Populaton Pharmacokinetic Modeling and Optimal Control

群体药代动力学建模和最优控制

基本信息

  • 批准号:
    7317917
  • 负责人:
  • 金额:
    $ 30.97万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2003
  • 资助国家:
    美国
  • 起止时间:
    2003-06-15 至 2011-07-31
  • 项目状态:
    已结题

项目摘要

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.
描述(由申请人提供):原始项目是GM 068968,响应联合DMS/NIGMS倡议,以支持数学生物学研究,PA NSF 02-125。这种竞争性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建模方法,无法保证研究更多受试者将获得更接近真实值的参数估计值(它们通常会变得更糟)。相比之下,我们的实验室已经开发出了参数(P)和非参数(NP)方法,这些方法在统计上是一致的。然而,仍然没有办法获得严格的置信区间的P或NP参数估计。这是一个很大的弱点。此外,目前的给药政策仅基于目前可用的信息,尽管我们知道我们将在未来监测患者并调整剂量。这些已知的未来动作被忽略。本文的目的是(1)提出一种新的建立PK/PD群体模型的序贯贝叶斯方法。我们提出了一个令人兴奋的新方法,以获得严格的置信区间的参数估计P和NP群体PK/PD模型。它是我们以前在GM 068968中工作的产物。它还应该提供关于临床医生达到期望的治疗目标血清浓度的能力的严格置信区间。这将为所有人群建模提供坚实的数学基础,并为我们目前的工作,以优化协调的联合药物治疗,我们最近在拨款EB 005803下资助。它也是连续的,因此允许新的主题被添加到模型中,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,以便在治疗期间优化对患者的了解。目前的剂量方案仅使用迄今为止可用的信息。我们知道我们将监测患者并在未来调整剂量。这一点被忽略了。给药方案不旨在帮助了解患者。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,通过计算人们可以偏离目标多少(和安全地)来仔细探测患者的系统以了解更多关于它的信息,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。我们的方法是近似的随机动态规划(SDP)方程的贝尔曼使用IPS(迭代策略空间)算法,和粒子滤波器来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。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Roger W. Jelliffe其他文献

A Stochastic Control Program to Predict Outcome and to Support Therapeutic Decisions: A Preliminery Report
  • DOI:
    10.1007/s10877-005-5870-5
  • 发表时间:
    2005-06-01
  • 期刊:
  • 影响因子:
    2.200
  • 作者:
    William C. Shoemaker;David S. Bayard;Charles C. J. Wo;Andreas Botnen;Nasarolla Ahmedpour;Ashutosth Gandhi;Demetrios Demetriades;Roger W. Jelliffe
  • 通讯作者:
    Roger W. Jelliffe
Computer Programs for Drug Therapy: A Tool for Teaching Pharmacokinetics
  • DOI:
    10.1016/s0002-9459(24)05083-6
  • 发表时间:
    1974-05-01
  • 期刊:
  • 影响因子:
  • 作者:
    Frank J. Goicoechea;Roger W. Jelliffe
  • 通讯作者:
    Roger W. Jelliffe
Administration of Digoxin
  • DOI:
    10.1378/chest.56.1.56
  • 发表时间:
    1969-07-01
  • 期刊:
  • 影响因子:
  • 作者:
    Roger W. Jelliffe
  • 通讯作者:
    Roger W. Jelliffe
Author’s reply to Veloso HH Comment on “The Role of Digitalis Pharmacokinetics in Converting Atrial Fibrillation and Flutter to Sinus Rhythm”
  • DOI:
    10.1007/s40262-016-0381-8
  • 发表时间:
    2016-03-21
  • 期刊:
  • 影响因子:
    4.000
  • 作者:
    Roger W. Jelliffe
  • 通讯作者:
    Roger W. Jelliffe
Author’s Reply to Proost: “Challenges in Individualizing Drug Dosage for Intensive Care Unit Patients”
  • DOI:
    10.1007/s40262-016-0482-4
  • 发表时间:
    2016-12-29
  • 期刊:
  • 影响因子:
    4.000
  • 作者:
    Roger W. Jelliffe
  • 通讯作者:
    Roger W. Jelliffe

Roger W. Jelliffe的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Roger W. Jelliffe', 18)}}的其他基金

