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），并作出了广泛的努力来描述艾滋病毒疾病的机制，但艾滋病毒研究中尚未解决的复杂挑战仍然存在，需要综合基础科学和临床科学的多学科方法。本着这种精神，该应用的前提是，宿主内HIV动力学的数学非线性动力系统模型与统计种群模型相结合，结合临床和生物学专业知识和信息性数据，可以加速HIV研究的突破。一个独特的多学科团队将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结合起来，他们已经建立了卓有成效的合作记录，将通过五个相互交织的具体目标，对数学、统计、免疫学和临床发展进行联合研究，以实现这一共同目标。虽然近似，在主体内的艾滋病毒动力学的数学模型可以产生的见解机制潜在的艾滋病毒发病机制。在最后一个项目期间开发的模型可以对个体受试者纵向免疫和病毒学概况进行准确的长期预测。第一个目标是扩展模型，使其更真实地反映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。第二个目标是发展和执行切实可行的统计方法，以应付这些挑战，并用来获得关于人口动态的资料，以便这些模型可以作为实现下一个目标的基础。这些模型在应用于数据时的预测能力表明，它们可以成为设计临床试验的有力工具。第三个目标涉及开发一种系统的策略，使用基于模型的模拟来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效益更高的艾滋病毒临床研究。为了证明这一原则并解决一个关键的、突出的临床问题，第四个目标是使用这种方法设计并进行临床试验，以确定在急性感染期间开始治疗，然后在模型确定的时间内终止治疗，是否比不治疗导致更低的病毒载量设定点和更高的CD4细胞计数。最后，第五个目标是发展和使用最优控制理论，即通过控制系统输入来修改非线性动态系统行为的数学理论，以提出新的、实用的自适应艾滋病毒治疗策略，这些策略可能会改善长期结果。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制定。

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.