Population Pharmacokinetic Modeling and Dual Optimal Control

描述（由申请人提供）：对于使用潜在毒性药物的治疗，例如用于传染病、移植和癌症的药物，医学界通常接受患者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是完全不可接受的。人们通常给出“一刀切”的标准疗法，并监测缺乏临床效果或毒性。相反，临床医生可以提前计划，通过为每位患者设定个性化的血清药物浓度或效果的临床目标，然后计算剂量，以最大的精度达到预期的目标，从而实现效果最大化和毒性最小化。例如，最大验后概率（MAP）贝叶斯方法至少部分控制了患者间药物暴露。它们提高了护理质量，减少了并发症，缩短了住院时间，降低了费用。然而，MAP贝叶斯方法对大多数临床医生来说是不熟悉的，它们只管理患者最可能的单一版本。他们必须假设所建议的给药方案会准确地达到目标。他们没有办法评估、控制和最小化在现实世界中可能击中目标的错误。我们的项目有四个目标。1)我们将首次在临床医生使用的多模型（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，将治疗环境中的过程噪声（如剂量定时误差）与测量噪声分开估计。我们将把它作为药代动力学研究质量的客观指标进行评价。3)为了直接检验我们方法的临床价值，我们将前瞻性地比较当前标准给药后、使用我们的软件在当前状态下给药后以及使用本文提出的功能更新后万古霉素治疗血清浓度的患者百分比。次要终点包括治疗结果和费用。4)最后，我们建议扩展我们的目标导向的MM，以探索新药在I/II期研究中首次给人服用后的剂量-反应关系，其双重目标是在最短的时间和最少的患者中确定有效、安全的剂量。

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.