Machine learning drives translational research from drug interactions to pharmacogenetics

机器学习推动从药物相互作用到药物遗传学的转化研究

• 批准号: 10608598
• 负责人: You Chen
• 金额: $ 63.34万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2028-02-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10608598
• 关键词: Active Learning; Adverse drug event; Adverse event; Alleles; Antidepressive Agents; Applications Grants; Big Data; Breast Cancer therapy; CYP2C19 gene; CYP2D6 gene; CYP3A4 gene; Clinical; Communities; Computerized Medical Record; Data; Development; Disease-Free Survival; Drug Interactions; Drug Kinetics; Enzyme Induction; Enzyme Inhibition; Enzymes; Feedback; Generations; Genes; Genetic Polymorphism; Human; Information Retrieval; Investigation; Joints; Knowledge; Knowledge Discovery; Literature; Machine Learning; Metabolism; Methodology; Methods; Myopathy; Natural Language Processing; Nested Case-Control Study; Omeprazole; Paper; Patients; Performance; Pharmaceutical Preparations; Pharmacogenetics; Prospective Studies; PubMed; Publishing; Research; Resources; Retrieval; Sampling; Scheme; Selective Serotonin Reuptake Inhibitor; Tamoxifen; Testing; Time; Translational Research; Translations; Transportation; Universities; Variant; Work; artificial intelligence method; biobank; clinical care; design; drug metabolism; feasibility research; genetic variant; hormone therapy; improved; inhibitor; innovation; interest; knowledge base; knowledgebase; loss of function; machine learning algorithm; machine learning method; malignant breast neoplasm; novel; pharmacogenetic testing; pharmacologic; precision medicine; tumor

Summary Drug-drug interactions (DDIs) and pharmacogenetics (PG) are leading causes of adverse drug events (ADEs), with one in four patients experiencing ADEs attributable to DDIs or PG. However, despite the intrinsic connection of their pharmacological mechanisms, DDI and PG are often studied separately. There is a significant need for more efficient and effective translational from DDI to PG research, and newly developed machine-learning (ML) and artificial-intelligence (AI) methods have made such research feasible. In our recent DDI knowledge-discovery study of 25 million PubMed abstracts, we used ML and natural-language-processing analyses for the first time to identify 986 DDI pairs with overlapping pharmacokinetic mechanisms and clinical evidence, from which we generated 137 new PG hypotheses regarding CYP2D6 and CYP3A. In this grant proposal, we will develop novel ML methods, including active learning that will allow human annotator involvement and knowledge base reasoning that relies on logical rules to represent pharmacological mechanisms. This proposal has three aims: (1) to develop an active-learning approach to perform DDI and PG information retrieval analysis from the literature; (2) to develop a joint information-extraction and knowledge- base-reasoning approach to perform DDI and PG information extraction analysis from the literature; and (3) (a) to examine whether CYP3A/CYP2C19 genetic polymorphisms are associated with omeprazole-induced myopathy, and (b) to develop a prioritization scheme to examine new PG hypotheses generated from the literature-based discovery analyses from Aims 1 and 2 using Vanderbilt University’s BioVU biobank. These PG findings will provide a valuable resource for the wider scientific community for potential prospective studies and contribute significantly to the improvement of precision medicine and clinical care.

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