Computationally modeling the impact of ontogeny on drug metabolic fate

计算模拟个体发育对药物代谢命运的影响

• 批准号: 9215358
• 负责人: GROVER P MILLER
• 金额: $ 32.35万
• 依托单位: UNIV OF ARKANSAS FOR MED SCIS
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9215358
• 关键词: Access to Information; Accounting; Affect; Age; Biological; Biological Assay; Biological Markers; Chemical Models; Chemistry; Child; Child Development; Childhood; Clinical; Communities; Computer Simulation; Computer software; Cytochrome P450; Data; Data Set; Databases; Development; Dextromethorphan; Dose; Drug Interactions; Drug Kinetics; Educational workshop; Enzyme Kinetics; Enzymes; Failure; Formulation; Fostering; Foundations; Goals; Growth; Hepatic; Hereditary Disease; In Vitro; Individual; Isoenzymes; Kinetics; Knowledge; Literature; Liver; Machine Learning; Metabolic; Metabolic Pathway; Metabolism; Microsomes; Midazolam; Modeling; Molecular Profiling; Molecular Structure; National Institute of Child Health and Human Development; Online Systems; Pattern; Pharmaceutical Preparations; Pharmacodynamics; Phenytoin; Prodrugs; Property; Reaction; Recombinants; Reporting; Risk; Role; Sampling; Site; Structure; Testing; Toxic effect; Toxin; Validation; Work; absorption; age related; base; computing resources; cost; data modeling; dosage; drug clearance; drug development; drug metabolism; drug structure; innovation; insight; mathematical model; novel; novel therapeutics; pediatric patients; pharmacokinetic model; predictive modeling; prospective; repository; research study; response; simulation; tool

ABSTRACT Advancements in pediatric drug development require innovative approaches that overcome challenges in assessing age-dependent drug disposition and toxic risks related to metabolism. Cytochromes P450 dominate drug metabolism yet roles for individual enzymes depend on genetics, disease states, co-medications, and ontogeny. Failure to account for those differences contributes to dosing challenges and possibly toxicity as reported for dextromethorphan, midazolam, and phenytoin. The NICHD Pediatric Formulation Initiative (PFI) workshops emphasized the need for better modeling to describe and predict how drug metabolism changes for children. Current pharmacokinetic models predict how drug clearance changes with age that affect the optimal dose, but those models are limited in two ways; (1) they require experimentally determined kinetic data for several enzymes that is not often available, and (2) they do not model formation of specific drug metabolites, which is important in predicting toxicity and drug interactions, regardless of whether clearance changes with age. We hypothesize that computational models of mixtures of P450 enzymes can predict how the “metabolic fate”, i.e. the kinetics of drug metabolism and the resulting metabolite structures, of drugs changes with age. We propose building hierarchical mathematical models that at first predict the drug metabolites formed by metabolic enzymes (Aim 1), then the efficiency of formation for each metabolite (the kinetics) (Aim 2), and combine these models to predict metabolites formed by age-specific mixtures of P450s (Aim 3). This proposal makes significant steps toward achieving PFI goals. First, datasets created for this study will be made publicly available to foster model refinement and validation by the community. Second, simulation of metabolite profiles would yield tractable biomarkers and support studies on possible age-dependent drug-drug interactions, off- target biological activities, pro-drug activation, and formation of toxic species. Third, the models will indicate, for both new and existing drugs, when metabolic fate (consequently, toxicity and interactions) changes in pediatric patients, even when pharmacokinetics stay the same. Fourth, though not the primary aim of this study, successfully modeling metabolic efficiency could enable pharmacokinetic studies for predicting problematic pediatric drugs prior to carrying out any necessary experimental kinetic studies. Taken together, this proposal lays a strong foundation for developing models relevant that resolve challenges in optimizing drug dosages and minimizing toxicity risks for children.

摘要 儿科药物开发的进步需要创新的方法来克服挑战， 评估年龄依赖性药物处置和与代谢相关的毒性风险。细胞色素P450占优势 药物代谢，但个别酶的作用取决于遗传学，疾病状态，合并用药， 个体发育未能解释这些差异导致给药挑战和可能的毒性， 报告了美沙芬、咪达唑仑和苯妥英。NICHD儿科配方倡议（PFI） 研讨会强调需要更好的建模来描述和预测药物代谢如何变化， 孩子目前的药代动力学模型预测了药物清除率如何随年龄变化，从而影响最佳药物代谢。 剂量，但这些模型在两个方面受到限制;（1）它们需要实验确定的动力学数据， 几种酶通常不可用，和（2）它们不模拟特定药物代谢物的形成， 这在预测毒性和药物相互作用方面很重要，无论清除率是否随 年龄我们假设，P450酶混合物的计算模型可以预测“代谢”是如何发生的。 药物的“命运”，即药物代谢的动力学和产生的代谢物结构，随年龄而变化。 我们建议建立分层数学模型，首先预测药物代谢物形成的 代谢酶（目标1），然后是每种代谢物的形成效率（动力学）（目标2），以及 联合收割机这些模型来预测由年龄特异性P450混合物形成的代谢物（目的3）。这项建议 朝着实现PFI目标迈出了重要一步。首先，为这项研究创建的数据集将公开 可用于促进社区对模型的改进和验证。第二，代谢物谱的模拟 将产生易于处理的生物标志物，并支持对可能的年龄依赖性药物相互作用的研究， 靶向生物活性、前药活化和有毒物质形成。第三，模型将表明， 对于新药和现有药物，当代谢命运（因此，毒性和相互作用）发生变化时， 儿科患者，即使药代动力学保持不变。第四，虽然不是主要目的， 研究，成功地建模代谢效率可以使药代动力学研究预测 在进行任何必要的实验动力学研究之前，综合起来看， 这一建议为开发相关模型奠定了坚实的基础， 剂量和最大限度地减少对儿童的毒性风险。