DATA AND TOOLS FOR MODELING METABOLISM AND REACTIVITY

用于模拟代谢和反应性的数据和工具

• 批准号: 9006922
• 负责人: GROVER P MILLER
• 金额: $ 35.11万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-01 至 2020-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9006922
• 关键词: Accounting; Adverse reactions; Algorithms; Assessment tool; Binding Proteins; Biological; Biological Process; Cell Death; Cessation of life; Chemical Models; Chemicals; Clinical; Computer Simulation; Data; Data Set; Databases; Development; Enzymes; Event; Exanthema; Exposure to; Foundations; Health; Hepatotoxicity; Hypersensitivity; Immune; Immune response; Knowledge; Lead; Length of Stay; Literature; Liver; Machine Learning; Mediating; Medicare; Medicine; Metabolic; Metabolic Activation; Metabolism; Methodology; Methods; Modeling; Molecular; Molecular Profiling; Molecular Structure; Patients; Pattern; Pharmaceutical Preparations; Physiological; Process; Property; Proteins; Publishing; Reaction; Relative Risks; Research Personnel; Risk; Role; Series; Stevens-Johnson Syndrome; Structure; Testing; Time; Toxic effect; Validation; Withdrawal; adduct; data modeling; drug candidate; drug development; drug metabolism; drug withdrawal; gene function; immunogenic; immunogenicity; improved; in vitro Assay; interdisciplinary approach; liver metabolism; macromolecule; mathematical model; model building; novel; online repository; predictive modeling; protein function; quantum; repository; research study; screening; tool

 DESCRIPTION (provided by applicant): Adverse drug reactions (ADRs) are dangerous and expensive, afflicting about 1.5% of hospitalized patients with profound health and financial consequences. In Medicare patients alone, adverse drug reactions account for 19% of total spending ($339 billion), more than 1,900 deaths, and more than 77,000 extra hospital days per year. Idiosyncratic ADRs, especially rare and severe hypersensitivity-driven ADRs, are the leading cause of medicine withdrawal and termination of clinical development. At the same time, a large proportion of drugs are not associated with hypersensitivity driven ADRs, offering hope that new medicines could avoid them entirely with reliable predictors of risk. Hypersensitivity driven ADRs are caused by the formation of chemically reactive metabolites by metabolic enzymes. These reactive metabolites covalently attach to proteins to become immunogenic and provoke an ADR. Unfortunately, current computational and experimental approaches do not reliably identify drug candidates that form reactive metabolites. These approaches are limited because they inadequately model metabolism, which can both render toxic molecules safe and safe molecules toxic. To overcome this limitation, the proposed study aims to curate a public database of metabolism and reactivity and use this database to build accurate and validated mathematical models of metabolism and reactivity. The models will be constructed using machine-learning algorithms that quantitatively summarize the knowledge from thousands of published studies. The Aims are to (1) curate a database of metabolism and build models that identify rules governing the structure of reaction products during drug metabolism in the liver, (2) curate a database of reactivity and build improved reactivity models that mechanistically predict which metabolites are reactive with biological molecules, and (3) curate a database of reactive metabolites and combine these models to predict when molecules form reactive metabolites that covalently bind proteins. The computational models generated by these Aims will be validated through statistical approaches and against bench-top experiments. Taken together, this approach will substantially improve on existing approaches by more accurately modeling the properties determining whether metabolism renders drugs toxic or safe. The predictive models will make new medicines safer by helping researchers avoid molecules prone to ADRs without harming patients.

 描述(由申请人提供)： 药品不良反应(ADR)是危险和昂贵的，约1.5%的住院患者受到影响，造成严重的健康和经济后果。仅在医疗保险患者中，药物不良反应就占总支出的19%(3390亿美元)，超过1900人死亡，每年额外住院天数超过7.7万天。特殊的不良反应，尤其是罕见和严重的超敏反应引起的不良反应，是导致停药和终止临床开发的主要原因。与此同时，很大一部分药物与超敏反应导致的不良反应无关，这为新药提供了希望，即通过可靠的风险预测，可以完全避免它们。由超敏反应引起的不良反应是由代谢酶形成化学反应的代谢物引起的。这些反应性代谢物共价附着在蛋白质上，成为免疫原性并引发ADR。不幸的是，目前的计算和实验方法不能可靠地识别形成反应性代谢物的候选药物。这些方法是有限的，因为它们没有对新陈代谢进行充分的建模，新陈代谢既可能使有毒分子安全，也可能使安全分子有毒。为了克服这一限制，拟议的研究旨在建立一个关于新陈代谢和反应性的公共数据库，并使用该数据库来建立准确和有效的新陈代谢和反应性数学模型。这些模型将使用机器学习算法来构建，这些算法量化地总结了数千项已发表研究的知识。其目的是(1)建立代谢数据库并建立模型，以确定在肝脏药物代谢期间控制反应产物结构的规则；(2)管理反应性数据库并建立改进的反应性模型，从机械上预测哪些代谢物与生物分子发生反应；以及(3)管理反应性代谢物数据库并结合这些模型来预测分子何时形成与蛋白质共价结合的反应性代谢物。由这些AIMS生成的计算模型将通过统计方法并对照台式实验进行验证。综上所述，这种方法将通过更准确地模拟决定新陈代谢是否使药物有毒或安全的性质，大大改进现有的方法。这些预测模型将帮助研究人员在不伤害患者的情况下避免易发生ADR的分子，从而使新药更加安全。