Population Pharmacokinetic Modeling and Dual Optimal Control

群体药代动力学建模和双重最优控制

• 批准号: 8534786
• 负责人: Michael N. Neely
• 金额: $ 43.18万
• 依托单位: CHILDREN'S HOSPITAL OF LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-06-15 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8534786
• 关键词: Adult; Affect; Algorithms; Americas; Bayesian Method; Blood; Blood specimen; Caring; Child; Childhood; Clinical; Clinical Research; Communicable Diseases; Communities; Computer software; Concentration measurement; Data; Data Quality; Discipline; Disease; Dose; Drug Design; Drug Exposure; Drug Kinetics; Drug Labeling; Engineering; Environment; Evolution; Future; Goals; Guidelines; Human; Individual; Intravenous; Laboratories; Learning; Length of Stay; Mathematics; Maximum Tolerated Dose; Measurement; Medical; Medicine; Methods; Metric; Modeling; Monitor; Noise; Normal Statistical Distribution; Outcome; Patients; Pharmaceutical Preparations; Pharmacology; Phase; Population; Probability; Process; Quality Control; Quality of Care; Regimen; Reliance; Research Design; Research Personnel; Residual state; Sampling; Schedule; Scheme; Serum; Simulate; Societies; Software Design; Software Tools; Source; Study Subject; Techniques; Testing; Therapeutic; Therapeutic Agents; Time; Toxic effect; Twin Multiple Birth; Update; Validation; Vancomycin; Work; cancer transplantation; clinical effect; clinical practice; computer science; cost; data modeling; design; dosage; improved; indexing; innovation; novel; novel strategies; pharmacokinetic model; programs; prospective; response; software development; statistics; tool

DESCRIPTION (provided by applicant): For therapy with potentially toxic drugs, such as those used in infectious diseases, transplantation, and cancer, the medical community routinely accepts enormous interpatient variability in drug exposure that would be totally unacceptable in any other scientific or industrial discipline. One usually gives "one size fits all" standard therapy and monitors for lack of clinical effect or toxicity. Instead, a clinician can plan in advance to maximize effect and minimize toxicity by setting individualized clinical targets of serum drug concentration or effect for each patient, and then calculating doses to hit the desired targets with the greatest precision. For example, maximum aposteriori probability (MAP) Bayesian approaches have achieved at least partial control of interpatient drug exposure. They have improved quality of care, reduced complications, shortened hospital stay, and lowered costs. However, MAP Bayesian methods are unfamiliar to most clinicians, and they manage only the single most likely version of the patient. They must assume that the proposed dosage regimen will hit the target exactly. They have no way to evaluate, control, and minimize the error with which the target can be hit in the real world. Our project has four aims. 1) We will truly optimize patient drug exposure by combining, for the first time, control of the dose and the timing of the serum concentration measurements (active dual control) in our multiple model (MM) Bayesian software designed for use by clinicians. 2) To more accurately reflect reality, we will further update the software to estimate process noise in the therapeutic environment (e.g. errors in dose timing) separately from measurement noise. We will evaluate this as an objective index of quality for pharmacokinetic studies. 3) To directly test the clinical value of our methods, we will prospectively compare the percentage of patients having therapeutic vancomycin serum concentrations after current standard dosing, after dosing with our software in both its current state and after it is updated with the capabilities proposed here. Secondary endpoints will include therapeutic outcomes and costs. 4) Finally, we propose to extend our goal-oriented MM to explore the dose-response relationship of a new drug after it is given to humans for the first time in phase I/II studies, with the twin goals of defining effective, safe doses in the shortest time and fewest patients.

描述（由申请人提供）：对于使用潜在毒性药物的治疗，例如用于感染性疾病、移植和癌症的药物，医学界通常接受药物暴露的巨大患者间差异，这在任何其他科学或工业学科中都是完全不可接受的。人们通常给出“一刀切”的标准治疗，并监测缺乏临床效果或毒性。相反，临床医生可以提前计划，通过为每个患者设定血清药物浓度或效果的个体化临床目标，然后计算剂量以最大精度达到所需目标，从而最大限度地提高效果并最小化毒性。例如，最大后验概率（MAP）贝叶斯方法已经实现了患者间药物暴露的至少部分控制。它们提高了护理质量，减少了并发症，缩短了住院时间，降低了成本。然而，MAP贝叶斯方法对大多数临床医生来说是不熟悉的，并且它们仅管理患者的单个最可能版本。他们必须假设建议的剂量方案将准确地击中目标。他们没有办法评估、控制和最小化目标在真实的世界中可以被击中的误差。我们的项目有四个目标。1)我们将首次在我们的多模型（MM）贝叶斯软件中结合剂量控制和血清浓度测量时间（主动双重控制），真正优化患者药物暴露。2)为了更准确地反映实际情况，我们将进一步更新软件，以估计治疗环境中的过程噪声（例如，剂量定时误差）与测量噪声分开。我们将评价其作为药代动力学研究的客观质量指标。3)为了直接测试我们的方法的临床价值，我们将前瞻性地比较当前标准给药后、在当前状态下使用我们的软件给药后以及使用本文提出的功能更新后，具有治疗性万古霉素血清浓度的患者百分比。次要终点将包括治疗结局和成本。4)最后，我们建议扩展我们的目标导向型MM，以探索新药在I/II期研究中首次给予人类后的剂量-反应关系，其双重目标是在最短的时间和最少的患者中定义有效，安全的剂量。