Precision Dosing for Critically Ill Children

危重儿童的精准给药

• 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
• 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

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，足以证明对给定组的基于人群的给药是合理的， 在治疗期间保持不变。不幸的是，有大量的证据表明，根据这一点给药 “一刀切”模式导致个体间血浆药物浓度差异很大 甚至在同一个人体内，所有这些都会影响临床结果。人口 药代动力学（PK）和药效学（PD）模型可以通过提供 临床医生有工具来相应地调整剂量，这一过程被称为模型知情（Model-Informed） 精密定量（MIPD）。然而，MIPD能够更好地控制个体间的变异 而不是个体内随时间推移的病例间变异（IOV）。存在MIPD方法来跟踪IOV 但不考虑未来可能的IOV。在这个项目中，我们将解决IOV在三个小说 接近。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， 自2009年以来，从我院约20，000名不稳定的危重儿童中获得的临床变量。我们将 建立递归神经网络（RNN）来预测个体内肾功能的变化， 与控制肾排泄药物有关。虽然存在预测肾衰竭的模型，但这将是一个新的趋势。 RNN首次应用于预测儿童肌酐清除率。有> 10万血清 肌酸酐测量来验证这项工作。我们的第二个目标是考虑到改变PK-PD在 不能与特定协变量（如肾功能）关联的模型。为此，将 随机微分方程（SDEs）来捕捉模型参数随时间的变化。我们的独特之处 工作中，我们将在我们的非参数PK-PD建模的悠久历史的背景下应用SDES， 对模型中参数值的潜在概率分布不做任何假设， 特别擅长描述和控制不寻常的病人，非常适合重症人群。我们 将在VPICU中使用> 40，000次万古霉素剂量和> 5，000次血浆浓度来测试我们的算法。我们 第三个目标是双重的。首先，我们将再次使用RNN来预测VPICU患者的结局， 万古霉素治疗葡萄球菌血流感染。我们将比较RNN，包括 使用IOV和不使用IOV估计的万古霉素暴露量。第二部分是利用我们的试管空心 纤维感染模型（HIBR），以直接评估万古霉素IOV对甲氧西林耐药 和甲氧西林敏感金黄色葡萄球菌。阻碍者可以复制小儿 PK，用于测量抗菌剂杀灭和在数天内出现较不敏感或持久的微生物， 周我们将IOV包含在障碍中是完全新颖的。我们将为临床医生提供软件工具 控制IOV并了解与抗葡萄球菌治疗结局相关的程度。