Optimizing Coordinated Combination Drug Therapy
优化协调联合药物治疗
  • 批准号:
    7248652
  • 财政年份:
    2006
  • 资助金额:
    $ 30.97万
  • 项目类别:
Optimizing Coordinated Combination Drug Therapy
优化协调联合药物治疗
  • 批准号:
    7667429
  • 财政年份:
    2006
  • 资助金额:
    $ 30.97万
  • 项目类别:
Optimizing Coordinated Combination Drug Therapy
优化协调联合药物治疗
  • 批准号:
    7423963
  • 财政年份:
    2006
  • 资助金额:
    $ 30.97万
  • 项目类别:
Optimizing Coordinated Combination Drug Therapy
优化协调联合药物治疗
  • 批准号:
    7139743
  • 财政年份:
    2006
  • 资助金额:
    $ 30.97万
  • 项目类别:
Population Pharmacokinetic Modeling and Optimal Control
群体药代动力学建模和最优控制
  • 批准号:
    6900308
  • 财政年份:
    2003
  • 资助金额:
    $ 30.97万
  • 项目类别:
Population Pharmacokinetic Modeling and Optimal Control
群体药代动力学建模和最优控制
  • 批准号:
    6756435
  • 财政年份:
    2003
  • 资助金额:
    $ 30.97万
  • 项目类别:
Populaton Pharmacokinetic Modeling and Optimal Control
群体药代动力学建模和最优控制
  • 批准号:
    7683166
  • 财政年份:
    2003
  • 资助金额:
    $ 30.97万
  • 项目类别:
Population Pharmacokinetic Modeling and Optimal Control
群体药代动力学建模和最优控制
  • 批准号:
    6687987
  • 财政年份:
    2003
  • 资助金额:
    $ 30.97万
  • 项目类别:
Populaton Pharmacokinetic Modeling and Optimal Control
群体药代动力学建模和最优控制
  • 批准号:
    7473264
  • 财政年份:
    2003
  • 资助金额:
    $ 30.97万
  • 项目类别:
Population Pharmacokinetic Modeling and Optimal Control
群体药代动力学建模和最优控制
  • 批准号:
    7071649
  • 财政年份:
    2003
  • 资助金额:
    $ 30.97万
  • 项目类别:

相似海外基金

Approximate algorithms and architectures for area efficient system design
区域高效系统设计的近似算法和架构
  • 批准号:
    LP170100311
  • 财政年份:
    2018
  • 资助金额:
    $ 30.97万
  • 项目类别:
    Linkage Projects
AMPS: Rank Minimization Algorithms for Wide-Area Phasor Measurement Data Processing
AMPS:用于广域相量测量数据处理的秩最小化算法
  • 批准号:
    1736326
  • 财政年份:
    2017
  • 资助金额:
    $ 30.97万
  • 项目类别:
    Standard Grant
Low Power, Area Efficient, High Speed Algorithms and Architectures for Computer Arithmetic, Pattern Recognition and Cryptosystems
用于计算机算术、模式识别和密码系统的低功耗、面积高效、高速算法和架构
  • 批准号:
    1686-2013
  • 财政年份:
    2017
  • 资助金额:
    $ 30.97万
  • 项目类别:
    Discovery Grants Program - Individual
Rigorous simulation of speckle fields caused by large area rough surfaces using fast algorithms based on higher order boundary element methods
使用基于高阶边界元方法的快速算法对大面积粗糙表面引起的散斑场进行严格模拟
  • 批准号:
    375876714
  • 财政年份:
    2017
  • 资助金额:
    $ 30.97万
  • 项目类别:
    Research Grants
Low Power, Area Efficient, High Speed Algorithms and Architectures for Computer Arithmetic, Pattern Recognition and Cryptosystems
用于计算机算术、模式识别和密码系统的低功耗、面积高效、高速算法和架构
  • 批准号:
    1686-2013
  • 财政年份:
    2016
  • 资助金额:
    $ 30.97万
  • 项目类别:
    Discovery Grants Program - Individual
Low Power, Area Efficient, High Speed Algorithms and Architectures for Computer Arithmetic, Pattern Recognition and Cryptosystems
用于计算机算术、模式识别和密码系统的低功耗、面积高效、高速算法和架构
  • 批准号:
    1686-2013
  • 财政年份:
    2015
  • 资助金额:
    $ 30.97万
  • 项目类别:
    Discovery Grants Program - Individual
Low Power, Area Efficient, High Speed Algorithms and Architectures for Computer Arithmetic, Pattern Recognition and Cryptosystems
用于计算机算术、模式识别和密码系统的低功耗、面积高效、高速算法和架构
  • 批准号:
    1686-2013
  • 财政年份:
    2014
  • 资助金额:
    $ 30.97万
  • 项目类别:
    Discovery Grants Program - Individual
AREA: Optimizing gene expression with mRNA free energy modeling and algorithms
区域:利用 mRNA 自由能建模和算法优化基因表达
  • 批准号:
    8689532
  • 财政年份:
    2014
  • 资助金额:
    $ 30.97万
  • 项目类别:
CPS: Synergy: Collaborative Research: Distributed Asynchronous Algorithms and Software Systems for Wide-Area Monitoring of Power Systems
CPS:协同:协作研究:用于电力系统广域监控的分布式异步算法和软件系统
  • 批准号:
    1329780
  • 财政年份:
    2013
  • 资助金额:
    $ 30.97万
  • 项目类别:
    Standard Grant
CPS: Synergy: Collaborative Research: Distributed Asynchronous Algorithms and Software Systems for Wide-Area Mentoring of Power Systems
CPS:协同:协作研究:用于电力系统广域指导的分布式异步算法和软件系统
  • 批准号:
    1329745
  • 财政年份:
    2013
  • 资助金额:
    $ 30.97万
  • 项目类别:
    Standard Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了