Opioid Drug Ontology (ODO)

阿片类药物本体论 (ODO)

• 批准号: 9895053
• 负责人: Stephan C Schurer
• 金额: $ 23.03万
• 依托单位: UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9895053
• 关键词: Acute Pain; Adverse drug effect; Analgesics; Anatomy; Animals; Behavioral; Binding; Biochemical; Biological; Brain; Cessation of life; Characteristics; Chemical Structure; Clinical; Communities; Complex; Crystallization; Data; Data Sources; Databases; Dependence; Development; Drug Design; Effectiveness; Elderly; FAIR principles; Failure; G-Protein-Coupled Receptors; Gene Expression; Genetic; Genome; Goals; Gold; Human; Hybrids; In Vitro; Knowledge; Knowledge Discovery; Libraries; Ligands; Link; Machine Learning; Maps; Metadata; Methods; Modeling; Molecular; Molecular Conformation; Mutagenesis; Narcotics; Network-based; Neuropharmacology; Ontology; Opioid; Opioid Analgesics; Opioid Receptor; Opioid Receptor Binding; Overdose; Pain; Pharmaceutical Preparations; Pharmacology; Phenotype; Physical Dependence; Population; Process; Program Development; Publications; Receptor Signaling; Reporting; Research; Research Personnel; Resolution; Risk; Scientist; Semantics; Signal Pathway; Signal Transduction; Structure; System; Tissues; Translational Research; United States; Ventilatory Depression; Work; addiction; base; chronic pain; chronic painful condition; clinical pain; cloud based; computer framework; computerized data processing; data analysis pipeline; data harmonization; data portal; data standards; design; diverse data; drug development; drug discovery; experimental study; heuristics; improved; in vivo; journal article; knowledge base; medical attention; mu opioid receptors; novel; novel therapeutics; opioid epidemic; opioid mortality; opioid overdose; overdose death; predictive modeling; prescription opioid; programs; receptor; response; scaffold; screening; search engine; side effect; simulation; small molecule; software development; software systems; structured data; success; tool

PROJECT SUMMARY Analgesics are among the most commonly prescribed medications, and opioid painkillers are the gold standard for the management of severe acute pain, and for many chronic pain conditions. More than 30% of the U.S. population suffers from chronic pain, and nearly 40% of older adults report debilitating chronic pain conditions not caused by cancer. However, side effects of opioids, including tolerance, physical dependence, and respiratory depression have limited their effectiveness as pain killers. Rates of addiction and opioid overdose have escalated to a point of crisis. In the United States, on average approximately 115 people die every day from accidental overdose. Better, efficacious and safe opioid analgesic drugs with reduced risk of use are urgently needed. We propose to develop the Opioid Drug Ontology (ODO) – an integrated knowledgebase aimed at accelerating and improving the success of translational research and drug discovery programs towards the identification of efficacious and save opioid drugs. ODO will enable multi-tiered analyses across diverse data types and hypothesis development for example by connecting chemical structure, biochemical binding profiles, pharmacological responses in animals and drug side effects and thus enable more effective rational drug discovery programs. To develop ODO we will leverage our extensive previous work in several research consortia developing formal ontologies, data standards, processing and integration methods, and software systems to enable integrated access, query and analysis of large scale and diverse data types. The current proposal aims to demonstrate the feasibility of the ODO integrated knowledgebase and illustrate proof of concept via two Specific Aims: (1) to curate and harmonize ODO content from diverse data sources via a semantic knowledge model enabling integration of diverse data types, and (2) to deploy the ODO integrated Data Portal and Search Engine engaging the community and demonstrate its heuristic value. We envision that the ODO will pave the way to enable advanced machine learning and link results from molecular simulations with opioid analgesic drug pharmacology and functional selectivity, thus facilitating, at larger scale, the rational, predictive design, and scaffold optimization in drug development efforts towards identifying safer opioid analgesics.

项目概要 镇痛药是最常用的处方药物之一，阿片类止痛药是金标准 用于治疗严重急性疼痛和许多慢性疼痛。美国30%以上 人口患有慢性疼痛，近 40% 的老年人报告患有慢性疼痛 不是由癌症引起的。然而，阿片类药物的副作用，包括耐受性、身体依赖性和 呼吸抑制限制了它们作为止痛药的有效性。成瘾率和阿片类药物过量 已经升级到危机的地步。在美国，平均每天约有 115 人死亡 意外服用过量。更好、更有效、更安全、降低使用风险的阿片类镇痛药是 急需。 We propose to develop the Opioid Drug Ontology (ODO) – an integrated knowledgebase aimed at accelerating 并提高转化研究和药物发现计划的成功率，以识别 有效并节省阿片类药物。 ODO 将支持跨不同数据类型的多层分析 假设的发展，例如通过连接化学结构、生化结合谱、 动物药理反应和药物副作用，从而使更有效的合理药物 发现计划。 为了开发 ODO，我们将利用我们之前在多个研究联盟中所做的大量工作来开发正式的 ODO 本体、数据标准、处理和集成方法以及软件系统，以实现集成 大规模、多样化数据类型的访问、查询和分析。 当前提案旨在论证 ODO 集成知识库的可行性并说明 通过两个具体目标进行概念验证：(1) 策划和协调来自不同数据源的 ODO 内容 通过语义知识模型实现不同数据类型的集成，以及 (2) 部署 ODO 集成的数据门户和搜索引擎吸引社区参与并展示其启发价值。 我们预计 ODO 将为实现高级机器学习和链接结果铺平道路 阿片类镇痛药物药理学和功能选择性的分子模拟，从而促进 药物开发工作中的更大规模、合理的、预测性的设计和支架优化 确定更安全的阿片类镇痛药。