DATA AND TOOLS FOR MODELING METABOLISM AND REACTIVITY

 描述（由申请人提供）： 药物不良反应（ADR）是危险和昂贵的，困扰着约1.5%的住院患者，并带来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），每年有1，900多人死亡，超过77，000个额外住院日。特异质ADR，特别是罕见和严重的超敏反应导致的ADR，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驱动的ADR无关，这为新药完全避免它们提供了可靠的风险预测指标。超敏反应驱动的ADR是由代谢酶形成化学反应性代谢物引起的。这些反应性代谢物共价连接到蛋白质上，成为免疫原性物质并引发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，因为它们不能充分模拟代谢，这既可以使有毒分子安全，也可以使安全分子有毒。为了克服这一局限性，拟议的研究旨在策划一个代谢和反应性的公共数据库，并使用该数据库建立准确和有效的代谢和反应性数学模型。这些模型将使用机器学习算法构建，这些算法定量总结了数千项已发表研究的知识。目的是（1）管理代谢数据库并建立模型，其识别在肝脏中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，其机械地预测哪些代谢物与生物分子反应，以及（3）管理活性代谢物的数据库，并将这些模型联合收割机组合以预测分子何时形成共价结合蛋白质的活性代谢物。这些目标产生的计算模型将通过统计方法和实验室实验进行验证。总之，这种方法将大大改善现有的方法，更准确地建模的属性，确定是否代谢使药物有毒或安全。预测模型将通过帮助研究人员避免容易发生ADR的分子而使新药更安全，而不会伤害患者。

 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.