Opioid Drug Ontology (ODO)

阿片类药物本体论 (ODO)

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要镇痛药是最常见的药物，阿片类止痛药是黄金标准用于严重的急性疼痛和许多慢性疼痛状况。人口患有慢性疼痛，近40％的老年人报告说使疼痛疼痛状况衰弱但是，不是由癌症引起的。呼吸道抑郁症限制了作为屁股杀手的有效性。在美国升级到危机。意外用药过量。迫切需要。我们建议开发阿片类药物本体论（ODO） - 旨在加速的综合知识基础并改善转化研究和药物发现计划的成功识别有效并节省阿片类药物。假设发展，例如通过连接化学结构，生化结合曲线，动物和药物副作用的药理学反应，从而实现更有效的理性药物发现程序。为了开发ODO，我们将利用我们在严重研究联盟领域进行广泛的以前的工作本体，数据标准，处理和集成方法以及软件系统以启用集成访问，查询和分析大型数据类型。当前的建议旨在证明ODO综合知识库的可行性并说明通过两个具体目标通过两个特定目的的概念证明：（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，可以集成各种数据类型，（2）部署ODO 集成数据门户和搜索引擎英语引擎委员会并展示其启发式价值。我们设想ODO将铺路以启用高级机器学习并链接从用阿片类镇痛药药理学和功能选择的分子模拟，从而促进，在大规模的，在药物开发源中的理性，预测设计和脚手架优化识别更安全的阿片类镇痛药。