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

基本信息

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
项目类别：
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

项目摘要

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.

概括药物相互作用（DDI）和药物遗传学（PG）是药物不良事件（ADE）的主要原因，四分之一的患者经历 ADE 归因于 DDI 或 PG 然而，尽管有内在的原因。由于其药理机制的联系，DDI 和 PG 经常被分开研究。迫切需要从 DDI 到 PG 研究以及新开发的更高效的转化机器学习（ML）和人工智能（AI）方法使此类研究变得可行。 DDI 对 2500 万条 PubMed 摘要进行知识发现研究，我们使用了 ML 和自然语言处理首次分析鉴定出 986 个具有重叠药代动力学机制和临床的 DDI 对证据，我们从中产生了 137 个关于 CYP2D6 和 CYP3A 的新 PG 假设。提案中，我们将开发新颖的机器学习方法，包括允许人类注释者进行主动学习依赖于逻辑规则来表示药理学的参与和知识库推理该提案有三个目标：（1）开发一种主动学习方法来执行 DDI 和 PG。 (2) 开发联合信息提取和知识- 基础推理方法从文献中进行 DDI 和 PG 信息提取分析；以及 (3) (a) 检查 CYP3A/CYP2C19 基因多态性是否与奥美拉唑诱导的肌病，以及 (b) 制定优先顺序方案来检查从肌病产生的新 PG 假设使用范德比尔特大学的 BioVU 生物库对目标 1 和 2 进行基于文献的发现分析。研究结果将为更广泛的科学界进行潜在的前瞻性研究和为精准医疗和临床护理的改善做出重大贡献。