Investigation of Drug-Drug and Drug-Circuit Interactions in Children on Continuous Renal Replacement Therapy

项目总结/摘要 连续性肾脏替代治疗（CRRT）是一种透析方式，可挽救急性肾病儿童的生命 损伤尽管如此，接受CRRT支持的儿童仍处于高死亡风险中，死亡率 超过40%。这种高死亡率被认为部分是由于药物暴露的改变。变异药物 暴露发生于1）由于给予多种药物引起的药物间相互作用; 2）多器官 功能障碍;和3）直接药物相互作用和/或通过CRRT回路清除药物。因此，对于大多数 药物，CRRT儿童的最佳剂量未知。 本研究的目的是确定CRRT儿童中5种常用药物的最佳剂量。在AIM 1中 我们将通过将五种药物单独和一起注射到 隔离的闭环CRRT回路。在AIM 2中，我们将建立基于生理学的药代动力学（PBPK） 模型来预测儿童的最佳药物剂量。PBPK模型是计算模型，其中身体是 表示为由血流连接的一组虚拟器官隔室。数学方程表征 药物通过虚拟器官时药物浓度的变化。这些机械模型可以 考虑生理协变量（如年龄和疾病）的影响，并纳入药物- 药物相互作用重要的是，我们可以使用AIM 1的数据在PBPK模型中构建CRRT“器官”， 说明CRRT对药物给药的影响。最后在AIM 3中，我们将收集预期的药物浓度 接受CRRT和一种或多种选定药物治疗的儿童的数据。我们将比较观察到的 将这些儿童的浓度数据与PBPK模型预测的浓度数据进行比较， 并对PBPK模型进行了验证。我们将使用最终的PBPK模型来预测不同条件下的最佳剂量。 如药物联合给药和不同CRRT设置等情况。 本研究将确定常见药物的PK和联合给药药物之间的相互作用， 接受CRRT治疗的儿童。结果可以直接转化到床旁，提高安全性和有效性， 用于CRRT重症儿童的药物。培训将在犹他州大学举行， PBPK建模方面的领先专家的指导。通过我的培训计划，我将培养必要的技能 作为一名独立的研究科学家，包括必要的技术技能，完成 药理学/药代动力学课程，以及数据和结果的有效交流。

PROJECT SUMMARY/ABSTRACT Continuous renal replacement therapy (CRRT), a form of dialysis, is life saving for children with acute kidney injury. Despite this, children supported with CRRT are at still at high risk for death, with mortality rates exceeding 40%. This high mortality is thought to result in part from altered drug exposure. Altered drug exposure occurs from 1) drug-drug interactions due to the administration of multiple drugs; 2) multi-organ dysfunction; and 3) direct drug interaction and/or removal of drug by the CRRT circuit. As a result, for most drugs, optimal dosing in children on CRRT is unknown. The goal of this study is to determine optimal dosing of 5 commonly used drugs in children on CRRT. In AIM 1 we will determine how drugs interact with the CRRT machines by injecting five drugs singly and together into isolated, closed-loop CRRT circuits. In AIM 2 we will build physiologically based pharmacokinetic (PBPK) models to predict optimal drug dosing in children. PBPK models are computational models in which the body is represented as a set of virtual organ compartments linked by blood flow. Mathematical equations characterize changes in drug concentrations as the drug passes through the virtual organs. These mechanistic models can account for the impact of physiologic covariates such as age and disease and incorporate the impact of drug- drug interactions. Importantly, we can use the data from AIM 1 to build a CRRT “organ” in the PBPK model to account for the impact of CRRT on drug dosing. Finally in AIM 3 we will collect prospective drug concentration data from children who are on CRRT and one or more of the selected drugs. We will compare the observed concentration data from these children with the PBPK model-predicted concentration data in order to refine and validate the PBPK model. We will use the final PBPK model to predict optimal dosing under different scenarios such as drug co-administration and different CRRT settings. This study will determine the PK of common medications and interactions between drugs co-administered in children on CRRT. Results can be directly translated to the bedside and improve safety and effectiveness of drugs used in critically ill children on CRRT. Training will take place at the University of Utah under the mentorship of a leading expert in PBPK modeling. Through my training plan, I will develop the necessary skills for a career as an independent research scientist including the necessary technical skillset, completing coursework in pharmacology/pharmacokinetics, and the effective communication of data and results.