QUANTITATIVE METHODS FOR MODELING HIV INFECTION DYNAMICS

HIV 感染动力学建模的定量方法

• 批准号: 6346047
• 负责人: STEVEN G. SELF
• 金额: $ 7.12万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-30 至 2002-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6346047
• 关键词: HIV infections; human data; mathematical model; model design /development; statistics /biometry

The dynamic nature of interactions between the Human Immunodeficiency Virus (HIV) and the human immune system is complex and remains poorly understood in spite of recent important findings about the kinetic of viral and T-cell replication and clearance and the identification of cell- surface co-receptors for HIV entry. Developing accurate descriptions and a deeper understanding of the interactions between HIV and the immune system during the acute and early stages of Hiv infection is critical to the evaluation of new, promising therapeutic regimens. Mathematical models, in the form of systems of deterministic or stochastic rate equations, provide the most natural and convenient framework for formal descriptions of interactions between HIV and various compartments of the human immune system. Various simplified biological models for these interactions have been translated into such mathematical models and published by numerous research groups. Most of these models have focused on descriptions of the long-term course rather than the acute/early stage of HIV infection. There has been no serious and systematic study of the mathematical and probabilistic properties of these models. These models have typically been fit to data from few, select patients using statistical models and methods that give virtually no serious attention to assessing sources of variability across patients. Finally, there has been no careful and comprehensive comparative study of these models with respect ot their ability to accurately describe systematic patterns in longitudinally collected virological and immunological data and to predict subsequent clinical outcomes. We propose to perform a systematic assessment of mathematical models for interactions between HIV and the human immune system with a particular emphasis on the utility of these models for describing acute and early stages of HIV infection. We will refine the formulation of these mathematical models and derive from them statistical models that acknowledge important sources of variation in observable analogs of model variables. We will develop and implement formal statistical methods to fit these models to data from clinical studies of acute/early HIV infection and perform a data-based comparative study of the models. Through existing and ongoing collaborations with clinical researchers, er will use the models and methods to address specific scientific questions that are posed in the context of clinical studies.

人类免疫缺陷之间相互作用的动态本质 病毒（HIV）和人体免疫系统很复杂且状况不佳 尽管最近关于动力学的重要发现 病毒和 T 细胞的复制和清除以及细胞的鉴定 HIV 进入的表面辅助受体。 制定准确的描述和 更深入地了解艾滋病毒与免疫之间的相互作用 艾滋病毒感染急性期和早期阶段的系统至关重要 评估新的、有前途的治疗方案。 数学模型，以确定性或随机系统的形式 速率方程，提供最自然和方便的框架 HIV 与各个区室之间相互作用的正式描述 人体免疫系统。 各种简化的生物模型 这些相互作用已被转化为这样的数学模型 由许多研究小组发表。 大多数这些模型都集中在 对长期病程而非急性/早期阶段的描述 艾滋病毒感染。 目前还没有对此进行认真、系统的研究 这些模型的数学和概率特性。 这些型号 通常适合使用少数精选患者的数据 统计模型和方法实际上没有认真关注 评估患者差异的来源。 最后，已经有了 没有对这些模型进行仔细和全面的比较研究 尊重他们准确描述系统模式的能力 纵向收集病毒学和免疫学数据并预测 随后的临床结果。 我们建议对数学模型进行系统评估 HIV 与人体免疫系统之间的相互作用 强调这些模型在描述急性和早期的效用 HIV 感染的阶段。 我们将完善这些内容的制定 数学模型并从中推导出统计模型 承认可观察的模型类似物中变异的重要来源 变量。 我们将制定并实施正式的统计方法 将这些模型与急性/早期艾滋病毒临床研究的数据进行拟合 感染并对模型进行基于数据的比较研究。 通过与临床研究人员现有和正在进行的合作，呃 将使用模型和方法来解决具体的科学问题 这些是在临床研究的背景下提出的。