Populaton Pharmacokinetic Modeling and Optimal Control

描述(申请人提供)：最初的项目是GM 068968，响应DMS/NIGMS支持数学生物学研究的联合倡议，PA NSF02-125。这种相互竞争的更新应用程序继续在生物医学计算科学和技术中提出新的数学创新。药物的药动学和药效学(PK/PD)行为模型存在严重的统计学缺陷。PK/PD社区仍然主要使用基于近似似然的参数建模方法，不能保证研究更多的受试者将获得更接近真实值的参数估计(它们往往会变得更糟)。相比之下，我们的实验室开发了参数方法(P)和非参数方法(NP)，这些方法在统计上是一致的。然而，对于P或NP参数估计，仍然没有办法获得严格的可信区间。这是一个很大的弱点。此外，目前的剂量政策仅基于目前可用的信息，尽管我们知道我们将监测患者并在未来调整剂量。这些已知的未来操作将被忽略。我们的目标是(1)发展一种新的序贯贝叶斯方法来建立PK/PD人口模型。我们提出了一种令人兴奋的新方法来获得P和NP总体PK/PD模型参数估计的严格可信区间。这是我们之前在GM 068968中工作的成果。它还应该为临床医生达到期望的治疗目标血清浓度的能力提供严格的可信区间。这将为所有人口建模提供坚实的数学基础，并为我们目前优化协调联合药物治疗的工作提供坚实的数学基础，我们最近获得了EB 005803拨款。它也是连续的，因此允许将新的主题添加到模型中，而不必从头开始重新制作。这将极大地帮助社区医院根据需要将自己的患者添加到原始模式中。(2)继续研究我们的主动控制策略，以便在治疗患者的同时优化对他/她的了解。目前的给药方案只使用到目前为止可用的信息。我们知道我们会监测患者，并在未来调整剂量。这一点将被忽略。给药方案的设计并不是为了帮助了解患者。我们现在建议在学习过程中使用剂量方案作为积极的合作伙伴，通过计算一个人可以偏离目标多远(且安全)，仔细地探测患者的系统以了解更多信息，从而在预计的治疗持续时间内最大限度地提高治疗精度。我们建议提前探索未来的临床情景，现在。我们的方法是使用IPS(策略空间迭代)算法来逼近Bellman的随机动态规划(SDP)方程，并使用粒子过滤器来解决潜在的非线性估计问题。这应该会使患者护理更加智能和优化。

DESCRIPTION (provided by applicant): The original project was GM 068968, responding to Joint DMS/NIGMS Initiative to Support Research in Mathematical Biology, PA NSF 02-125. This competing renewal application is continues to propose new mathematical innovations in biomedical computational science and technology. Modeling the pharmacokinetic and pharmacodynamic (PK/PD) behavior of drugs has serious statistical flaws. The PK/PD community still uses mainly parametric methods of modeling based on approximate likelihoods, with no guarantee that studying more subjects will obtain parameter estimates closer to the true values (they often get worse). In contrast, our laboratory has developed methods, both parametric (P) and nonparametric (NP), which are statistically consistent. However, there is still no way to obtain rigorous confidence intervals on P or NP parameter estimates. This is a great weakness. Also, current dosing policies are based only on information available now, though we know we will monitor the patient and adjust dosage in the future. These known future actions are ignored. Our aims are (1) TO DEVELOP A NEW SEQUENTIAL BAYESIAN METHOD FOR MAKING PK/PD POPULATION MODELS. We propose an exciting new method to obtain rigorous confidence intervals for parameter estimates for both P and NP population PK/PD models. It is an outgrowth of our previous work in GM 068968. It should also provide rigorous confidence intervals on a clinician's ability to hit a desired therapeutic target serum concentration. This will provide a firm mathematical foundation for all population modeling, and for our current work to optimize coordinated combination drug therapy for which we have recently been funded under grant EB 005803. It is also sequential, and thus permits new subjects to be added to a model rather than having to remake it from scratch. This will greatly aid community hospitals to add their own patients to the original model as desired. (2) TO CONTINUE WORK ON OUR ACTIVE CONTROL STRATEGY TO OPTIMIZE LEARNING ABOUT THE PATIENT WHILE TREATING HIM/HER AT THE SAME TIME. Current dosage regimens use only information available up to now. We know we will monitor the patient and adjust dosage in the future. This is ignored. The dosage regimen is not designed to aid in learning about the patient. We now propose to use the dosage regimen as an active partner in the learning process, by calculating how far (and safely) one can deviate a bit from the target goal to probe the patient's system thoughtfully to learn more about it, and thus to maximize therapeutic precision over the projected duration of therapy. We propose to explore future clinical scenarios in advance, now. Our approach is to approximate the Stochastic Dynamic Programming (SDP) equations of Bellman using the IPS (Iteration in Policy Space) algorithm, and a Particle Filter to solve the underlying nonlinear estimation problem. This should make patient care still more intelligent and optimal.