Precision Dosing for Critically Ill Children

危重儿童的精准给药

基本信息

批准号：
10384141
负责人：
Michael N. Neely
金额：
$ 72.58万
依托单位：
CHILDREN'S HOSPITAL OF LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-17 至 2026-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10384141
关键词：
Accounting Acute Renal Failure with Renal Papillary Necrosis Address Admission activity Algorithms Anti-Bacterial Agents Antibiotics Bacteremia Bacterial Infections Categories Cefazolin Characteristics Child Childhood Clindamycin Clinical Collection Computer software Computing Methodologies Creatinine Creatinine clearance measurement Critical Illness Critically ill children Data Set Differential Equation Dose Drug Design Drug Exposure Drug Kinetics Drug Modelings Drug Monitoring Drug Prescriptions Drug Targeting FDA approved Fiber Future Genus staphylococcus Hematologic Agents Hospitalized Child Hospitals In Vitro Individual Individuality Infection Kidney Kidney Failure Laboratories Laboratory Infection Learning Link Machine Learning Measurement Measures Methicillin Methicillin Resistance Methods Modeling Obstetric pharmacology Organism Outcome Patients Pediatric Intensive Care Units Pharmaceutical Preparations Pharmacodynamics Plasma Population Probability Process Recording of previous events Regimen Renal function Sepsis Series Serum Software Tools Staphylococcus aureus Statistical Methods Testing Therapeutic Time Toxic effect Training Translational Research United States Vancomycin Variant Voriconazole Weight Work clinically relevant drug development improved individualized medicine innovation inter-individual variation mathematical methods methicillin resistant Staphylococcus aureus novel novel strategies off-label use outcome prediction patient variability pediatric pharmacology pharmacodynamic model pharmacokinetic model population based predictive modeling predictive tools recurrent neural network severe injury therapy duration tool virtual

项目摘要

The drug development process and FDA-approved prescribing generally assume that patients are sufficiently stable and similar enough to justify population-based dosing for a given group that is usually unchanged during therapy. Unfortunately, there is a huge body of evidence that dosing according to this “one size fits all” paradigm results in wide variation in plasma drug concentrations between individuals and even within the same individual over time, all of which can compromise clinical outcomes. Population pharmacokinetic (PK) and pharmacodynamic (PD) models can control for this variability by providing clinicians with tools to adjust doses accordingly, a process that has come to be known as Model-Informed Precision Dosing (MIPD). However, MIPD has been better able to control for inter-individual variation rather than interoccasion variation (IOV) within an individual over time. MIPD methods exist to track IOV in the past, but not to account for possible future IOV. In this project we will address IOV in three novel approaches. Our first aim uses our unique Virtual Pediatric Intensive Care Unit (VPICU) dataset with >400 clinical variables obtained from ~20,000 unstable, critically ill children in our hospital since 2009. We will build recurrent neural networks (RNNs) to predict changes in renal function within individuals, which is relevant to the control of renally excreted drugs. While models exist to predict renal failure, this will be the first application of RNNs to predict creatinine clearance in children. There are >100,000 serum creatinine measurements to validate this work. Our second aim is to account for changing PK-PD in models that cannot be linked to a specific covariate like renal function. To do this, will incorporate stochastic differential equations (SDEs) to capture changes in model parameters over time. Unique to our work, we will apply SDEs in the setting of our long history of non-parametric PK-PD modeling, which makes no assumptions about underlying probability distributions for parameter values in a model and is particularly good at describing and controlling unusual patients, perfect for a critically ill population. We will use >40,000 vancomycin doses and >5,000 plasma concentrations in VPICU to test our algorithms. Our third aim is two-fold. First, we will again use RNNs to predict outcomes of VPICU patients with Staphylococcal bloodstream infections treated with vancomycin. We will compare RNNs that include vancomycin exposure estimated with IOV and without IOV. The second part is to use our in vitro hollow fiber infection model (HFIM) to directly assess the effect of vancomycin IOV on both methicillin-resistant and methicillin-susceptible Staphylococcus aureus in our laboratory. The HFIM can reproduce pediatric PK to measure antibacterial kill and emergence of less susceptible or persister organisms over days to weeks. Our inclusion of IOV in the HFIM is completely novel. We will deliver software tools to clinicians to control IOV and understand the magnitude relevant to outcomes of anti-Staphylococcal therapy.

药物开发过程和FDA批准的处方通常认为患者是足够稳定且足够相似，足以证明给定群体的基于人群的剂量合理治疗期间没有变化。不幸的是，有大量的证据据此给药 “一种尺寸适合所有”范式导致个体之间的血浆药物浓度变化甚至随着时间的流逝，所有这些都会损害临床结果。人口药代动力学（PK）和药效学（PD）模型可以通过提供这种变异性来控制这种变异性具有相应调整剂量的工具的临床医生，该过程已被称为模型信息精度给药（MIPD）。但是，MIPD能够更好地控制个人间变化而不是随着时间的流逝，而不是个体内部的变化（IOV）。 MIPD方法存在跟踪IOV 过去，但不考虑可能的未来IOV。在这个项目中，我们将在三本小说中向IOV讲话方法。我们的第一个目标使用我们独特的虚拟小儿重症监护室（VPICU）数据集，> 400 自2009年以来，我们医院的约20,000个不稳定的，重病的孩子获得的临床变量。我们将构建经常性神经网络（RNN）来预测个体内肾功能的变化，这就是与控制肾脏独家药物的控制有关。尽管存在模型来预测肾衰竭，但这将是 RNN的首次应用在儿童中预测肌酐清除率。有> 100,000个连续剧肌酐的测量以验证这项工作。我们的第二个目的是考虑更改PK-PD 无法链接到肾功能等特定协变量的模型。为此，将合并随机微分方程（SDE）捕获模型参数随时间的变化。我们独有的工作，我们将在我们的非参数PK-PD建模的悠久历史的环境中应用SDE，这没有对模型中参数值的潜在概率分布做出的假设特别擅长描述和控制异常患者，非常适合重症患者。我们将在VPICU中使用> 40,000个万古霉素剂量和> 5,000个血浆浓度来测试我们的算法。我们的第三目的是两倍。首先，我们将再次使用RNN来预测VPICU患者的结果用万古霉素治疗的葡萄球菌血液感染。我们将比较包括通过IOV估计的万古霉素暴露，没有IOV。第二部分是使用我们的体外空心纤维感染模型（HFIM）直接评估万古霉素IOV对两种耐甲氧西林的影响和我们实验室中的甲氧西林葡萄球菌金黄色葡萄球菌。 HFIM可以再现儿科 PK测量抗菌杀害和较易感性或持久生物的抗菌杀戮的出现几周。我们将IOV纳入HFIM是完全新颖的。我们将向临床医生提供软件工具控制IOV并理解与抗史黛利局疗法的结果相关的幅度。