Physiological; Production; Protein Dynamics; Proteins; Proteomics; Quality Control; Reporting; Research; Research Personnel; Research Project Grants; Running; Specific qualifier value; Statistical Models; Structural Models; System; Therapeutic; Tissues; Toxic effect; Tyrosine Kinase Inhibitor; Weight; Work; base; cell type; computerized data processing; drug development; drug discovery; human disease; improved; induced pluripotent stem cell; mathematical model; network models; outreach; public health relevance; quality assurance; rat Ran 2 protein; research study; response; simulation; tool

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.

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高通量测量以及蛋白质状态的中通量实验测量作为计算分析的基础，该计算分析将网络分析与多种细胞类型中的结构约束和动力学模型相结合，以识别预测单个药物诱导的毒性和药物组合缓解该毒性的特征。为了将这些特征锚定在可观察到的人类疾病和治疗方法中，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），发现了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，我们可以使用这些观察，以提高我们的能力，预测药物的毒性和缓解药物对。该中心有三个主要目标：1）实验获得mRNA、蛋白质和蛋白质状态的表达模式（例如磷酸化）的影响：在FAERS中鉴定的120种两药组合，其中第二种药物减轻了由第一种药物诱导的严重毒性，以及在FAERS中显示的130种单独药物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细胞系和直接从人诱导多能细胞（hIPSC）分化的细胞类型。对于每种药物组合和两种组分药物，我们将从至少18种细胞系中获得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，该分析将使用人类相互作用组和基因本体的统计和网络模型与基于结构模型的过滤和动态多隔室ODE模型相结合，以获得每种药物组合的关系签名集。为此，我们将结合联合收割机的干扰素诱导的mRNA水平和蛋白质水平的变化，开发网络，将通过结构建模的约束，以确定新的脱靶和动态模型，使用蛋白质状态数据。网络模型提供毒性缓解机制的假设，在一个有向符号指定的图形，将定量加权的动态模型的全局灵敏度分析的形式。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，用于与LINCS数据协调中心和更大的社区共享原始和处理后的数据。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从头分析工具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来开发可以竞争个人研究资助的研究项目。