A Time-Embedding Network Models the Ontogeny of 23 Hepatic Drug Metabolizing Enzymes.
A Time-Embedding Network Models the Ontogeny of 23 Hepatic Drug Metabolizing Enzymes.
复制标题
时间嵌入网络模拟 23 种肝脏药物代谢酶的个体发育。
DOI：
10.1021/acs.chemrestox.9b00223
复制
发表时间：
2019
影响因子：
4.1
通讯作者：
Swamidass,SJoshua
中科院分区：
文献类型：
--
作者：
Matlock,MatthewK;Tambe,Abhik;Elliott-Higgins,Jack;Hines,RonaldN;Miller,GroverP;Swamidass,SJoshua
儿童患者发生药物不良反应的风险较高，而且有关儿童药物安全性的信息不足。使儿童风险评估复杂化的是，药物的吸收、分布、代谢和消除存在许多年龄依赖性变化。年龄依赖性药物毒性风险的一个关键因素是药物代谢酶的个体发生，即从胎儿期到成年期整个发育过程中丰度和类型的变化。重要的是，这些变化不仅影响药物的总体清除率，还影响单个代谢物的暴露。在这项研究中，我们引入时间嵌入神经网络，以模拟人口水平的变化，代谢酶的表达作为年龄的函数。我们使用时间嵌入网络来模拟23种药物代谢酶的个体发育。时间嵌入网络概括了影响3A5表达的已知人口统计学因素。时间嵌入网络还有效地模拟了2D6表达的非线性动力学，比以前的工作更适合临床数据。相比之下，标准神经网络无法对3A5和2D6表达的这些特征进行建模。最后，我们结合联合收割机的时间嵌入模型的个体发育与额外的信息，以估计年龄依赖性变化的活性代谢物暴露。这种简单的方法确定了暴露于丙戊酸和美沙芬代谢物的年龄依赖性变化，并提出了丙戊酸毒性的潜在机制。这种方法可以帮助研究人员评估儿科人群中药物毒性的风险。
Pediatric patients are at elevated risk of adverse drug reactions, and there is insufficient information on drug safety in children. Complicating risk assessment in children, there are numerous age-dependent changes in the absorption, distribution, metabolism, and elimination of drugs. A key contributor to age-dependent drug toxicity risk is the ontogeny of drug metabolism enzymes, the changes in both abundance and type throughout development from the fetal period through adulthood. Critically, these changes affect not only the overall clearance of drugs but also exposure to individual metabolites. In this study, we introduce time-embedding neural networks in order to model population-level variation in metabolism enzyme expression as a function of age. We use a time-embedding network to model the ontogeny of 23 drug metabolism enzymes. The time-embedding network recapitulates known demographic factors impacting 3A5 expression. The time-embedding network also effectively models the nonlinear dynamics of 2D6 expression, enabling a better fit to clinical data than prior work. In contrast, a standard neural network fails to model these features of 3A5 and 2D6 expression. Finally, we combine the time-embedding model of ontogeny with additional information to estimate age-dependent changes in reactive metabolite exposure. This simple approach identifies age-dependent changes in exposure to valproic acid and dextromethorphan metabolites and suggests potential mechanisms of valproic acid toxicity. This approach may help researchers evaluate the risk of drug toxicity in pediatric populations.