Mathematical and Statistical Modeling to Inform Design of HIV Clinical Trials

描述(由申请人提供)：尽管出现了有效的抗逆转录病毒疗法(ARV)和以艾滋病毒疾病的机制为特征的广泛努力，但艾滋病毒研究中仍存在尚未解决的复杂挑战，需要综合基础和临床科学的多学科方法。本着这一精神，这一应用的前提是宿主内艾滋病毒动态的数学非线性动力系统模型与统计人口模型相结合，结合临床和生物学专业知识和信息数据，可以加速艾滋病毒研究的突破。一个独特的多学科团队融合了免疫学和临床研究方面的专业知识与数学和统计建模方面的专业知识，其卓有成效的合作记录已经建立，将通过五个相互交织的具体目标，对数学、统计学、免疫学和临床的发展进行联合研究，以实现这一共同目标。虽然近似，但受试者内艾滋病毒动力学的数学模型可以深入了解艾滋病毒致病的潜在机制。在最后一个项目期间开发的一个模型产生了对个体受试者纵向免疫学和病毒学概况的准确的长期预测。第一个目标是扩展该模型，以更真实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一个适当的统计框架，并掌握随之而来的计算挑战。第二个目标是制定和实施实用的统计方法，以应对这些挑战，并用于获得关于人口动态的信息，以便这些模型可用作下一个目标的基础。这些模型的预测能力在应用于数据时表明，它们可以成为临床试验设计中的强大工具。第三个目标涉及制定一项系统战略，利用基于模型的模拟为临床试验的设计和实施提供信息，这有可能导致更多具有时间和成本效益的艾滋病毒临床研究。为了证明这一原理和解决一个关键的、悬而未决的临床问题，第四个目标是使用这种方法设计和进行一项临床试验，以确定在急性感染期间开始治疗，然后在模型确定的终止中断时间(S)，是否会导致较低的病毒载量设定点和较高的CD_4细胞计数。最后，第五个目标是开发和使用最优控制理论，即通过控制系统输入来改变非线性动力系统行为的数学理论，提出可能导致改善长期结果的新的、实用的适应性艾滋病毒治疗策略。在试验期间收集的丰富的纵向数据将用于促进这些战略的发展。

DESCRIPTION (provided by applicant): 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 longitudinal immunologic and virologic profiles. The first aim involves extending the model to incorporate more realistic 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 population, 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.