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: 8733174
• 负责人: Michael N. Neely
• 金额: $ 44.25万
• 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/8733174
• 关键词: Adult; Affect; Algorithms; Americas; Bayesian Method; Bayesian Modeling; 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(申请人提供)：对于潜在有毒药物的治疗，如用于传染病、移植和癌症的药物，医学界经常接受患者之间在药物暴露方面的巨大差异，这在任何其他科学或工业学科中都是完全不可接受的。人们通常会进行“一刀切”的标准治疗，并对缺乏临床效果或毒性的情况进行监测。相反，临床医生可以提前计划，通过为每个患者设置个性化的血清药物浓度或效果的临床目标，然后计算剂量，以最高的精度达到预期的目标，从而实现最大限度的效果和最小的毒性。例如，最大后验概率(MAP)贝叶斯方法至少实现了对患者间药物暴露的部分控制。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，大多数临床医生对MAP贝叶斯方法并不熟悉，而且它们只管理最有可能的单一版本的患者。他们必须假设拟议的剂量方案将准确地击中目标。他们无法评估、控制和最小化在现实世界中可以命中目标的误差。我们的项目有四个目标。1)我们将首次在我们为临床医生设计的多模型(MM)贝叶斯软件中结合剂量控制和血清浓度测量的时间(主动双重控制)，从而真正优化患者的药物暴露。2)为了更准确地反映实际情况，我们将进一步更新软件，以便将治疗环境中的过程噪声(例如剂量定时错误)与测量噪声分开估计。我们将把它作为药代动力学研究的客观质量指标进行评估。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剂量、使用我们的软件在当前状态下的剂量以及使用本文提出的功能更新后的万古霉素血药浓度患者的百分比。次要终点将包括治疗结果和成本。4)最后，我们建议将我们的面向目标的MM扩展到探索新药在I/II阶段首次给药后的剂量-反应关系，以确定在最短的时间和最少的患者中确定有效、安全的剂量这两个目标。