Mathematical modeling of optimal therapeutic combinations for HIV cure

HIV治愈最佳治疗组合的数学模型

基本信息

批准号：
10540716
负责人：
Joshua Tisdell Schiffer
金额：
$ 46.59万
依托单位：
FRED HUTCHINSON CANCER CENTER
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-12-16 至 2024-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10540716
关键词：
Achievement Acquired Immunodeficiency Syndrome Acute Adult Aftercare Agreement CAR T cell therapy CD4 Positive T Lymphocytes Caring Cell Therapy Cells Characteristics Chronic Clinic Clinical Trials Collaborations Combined Modality Therapy Consensus Consumption Creativeness Data Dose Drug Kinetics Encapsulated Experimental Designs Fostering Fred Hutchinson Cancer Research Center Gene Modified Genetic Variation Goals HIV HIV Infections HIV antiretroviral HIV therapy Health protection Home Human Immunologics Individual Infrastructure Infusion procedures Ingestion Interruption Intervention Kinetics Link Longevity Lymphocyte Military Personnel Mission Modeling Outcome Outcome Measure Pattern Peptide antibodies Persons Probability Public Health Research Research Personnel Running Safety Schedule Site Testing Therapeutic Time Training Treatment Efficacy United States National Institutes of Health Vaccination Vaccine Therapy Viral Virus Replication antiproliferative agents antiretroviral therapy collaboratory combinatorial control theory cost curative treatments effective intervention experimental study gene therapy gene transplantation for gene therapy in silico in vivo innovation mathematical model multimodality nanoparticle nonhuman primate pill prevent process optimization programs response simulation social stigma stem cells synthetic antibodies theories translational therapeutics transmission process treatment duration viral rebound

项目摘要

PROJECT SUMMARY Antiretroviral therapy (ART) suppresses HIV replication and allows a normal lifespan for infected persons, but daily pill ingestion is required to avoid progression to AIDS and further HIV transmission. Multiple therapeutic strategies are being considered to achieve a functional cure for HIV. However, to date, no single approach has achieved sufficient potency for an HIV functional cure. Therefore, there is increasing agreement that an HIV cure will require a multi-pronged approach. This proposal has the objective to identify optimal and feasible combinations of investigational therapeutic approaches to achieve functional cure of HIV using data-validated mathematical models. Our hypothesis is that data-validated mathematical models can identify specific mechanisms of therapeutic combinations, by linking observed kinetics and potency with various quantifiable outcome measures. Our specific aims will validate this hypothesis by fitting different mathematical models that encapsulate competing possible mechanisms to outcome data from curative interventions currently under study, including levels of different reservoir cellular subset, viral quantities, viral diversity and time to viral rebound. Model selection theory will be used to identify the most parsimonious models that reliably explain experimental results. We will use the most parsimonious model that recapitulated the data from each study to perform in silico experiments. We will list all plausible combinations of therapeutic approaches and model each combination. We will create combinatorial dose-response curves by running simulations for each combination by using the parameterization obtained from the fits and by tuning the parameters for each therapy including dosing, scheduling, and order of treatment. This proposal is significant because testing all possible combinations of approaches is impractical, excessively time consuming and expensive. The inability to rigorously assess all potential approaches is a critical barrier to achieve optimal outcomes. Therefore, our proposal is innovative because we propose a rigorous, quantitative framework in which plausible combinations of available interventions are considered and compared with the potential to identify which combination therapies most likely will achieve a functional cure.

项目概要抗逆转录病毒疗法 (ART) 可抑制 HIV 复制并让感染者获得正常的寿命，但需要每天服用药物以避免发展为艾滋病和进一步的艾滋病毒传播。多重治疗正在考虑实现艾滋病毒功能性治愈的策略。然而，迄今为止，还没有任何单一方法能够达到了艾滋病毒功能性治愈的足够效力。因此，越来越多的人认为治愈艾滋病毒需要采取多管齐下的方法。该提案的目标是确定最佳且可行的使用经过数据验证的研究性治疗方法组合来实现艾滋病毒的功能性治愈数学模型。我们的假设是，经过数据验证的数学模型可以识别特定的通过将观察到的动力学和效力与各种可量化的联系起来，治疗组合的机制结果措施。我们的具体目标将通过拟合不同的数学模型来验证这一假设概括目前正在研究的治疗干预措施结果数据的竞争可能机制，包括不同储存细胞亚群的水平、病毒数量、病毒多样性和病毒反弹时间。模型选择理论将用于识别能够可靠解释实验的最简约模型结果。我们将使用最简洁的模型来概括每项研究的数据，以在计算机中执行实验。我们将列出所有可能的治疗方法组合并对每种组合进行建模。我们将通过使用以下方法对每个组合运行模拟来创建组合剂量反应曲线从拟合中获得参数化并通过调整每种治疗的参数（包括剂量），安排和治疗顺序。这个提议很重要，因为测试了所有可能的组合这些方法不切实际、过于耗时且昂贵。无法严格评估所有潜在的方法是实现最佳结果的关键障碍。因此，我们的建议是有创新性的因为我们提出了一个严格的定量框架，其中可用的合理组合考虑干预措施并与潜力进行比较，以确定最有可能的联合疗法将达到功能性治愈。