Modeling the Effect of Drugs in Intergomics by Linking Drug Ontology and Pathways

通过连接药物本体和通路来模拟药物在组学中的作用

• 批准号: 7558891
• 负责人: GUNTHER SCHADOW
• 金额: $ 24.41万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-26 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7558891
• 关键词: Accounting; Affect; Autacoids; Back; Basic Science; Biochemical Pathway; Biological; Caring; Chemicals; Class; Classification; Clinical; Clinical Data; Clinical Medicine; Clinical Trials; Code; Collection; Data; Data Analyses; Data Quality; Data Set; Data Sources; Databases; Drug Catalogs; Drug Delivery Systems; Drug Industry; Drug Interactions; Drug Labeling; Evaluation; Food; Frequencies; Gene Expression Profile; Health Services Research; Human; Hypersensitivity; Industry; Institution; Knowledge; Laboratories; Learning; Link; Manuals; Manufacturer Name; Maps; MeSH Thesaurus; Measures; Metabolism; Methodology; Modeling; Morbidity - disease rate; Nature; Numbers; Ontology; Pathway interactions; Patients; Pattern; Pharmaceutical Preparations; Pharmacodynamics; Pharmacologic Substance; Pharmacotherapy; Population; Population Research; Process; Product Labeling; Proteins; Proteome; Proteomics; Public Health; Purpose; Regulation; Representations, Knowledge (Computer); Research; Research Personnel; Resources; Running; Safety; Sampling; Source; Standards of Weights and Measures; Structure; System; Terminology; Testing; Time; Today; Translational Research; United States Department of Veterans Affairs; Vocabulary; Work; base; biochemical model; data integration; data modeling; drug standard; feeding; high throughput analysis; high throughput technology; information organization; interest; knowledge base; metabolomics; numb protein; prospective; research study

DESCRIPTION (provided by applicant): High throughput experiments are now frequently part of clinical trials and many institutions establish prospective biospecimen collections from routine patient populations in order to run proteomics and metabolomics experiments. Because these samples are collected from routine clinical populations with co-morbidities and on drug therapies, it is necessary to account for the drugs in the data analysis in a systematic manner. Increasingly multiple high-throughput technologies, such as proteomics and metabolomics, are being used together which requires comprehensive pathway networks that make changes in the metabolome traceable to the proteome and in turn to the gene expression profile. In this study we propose to link drug therapies found in the clinical data with biologic pathways data used for the integrated analysis of high-throughput experimental results. Specifically we will integrate formal knowledge of biological pathways with drug knowledge found in the emergent national standard for the comprehensive knowledge representation for medicinal products, including the Veteran Administrations National Drug File Reference Terminology (NDF-RT), the National Drug Codes and the RxNorm clinical drug vocabulary. The hub of these terminologies is the Structured Product Labeling (SPL) standard for drug knowledge representation. SPL is part of the HL7 version 3 standards and based on the HL7 Reference Information Model (RIM). All U.S. pharmaceutical manufacturers today submit SPL data to the Food and Drug Agency (FDA). Pharmacodynamic knowledge is represented in SPL as a using the NDF-RT mechanism of action (MoA) classes. In this project we will expand these MoA classes with their ontological definitions, and thus link to the biologic pathways described in various pathway network resources including KEGG, Reactome, and the NCI/Nature Protein Interaction Database (PID), all of which use different formats and models. Our methodology for integration consists of (1) transforming the original pathway resource into a common data schema, and (2) purpose-driven reconciliation of overlapping content, by (3) connecting the MoA classes. Because no single pathway data schema exists and because SPL is already the hub of the national federated drug terminology, we propose to integrate the pathway data into the drug knowledge base itself. The resulting integrated data will be evaluated against the frequency of drugs encountered in clinical care and research. This work will yield revisions of existing ontologies and, only where necessary, new ontologies to describe drug- pathway interactions. The project will demonstrate how the HL7/ISO Reference Information Model can represent biological entities and processes taking a realist perspective, and thus set an important precedence for cross-domain data integration between basic sciences and clinical medicine that is essential for the translational research agenda. PUBLIC HEALTH RELEVANCE: The project will combine the national drug catalog with models of the biochemical regulation and metabolism of body functions. This will allow researchers to understand better the effect which drugs have on the results of laboratory tests which measure a large number of proteins and chemicals in the body at the same time.

描述（由申请人提供）：高通量实验现在经常是临床试验的一部分，许多机构从常规患者人群中建立前瞻性生物标本采集，以进行蛋白质组学和代谢组学实验。由于这些样本是从患有合并症和接受药物治疗的常规临床人群中采集的，因此有必要以系统的方式说明数据分析中的药物。越来越多的高通量技术，如蛋白质组学和代谢组学，正在一起使用，这需要全面的途径网络，使代谢组的变化可追溯到蛋白质组，进而到基因表达谱。在这项研究中，我们建议将临床数据中发现的药物治疗与用于高通量实验结果综合分析的生物途径数据联系起来。具体而言，我们将整合生物学途径的正式知识与药品综合知识表示的紧急国家标准中的药物知识，包括退伍军人管理局国家药物文件参考术语（NDF-RT），国家药物代码和RxNorm临床药物词汇。这些术语的中心是用于药物知识表示的结构化产品标签（SPL）标准。SPL是HL 7第3版标准的一部分，基于HL 7参考信息模型（RIM）。今天，所有美国制药商都向食品和药物管理局（FDA）提交SPL数据。在SPL中，药效学知识表示为使用NDF-RT作用机制（MoA）类别。在这个项目中，我们将扩展这些MoA类与它们的本体论定义，从而链接到各种途径网络资源中描述的生物途径，包括KEGG，Reactome和NCI/Nature蛋白质相互作用数据库（PID），所有这些都使用不同的格式和模型。我们的整合方法包括（1）将原始途径资源转换为通用数据模式，以及（2）通过（3）连接MoA类，对重叠内容进行目的驱动的协调。因为没有单一的途径数据模式存在，因为SPL已经是国家联合药物术语的中心，我们建议将途径数据集成到药物知识库本身。将根据临床护理和研究中遇到的药物频率对所得综合数据进行评价。这项工作将产生对现有本体的修订，并且仅在必要时产生新的本体来描述药物途径相互作用。该项目将展示HL 7/ISO参考信息模型如何以现实主义的视角代表生物实体和过程，从而为基础科学和临床医学之间的跨领域数据集成奠定重要的优先地位，这对转化研究议程至关重要。公共卫生关系：该项目将联合收割机与国家药品目录和人体功能的生化调节和代谢模型相结合。这将使研究人员更好地了解药物对实验室测试结果的影响，这些测试同时测量体内大量的蛋白质和化学物质。