Computationally modeling the impact of ontogeny on drug metabolic fate

摘要 儿科药物开发的进步需要创新的方法来克服 评估与年龄相关的药物处置和与新陈代谢相关的毒性风险。细胞色素P450占主导地位 药物新陈代谢然而，个体酶的作用取决于遗传、疾病状态、联合用药和 个体发育学。未能考虑到这些差异会造成服药挑战，并可能产生毒性，因为 报告右美沙芬、咪达唑仑和苯妥英钠。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 孩子们。目前的药代动力学模型预测药物清除量如何随年龄变化而影响最佳 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 几种通常不常见的酶，以及(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，无论清除量是否随着 年龄。我们假设，P450酶混合物的计算模型可以预测 药物的命运“，即药物代谢动力学和由此产生的代谢物结构，随着年龄的变化而变化。 我们建议建立分层的数学模型，首先预测由 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 结合这些模型来预测特定年龄的P450混合物形成的代谢物(目标3)。这项建议 朝着实现PFI目标迈出了重要的一步。首先，将公开为该研究创建的数据集 可用于促进社区对模型的改进和验证。第二，代谢物分布的模拟 将产生易于处理的生物标志物，并支持对可能与年龄相关的药物-药物相互作用的研究。 靶向生物活性、前药物激活和有毒物种的形成。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)改变时 儿科患者，即使药代动力学保持不变。第四，尽管这不是主要目的 研究，成功地对代谢效率进行建模可以使药物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，先给出有问题的儿科药物。加在一起， 这一建议为开发相关模型奠定了坚实的基础，以解决优化药物方面的挑战 剂量和将儿童的毒性风险降至最低。

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.