Precision Dosing for Critically Ill Children

危重儿童的精准给药

• 批准号: 10685247
• 负责人: Michael N. Neely
• 金额: $ 71.88万
• 依托单位: CHILDREN'S HOSPITAL OF LOS ANGELES
• 依托单位国家: 美国
• 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/10685247
• 关键词: Accounting; Acute Renal Failure with Renal Papillary Necrosis; Admission activity; Algorithms; Anti-Bacterial Agents; Antibiotics; Bacteremia; Bacterial Infections; Blood; Categories; Cefazolin; Characteristics; Child; Childhood; Clindamycin; Clinical; Collection; Compensation; 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; Excretory function; FDA approved; Fiber; Future; Genus staphylococcus; 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; Predisposition; Probability; Process; Qualifying; 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; adverse drug reaction; 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; pharmacokinetics and pharmacodynamics; population based; predictive modeling; predictive tools; recurrent neural network; severe injury; therapy duration; tool; virtual

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 批准的处方通常假设患者 足够稳定和相似，足以证明针对特定群体的基于人群的剂量通常是合理的 不幸的是，有大量证据表明按照此剂量给药。 “一刀切”范式导致个体之间血浆药物浓度存在很大差异 即使在同一个人身上，随着时间的推移，所有这些都会损害临床结果。 药代动力学 (PK) 和药效学 (PD) 模型可以通过提供来控制这种变异性 相应地调整剂量的工具受到青睐，这一过程被称为“模型知情” 精密剂量 (MIPD) 然而，MIPD 能够更好地控制个体间的差异。 MIPD 方法不是跟踪个体随时间变化的 IOV 方法。 过去，但不考虑未来可能的 IOV 在这个项目中，我们将在三个小说中解决 IOV。 我们的第一个目标是使用我们独特的虚拟儿科重症监护病房 (VPICU) 数据集，其中包含超过 400 个数据。 自 2009 年以来，我们从我们医院约 20,000 名不稳定的危重儿童中获得了临床变量。我们将 建立循环神经网络（RNN）来预测个体肾功能的变化，即 与肾排泄药物的控制相关，虽然存在预测肾衰竭的模型，但这将是。 首次应用 RNN 预测儿童肌酐清除率 有 >100,000 份血清。 我们的第二个目标是解释 PK-PD 的变化。 无法与特定协变量（如肾功能）联系起来的模型将纳入其中。 随机微分方程 (SDE) 用于捕获模型参数随时间的变化，这是我们独有的。 工作中，我们将在我们悠久的非参数 PK-PD 建模历史中应用 SDE，这 不对模型中参数值的潜在概率分布做出任何假设，并且是 特别擅长描述和控制异常患者，非常适合重症患者。 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。 第三个目标有两个：首先，我们将再次使用 RNN 来预测 VPICU 患者的结果。 我们将比较使用万古霉素治疗的葡萄球菌血流感染。 使用 IOV 和不使用 IOV 估算万古霉素暴露量 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对甲氧西林耐药的影响 我们实验室的 HFIM 可以繁殖对甲氧西林敏感的金黄色葡萄球菌。 PK 用于测量几天内抗菌剂的杀灭情况以及较不敏感或持久性微生物的出现情况 我们将 IOV 纳入 HFIM 是完全新颖的，我们将向叛乱分子提供软件工具。 控制 IOV 并了解与抗葡萄球菌治疗结果相关的程度。