Mathematical and Statistical Modeling to Inform Design of HIV Clinical Trials

尽管出现了有效的抗逆转录病毒疗法（ARV）和广泛的努力， 艾滋病毒疾病的机制，艾滋病毒研究中尚未解决的复杂挑战仍然存在，需要 多学科方法整合基础和临床科学。本着这种精神， 应用是宿主内HIV动力学耦合的数学非线性动力系统模型， 利用统计群体模型，结合临床和生物学专业知识以及信息数据， 加速艾滋病毒研究的突破。一个独特的多学科团队， 具有数学和统计建模方面的专业知识， 已经建立了富有成效的合作，将在数学，统计， 通过五个相互交织的具体目标，实现这一共同目标的免疫学和临床发展。 尽管是近似值，但受试者内HIV动力学的数学模型可以深入了解 HIV发病机制。在上一个项目期间开发的模型产生了准确的长期- 个体受试者免疫学和病毒学特征的长期预测。第一个目标是 扩展该模型以包含身体对病毒的免疫反应的更可靠的表示， 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 框架和随之而来的计算挑战的掌握。第二个目标是发展和 实施能够应对这些挑战并用于获取信息的实用统计方法 在人口动态，使模型可以作为下一个目标的基础。预测 这些模型在应用于数据时的能力表明，它们可以成为设计 临床试验第三个目标是发展一个系统的战略，使用基于模型的模拟 为临床试验的设计和实施提供信息，这有可能导致更多的时间和成本， 有效的艾滋病临床研究。为了证明这一原则，并解决一个关键的，杰出的临床 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 模型，导致较低的病毒载量设定点和较高的CD 4细胞计数比没有治疗。最后在第五 目的是发展和使用最优控制理论，数学理论，用于修改非线性的行为， 通过控制系统输入，提出新的、实用的适应性艾滋病治疗方法 可能导致改善长期结果的战略。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。

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.