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.

抽象的 小儿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有关的毒性风险。细胞色素P450占主导地位 药物代谢尚未在单个酶中作用，取决于遗传学，疾病状态，共同药物和 个体发育。不考虑这些差异会导致给药挑战和可能的毒性 报道了右美甲苯，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地建模来描述和预测药物代谢如何改变 孩子们。当前的药代动力学模型预测药物清除率如何随着年龄的影响而变化 剂量，但这些模型以两种方式受到限制； （1）他们需要实验确定的动力学数据 几种通常不可用的酶，（2）它们不模拟特定药物代谢产物的形成， 这对于预测毒性和药物相互作用很重要，无论清除是否随着 年龄。我们假设P450酶混合物的计算模型可以预测“代谢 命运”，即药物代谢和由此产生的代谢产物结构的动力学随着年龄的增长而变化。 我们提出构建层次数学模型，该模型首先预测由 代谢酶（AIM 1），然后是每种代谢物（动力学）（AIM 2）的形成效率和 结合这些模型以预测由P450年龄特异性混合物形成的代谢产物（AIM 3）。这个建议 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发布 可用于培养社区的改进和验证。其次，代谢物轮廓的模拟 将产生可拖动的生物标志物并支持有关可能依赖年龄的药物相互作用的研究 靶向生物学活性，促毒物激活和有毒物种的形成。第三，模型将指示， 对于新药物和现有药物，当代谢命运（因此是毒性和相互作用）发生变化时 小儿患者，即使药代动力学保持不变。第四，虽然不是主要目的 研究，成功建模代谢效率可以使药代动力学研究能够预测 在进行任何必要的实验动力学研究之前，有问题的小儿药物。在一起， 该建议为开发相关模型的基础奠定了坚实的基础，以解决优化药物的挑战 剂量和最小化儿童毒性风险。