Opioid Drug Ontology (ODO)

点击翻译按钮获取中文摘要

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.