Population Pharmacokinetic Modeling and Optimal Control

群体药代动力学建模和最优控制

• 批准号: 6900308
• 负责人: Roger W. Jelliffe
• 金额: $ 22.38万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 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 2007-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6900308
• 关键词: Internet; chemotherapy; computer program /software; computer system design /evaluation; human population study; mathematical model; method development; pharmacokinetics; statistics /biometry

DESCRIPTION (provided by applicant): This proposal is to develop new tools to optimize population drug modeling and individualized therapy. Aim 1 will develop new methods for consistent and efficient parametric population modeling using low -discrepancy Faure integration methods. Aim 2 will develop new methods for determining confidence intervals for nonparametric population analysis using profile likelihood techniques. Aim 3 develops a new approach to stochastic dosage optimization using the latest sampling-based methods in nonlinear filtering and a novel method for solving the Bellman stochastic dynamic programming equations. This work will have a broad impact on population modeling and patient care. Most researchers and clinicians now use parametric (P) methods which are not statistically consistent. Much effort is spent on clinical ananlyses using those flawed methods. The new consistent and efficient P method in Aim 1 will be a major advance that will put maximum likelihood P population modeling on a sound statistical footing. Nonparametric (NP) methods of population analysis are also consistent. They can in addition handle mixture distributions from genetically polymorphic populations, and are suitable for "multiple model" (MM) optimal dosage design. However, computationally intensive bootstrap methods are the only current way to obtain NP confidence intervals. Aim 2 will provide a new, more efficient, route to these intervals. Then both P and NP consistent methods will be available to the health care community. NP population modeling is a tool for optimal MM design of coordinated combination dosage regimens of toxic drugs for treatment of AIDS, cancer, infectious diseases, and transplant patients. This, plus feedback from serum, effect, and toxicity measurements can now be combined into a new and unique paradigm for planning and optimizing the entire process of learning about drug behavior in patients while treating them at the same time. This is Aim 3. Such a tool does not exist at present, to our kowledge. For any planned duration of therapy, one will be able to coordinate both the amounts and timing of the doses, and the number and timing of the measurements. The result will be an active therapeutic strategy which achieves desired target goals with optimal precision, rehearsing many future scenarios in advance (this is new). In all three Aims, the end product will be widely disseminated software with user interfaces for research and clinical use.

描述（由申请人提供）：该提案旨在开发新的工具，以优化群体药物建模和个体化治疗。 目标1将开发新的方法，一致和有效的参数人口建模使用低差异福雷整合方法。目标2将开发新的方法来确定置信区间的非参数人口分析使用轮廓似然技术。目的3开发了一种新的方法来随机剂量优化使用最新的基于采样的方法在非线性滤波和一种新的方法来解决贝尔曼随机动态规划方程。 这项工作将对人口建模和患者护理产生广泛的影响。大多数研究人员和临床医生现在使用的参数（P）方法在统计上并不一致。大量的精力都花在临床分析使用这些有缺陷的方法。目标1中新的一致和有效的P方法将是一个重大的进步，它将使最大似然P总体建模建立在一个良好的统计基础上。 人口分析的非参数（NP）方法也是一致的。它们还可以处理遗传多态群体的混合分布，并适用于“多模型”（MM）最优剂量设计。然而，计算密集的bootstrap方法是目前获得NP置信区间的唯一方法。目标2将提供一个新的，更有效的，这些间隔的路线。然后，P和NP一致的方法将提供给卫生保健界。NP群体建模是用于治疗艾滋病、癌症、传染病和移植患者的毒性药物的协调组合剂量方案的最佳MM设计的工具。这一点，加上来自血清，效果和毒性测量的反馈，现在可以结合成一个新的和独特的范式，用于规划和优化整个过程，了解患者的药物行为，同时治疗他们。这是目标3。据我们所知，目前还不存在这样的工具。对于任何计划的治疗持续时间，将能够协调剂量的量和时间以及测量的次数和时间。其结果将是一种积极的治疗策略，以最佳的精度实现预期的目标，提前排练许多未来的场景（这是新的）。在所有三个目标中，最终产品将是广泛传播的软件，具有供研究和临床使用的用户界面。