Modeling of Viral Load Trajectories for HIV Cure Research

HIV 治疗研究的病毒载量轨迹建模

• 批准号: 10548503
• 负责人: Rui Wang
• 金额: $ 43.26万
• 依托单位: HARVARD PILGRIM HEALTH CARE, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-24 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10548503
• 关键词: AIDS clinical trial group; Accounting; Address; Affect; Algorithms; Biological Assay; Biological Markers; Clinical Research; Collaborations; Communicable Diseases; Data; Development; Disease remission; Drug Costs; Drug Interactions; Drug resistance; Evaluation; Future; Gifts; Group Identifications; HIV; HIV Infections; Heterogeneity; Immunologic Factors; Individual; Interruption; Intervention; Mediation; Methodology; Methods; Mission; Modeling; National Institute of Allergy and Infectious Disease; Non-linear Models; Outcome; Patients; Pharmaceutical Preparations; Phase; Predictive Factor; Prevalence; Process; Public Health; RNA; Randomized; Reaction Time; Recording of previous events; Research; Research Personnel; Research Support; Resources; Role; Sample Size; Source; Techniques; Testing; Therapeutic Agents; Time; Viral; Viral Load result; Viral load measurement; Viremia; Virus Replication; Withdrawal; Work; antiretroviral therapy; base; biomarker selection; cohort; design; expectation; feature selection; flexibility; high dimensionality; improved; innovation; insight; mortality; novel therapeutic intervention; novel therapeutics; primary endpoint; response; side effect; success; tool; viral rebound

The world urgently needs to advance the HIV cure research agenda to address the persistently high global HIV prevalence and associated mortality. Despite the success of combined antiretroviral therapy (ART) in achieving sustained control of viral replication, the concerns about side-effects, drug-drug interactions, drug resistance and cost call for a need to identify strategies for achieving HIV eradication or an ART-free remission. Following ART withdrawal, patients' viral load levels usually increase rapidly to a peak followed by a dip, and then stabilize at a viral load set point. Characterizing features of the viral rebound trajectories (e.g., time to viral rebound and viral set points) after analytic antiretroviral treatment interruption (ATI) and identifying host, virological, and immunological factors that are predictive of these features are central to HIV cure research. But doing so requires addressing a variety of analytical challenges, including the non-linear viral rebound trajectories, coarsened data due to the assay's limit of quantification, intermittent measurements of viral load values, small sample sizes from individual studies, and high-dimensional candidate predictors. Motivated by our ongoing collaborations with HIV cure research investigators and built on our previous work, we aim to address key methodological gaps by leveraging data from multiple randomized studies conducted by the AIDS Clinical Trials Group and from the Zurich Primary HIV Infection Cohort. Aim 1 proposes to develop a new set of methods for prediction of time to viral rebound based on comprehensive history profiles, such as the rate of viral decay after ART initiation, extending fitting algorithms and variable selection techniques developed for interval-censored outcomes. Aim 2 proposes to fit the viral rebound model using a Smoothed Simulated Pseudo Maximum likelihood method which maximizes a smoothed simulated objective function constructed based on a Monte Carlo approximation of the first two moments of the smoothed responses, and to develop methods to assess the association between time to rebound and the viral set point and to simultaneously select biomarkers that affect different finer features of the viral rebound trajectory. Aim 3 proposes to develop methods that optimally integrate data from multiple cohorts and different phases of viral load trajectories while properly accounting for the homogeneity and heterogeneity in covariate effects across studies. Innovation lies in the development and application of new methods for modeling viral rebound that address various inherent challenges in analyses of available data. Significance lies in the role of these methods in better characterizing viral rebound trajectories, identifying pre- ATI predictors, and assessing the effects and mechanisms of novel therapeutic agents. The results of the proposed research can inform optimal design of future ATI studies and provide new tools that can extract more information from data collected in completed and ongoing ATI studies. These new insights are useful in the discovery of pre-ATI predictors of better viremia control post ATI and evaluation of interventions that target different components of viral rebound process, ultimately improving our capacity to find a cure for HIV.

世界迫切需要推进艾滋病毒治疗研究议程，以解决全球艾滋病毒持续高企的问题。 患病率和相关死亡率。尽管联合抗逆转录病毒疗法（ART）在实现 持续控制病毒复制，关注副作用，药物相互作用，耐药性和 成本要求需要确定实现艾滋病毒根除或无ART缓解的战略。在ART之后 停药后，患者的病毒载量水平通常会迅速上升到峰值，然后下降，然后稳定在 病毒载量设定点。表征病毒反弹轨迹的特征（例如，病毒反弹时间和病毒 分析性抗逆转录病毒治疗中断（ATI）和确定宿主、病毒学和 预测这些特征的免疫学因素是HIV治愈研究的核心。但这样做需要 解决各种分析挑战，包括非线性病毒反弹轨迹，粗化数据 由于检测的定量限、病毒载量值的间歇性测量、 个体研究和高维候选预测因子。受我们与艾滋病毒持续合作的激励 治愈研究调查人员和建立在我们以前的工作，我们的目标是解决关键的方法差距， 利用艾滋病临床试验组进行的多项随机研究和 苏黎世主要HIV感染队列。目标1提出了一套新的方法来预测时间， 基于综合病史特征的病毒反弹，如ART启动后的病毒衰减率， 扩展拟合算法和变量选择技术开发的区间删失的结果。目的2 建议使用平滑模拟伪最大似然法拟合病毒反弹模型， 最大化基于蒙特卡罗近似的平滑模拟目标函数， 平滑响应的前两个时刻，并开发方法来评估时间 以反弹和病毒设定点，并同时选择生物标志物，影响不同的更精细的特征， 病毒反弹轨迹目标3提出了开发方法，最佳地整合数据，从多个 队列和病毒载量轨迹的不同阶段，同时适当考虑同质性， 研究间协变量效应的异质性。创新在于开发和应用新的 用于对病毒反弹进行建模的方法，其解决了可用数据分析中的各种固有挑战。 重要性在于这些方法在更好地表征病毒反弹轨迹、识别病毒反弹前的作用。 ATI的预测，并评估新的治疗药物的效果和机制。的结果 拟议的研究可以为未来ATI研究的优化设计提供信息，并提供新的工具， 已完成和正在进行的ATI研究中收集的数据信息。这些新的见解在 发现ATI后更好的病毒血症控制的前ATI预测因子，并评估针对 病毒反弹过程的不同组成部分，最终提高我们找到治愈艾滋病毒的能力。