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（DDIS）和药物遗传学（PG）是不良药物事件（ADE）的主要原因，归因于DDIS或PG的四分之一患者中有四分之一的患者。但是，执行固有的通常对其药物机制，DDI和PG的连接进行分开研究。有一个对从DDI到PG研究的更有效翻译的巨大需求，并新开发机器学习（ML）和人工智能（AI）方法使此类研究可行。在我们的最新消息中 DDI知识发现的2500万PubMed摘要研究，我们使用了ML和自然语言处理首次与重叠的药代机制和临床的分析首次识别986个DDI对证据，我们从中产生了137个有关CYP2D6和CYP3A的新PG假设。在这笔赠款中提案，我们将开发新颖的ML方法，包括积极学习，以允许人类注释者参与和知识基础推理依赖于逻辑规则代表药物机制。该提议具有三个目标：（1）开发一种主动学习方法来执行DDI和PG 文献中的信息检索分析；（2）开发联合信息萃取和知识 - 从文献中执行DDI和PG信息提取分析的碱性方法；（3）（a）检查CYP3A/CYP2C19遗传多态性是否与奥美拉唑诱导的肌病和（b）制定优先级方案，以检查从该方案中产生的新PG假设基于文学的发现分析了AIM 1和2使用Vanderbilt University的Biovu Biobank。这些pg 调查结果将为更广泛的科学界提供潜在的前瞻性研究和为改善精度医学和临床护理做出了重大贡献。