Populaton Pharmacokinetic Modeling and Optimal Control

描述（由申请人提供）：原始项目为 GM 068968，响应 DMS/NIGMS 联合倡议支持数学生物学研究，PA NSF 02-125。这一竞争性的更新应用将继续在生物医学计算科学和技术领域提出新的数学创新。药物的药代动力学和药效学 (PK/PD) 行为建模存在严重的统计缺陷。 PK/PD 社区仍然主要使用基于近似似然的参数化建模方法，不能保证研究更多受试者将获得更接近真实值的参数估计（它们通常会变得更糟）。相比之下，我们的实验室开发了参数（P）和非参数（NP）方法，它们在统计上是一致的。然而，仍然没有办法获得 P 或 NP 参数估计的严格置信区间。这是一个很大的弱点。此外，当前的剂量政策仅基于现有信息，尽管我们知道我们将在未来监测患者并调整剂量。这些已知的未来行动将被忽略。我们的目标是 (1) 开发一种新的顺序贝叶斯方法来制作 PK/PD 群体模型。我们提出了一种令人兴奋的新方法来获得 P 和 NP 群体 PK/PD 模型参数估计的严格置信区间。它是我们之前在 GM 068968 中工作的成果。它还应该为临床医生达到所需治疗目标血清浓度的能力提供严格的置信区间。这将为所有群体建模以及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 拨款的资助。它也是连续的，因此允许将新受试者添加到模型中，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型中。 (2) 继续实施我们的主动控制策略，以在治疗患者的同时优化对患者的了解。目前的剂量方案仅使用迄今为止可获得的信息。我们知道我们将来会监测患者并调整剂量。这被忽略了。剂量方案的目的不是帮助了解患者。我们现在建议将剂量方案作为学习过程中的积极伙伴，通过计算可以偏离目标多远（且安全），仔细地探索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。我们建议现在就提前探索未来的临床场景。我们的方法是使用 IPS（策略空间迭代）算法和粒子滤波器来近似 Bellman 的随机动态规划 (SDP) 方程，以解决潜在的非线性估计问题。这将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.