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.

尽管有效的抗逆转录病毒疗法（ARV）出现了，并且是特征的广泛努力 艾滋病毒疾病的机制，未解决的艾滋病毒研究中未解决的复杂挑战仍然要求 多学科方法整合了基础科学和临床科学。本着这个精神的前提 应用是宿主内HIV动力学的数学非线性动力学系统模型耦合 借助统计人群模型，与临床和生物学专业知识和信息性数据集成在一起可以 加速艾滋病毒研究中的突破。一个独特的多学科团队，合并专业知识 免疫学和临床研究具有数学和统计建模方面的专业知识，其记录 已经建立了富有成果的合作，将对数学，统计， 免疫学和临床发展通过五个交织的特定目标来实现这一共同目标。 尽管近似值，但受试者内HIV动力学的数学模型可以产生洞察力 HIV发病机理的基础机制。在最后一个项目期间开发的模型可得出准确的长期 单个主体纵向免疫和病毒学特征的术语预测。第一个目标涉及 扩展该模型以纳入身体对病毒免疫反应的更为复杂的表示， 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 框架和随附的计算挑战的精通。第二个目的是发展和 实施可以解决这些挑战并用于获取信息的实用统计方法 关于popluation的动态，因此可以将模型用作下一个目标的基础。预测性 这些模型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。第三个目的涉及制定用于使用基于模型的模拟的系统策略 为了告知临床试验的设计和行为，这有可能导致更多的时间和成本 有效的HIV临床研究。既要证明这一原则，又要解决一个钥匙，出色的临床 问题，第四个目的是使用这种方法设计和进行临床试验，以确定是否是否 在急性感染期间开始的治疗，然后在时间终末中断。 模型，导致病毒载荷设定点和CD4细胞计数较低，而不是没有治疗。最后，第五 目的是发展和使用最佳控制理论，数学理论来修改非线性的行为 通过控制系统输入的动态系统，建议新的，实用的自适应艾滋病毒治疗 可能会改善长期结果的策略。试验期间收集的丰富纵向数据 将用于促进这些策略的制定。