Population Pharmacokinetic Modeling and Optimal Control

群体药代动力学建模和最优控制

• 批准号: 6756435
• 负责人: Roger W. Jelliffe
• 金额: $ 22.37万
• 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/6756435
• 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(申请人提供)：该提案旨在开发新的工具来优化人群药物建模和个体化治疗。目标1将开发使用低偏差Faure积分方法进行一致和有效的参数种群建模的新方法。目标2将开发利用轮廓似然技术确定非参数总体分析的可信区间的新方法。目的3利用非线性滤波中最新的基于抽样的方法和求解Bellman随机动态规划方程的新方法，发展了一种新的随机剂量优化方法。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参数(P)方法，但这些方法在统计上并不一致。使用这些有缺陷的方法进行临床分析花费了大量的精力。目标1中新的一致和有效的P方法将是一个重大进步，它将使最大似然P总体建模建立在可靠的统计基础上。人口分析的非参数(NP)方法也是一致的。此外，它们还可以处理遗传多态种群的混合分布，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可信区间的唯一途径。AIM 2将为这些间隔提供一条新的、更有效的路线。然后，P和NP一致的方法将可用于医疗保健社区。NP群体模型是治疗艾滋病、癌症、传染病和移植患者的有毒药物协调组合给药方案的最优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独特的范例，用于规划和优化了解患者药物行为的整个过程，同时对他们进行治疗。这就是目标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，人们将能够协调剂量的数量和时间，以及测量的数量和时间。结果将是一个积极的治疗策略，以最佳的精确度实现预期的目标，提前排练许多未来的场景(这是新的)。在这三个目标中，最终产品将是广泛传播的带有用户界面的软件，用于研究和临床使用。