Mathematical and Statistical Modeling to Inform Design of HIV Clinical Trials

数学和统计模型为艾滋病毒临床试验的设计提供信息

• 批准号: 7480736
• 负责人: H. THOMAS BANKS
• 金额: $ 7.12万
• 依托单位: NORTH CAROLINA STATE UNIVERSITY RALEIGH
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7480736
• 关键词: Acute; Address; Anti-Retroviral Agents; Behavior; Biological; Biological Models; CD4 Lymphocyte Count; CD4 Positive T Lymphocytes; Cells; Chronic; Classification; Clinical; Clinical Research; Clinical Sciences; Clinical Trials; Clinical Trials Design; Collaborations; Complex; Condition; Conduct Clinical Trials; Coupled; Cytotoxic T-Lymphocytes; Data; Death Rate; Development; Disease; Equation; Excision; Goals; HIV; HIV Infections; Immune response; Immune system; Immunologics; Immunology; Individual; Infection; Interruption; Joints; Kinetics; Lead; Life; Linear Models; Modeling; Non-linear Models; Outcome; Pathogenesis; Patients; Pharmacotherapy; Population; Predisposition; Principal Investigator; Process; Production; Protease Inhibitor; Publishing; RNA; Rate; Research; Reverse Transcriptase Inhibitors; Science; Seminal; Series; Solutions; Source; Statistical Methods; Statistical Models; Suggestion; System; T-Lymphocyte; Time; Treatment Efficacy; Viral; Viral Load result; Virion; Virus; Work; base; cost efficient; design; improved; insight; interdisciplinary approach; macrophage; mathematical model; mathematical theory; models and simulation; multidisciplinary; neutralizing antibody; optimal control theory; programs; theories; tool; treatment effect; vector

Despite the advent of potent anti-retroviral therapy (ARV) and extensive efforts characterizing the mechanisms of HIV disease, unresolved, complex challenges in HIV research remain that demand a multidisciplinary approach integrating the basic and clinical sciences. In this spirit, the premise of this application is that mathematical nonlinear dynamical system models of within-host HIV dynamics coupled with statistical population models, integrated with clinical and biological expertise and informative data can accelerate breakthroughs in HIV research. A unique, multidisciplinary team merging expertise in immunology and clinical investigation with expertise in mathematical and statistical modeling, whose record of fruitful collaboration is already established, will carry out joint research on mathematical, statistical, immunological, and clinical developments toward this common goal through five interwoven specific aims. Although approximations, mathematical models of within-subject HIV dynamics can yield insights into mechanisms underlying HIV pathogenesis. A model developed in the last project period yields accurate long- term predictions of individual subject longitudional immunologic and virologic profiles. The first aim involves extending the model to incorporate more reliastic representation of body's immune response to the virus, enhancing its predictive ability. Applying the models to data in this way requires an appropriate statistical framework and mastery of the accompanying computational challenges. The second aim is to develop and implement practical statistical methods that can address these challenges and be used to gain information on dynamics in the popluation, so that the models can be used as the basis for the next aim. The predictive capability of these models when applied to data suggests that they can be a powerful tool in the design of clinical trials. The third aim involves development of a systematic strategy for using model-based simulation to inform the design and conduct of clinical trials, which has the potential to lead to more time- and cost- efficient HIV clinical research. Both to prove this principle and to address a key, outstanding clinical question, the fourth aim is to use this approach to design and conduct a clinical trial to determine whether treatment initiated during acute infection followed by terminal interruption at time(s) determined by the models, results in a lower viral load set point and higher CD4 cell count than no treatment. Finally, the fifth aim is to develop and use optimal control theory, mathematical theory for modifying the behavior of nonlinear dynamical systems through control of system inputs, to suggest new, practical adaptive HIV treatment strategies that may lead to improved long-term outcomes. The rich longitudinal data collected during the trial will be used to facilitate development of these strategies.

尽管出现了有效的抗逆转录病毒疗法（ARV）并做出了广泛的努力， 艾滋病毒疾病的机制、未解决的、艾滋病毒研究中的复杂挑战仍然存在，需要 结合基础科学和临床科学的多学科方法。本着这种精神，这个前提 应用是宿主内 HIV 动力学耦合的数学非线性动力学系统模型 通过统计人口模型，结合临床和生物学专业知识以及信息数据，可以 加速艾滋病毒研究的突破。独特的多学科团队融合了以下领域的专业知识 具有数学和统计建模专业知识的免疫学和临床研究，其记录 已经建立了富有成效的合作关系，将在数学、统计、 通过五个相互交织的具体目标，朝着这一共同目标的免疫学和临床发展。 尽管是近似值，但受试者体内 HIV 动态的数学模型可以深入了解 HIV发病机制。上一个项目期间开发的模型产生了准确的长期 个体受试者纵向免疫学和病毒学特征的长期预测。第一个目标涉及 扩展模型以更可靠地表示人体对病毒的免疫反应， 增强其预测能力。以这种方式将模型应用于数据需要适当的统计 框架并掌握随之而来的计算挑战。第二个目标是开发和 实施实用的统计方法来应对这些挑战并用于获取信息 人口动态，以便模型可以作为下一个目标的基础。预测性的 这些模型在应用于数据时的能力表明它们可以成为设计中的强大工具。 临床试验。第三个目标涉及开发使用基于模型的仿真的系统策略 为临床试验的设计和实施提供信息，这有可能导致更多的时间和成本 高效的艾滋病毒临床研究。既证明了这一原理，又解决了一个关键的、突出的临床问题 问题，第四个目标是使用这种方法来设计和进行临床试验，以确定是否 在急性感染期间开始治疗，然后在由主管部门确定的时间终止治疗 模型中，与未治疗相比，病毒载量设定点较低，CD4 细胞计数较高。最后，第五个 目标是发展和使用最优控制理论、数学理论来修正非线性行为 通过控制系统输入的动力系统，提出新的、实用的适应性艾滋病治疗 可能导致改善长期结果的策略。试验期间收集的丰富纵向数据 将用于促进这些战略的制定。