Predicting HIV-1 escape from therapeutics in vitro and in vivo - toward personalizing medicine for people living with HIV

预测 HIV-1 从体外和体内治疗中逃逸 - 为 HIV 感染者提供个性化医疗

• 批准号: 10619924
• 负责人: Hillel Haim
• 金额: $ 57.38万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-21 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10619924
• 关键词: Aftercare; Algorithms; Amino Acids; Anti-Retroviral Agents; Antiviral Agents; Appearance; Blood; Blood Cells; Clinical; Clinical Trials; Conserved Sequence; Drug resistance; Exhibits; Foundations; Future; Glycoproteins; Goals; HIV; HIV-1; Human; In Vitro; Individual; Investigation; Knowledge; Measurable; Measures; Mediating; Medicine; Methods; Modeling; Molecular Cloning; Monitor; Morbidity - disease rate; Mutation; Nature; Outcome; Patient Monitoring; Patients; Pattern; Persons; Plasma; Positioning Attribute; Probability; Property; Proteins; Publishing; Reducing Agents; Resistance; Resistance development; Sampling; Site; Source; Specificity; Testing; Therapeutic; Therapeutic antibodies; Time; Variant; Viral; Viral Genome; Viral Proteins; Viral reservoir; Virus; Virus Latency; Work; de novo mutation; deep sequencing; fitness; in vitro testing; in vivo; mortality; mutant; mutation screening; neutralizing antibody; peripheral blood; personalized medicine; predictive modeling; resistance mutation; targeted treatment; therapeutic target; therapy resistant; timeline; tool; treatment strategy; viral fitness

Project summary Antiretroviral agents reduce morbidity and mortality in HIV-infected individuals; however, mutations in the viral genome often result in clinical resistance to their effects. Due to the random nature of the mutations, the emergence of therapy-resistant mutants is mostly considered unpredictable. Consequently, antiviral treatment strategies are mainly empiric. The premise of this proposal is that the emergence rate of therapy-resistant mutants in each patient is largely predictable. Our published and preliminary studies suggest that changes in virus proteins can be accurately forecasted based their sequence properties before initiation of treatment. In the proposed study, we will use the example of the HIV-1 envelope glycoproteins (Envs). Several broadly neutralizing antibody (BNAb) therapeutics that target Env have shown great promise in clinical trials. However, escape from these agents often occurs after treatment, at different rates for different patients. The goal of this work is to advance our ability to personalize BNAb therapeutics to people living with HIV, by establishing the tools to determine the likelihood of each patient to develop resistance to each agent. The central hypothesis of this proposal is that each swarm of HIV-1 that infects a patient has an inherent and measurable likelihood to escape from each therapeutic. This likelihood is shared by the viruses that circulate in the blood and the reservoir of latent viruses, which is often the source of resistant mutants that emerge after therapy. To test the above hypothesis, we will first determine if the rate of HIV-1 escape from BNAbs is specific for each swarm of the virus that infects a patient. To this end, we will test in vitro the escape of strains from different patients that were isolated from samples collected at different time points. We will then test the ability of our models to forecast the rate and site of escape for each strain. Based on these studies, our models will be refined and applied to determine their ability to forecast resistance rates in four clinical trials of BNAbs in humans. Next, we will determine whether the appearance of resistant mutants is driven by their fitness (i.e., higher likelihood to appear before treatment) or by their resistance (i.e., higher replicative capacity after treatment). Such knowledge is critical for our ability to use patient samples before treatment, which inform of the viral fitness profiles, to predict escape from the treatment. We will then examine whether the fitness profile of BNAb escape sites is a persistent property of each virus swarm by measuring the changes that occur over time in patients. Finally, we will induce outgrowth of latent HIV-1 from peripheral blood cells and compare their fitness profiles at BNAb escape sites with those of viruses that circulate in the blood. The models to be developed have the potential to make important contributions to the treatment of patients by antiviral agents. They will lay the foundations for personalized antiviral medicine that is based on the likelihood of each virus swarm in a patient to develop resistance to each agent.

项目摘要 抗逆转录病毒药物降低了HIV感染者的发病率和死亡率;然而， 基因组中的突变通常导致对其作用的临床抗性。由于突变的随机性， 治疗抗性突变体的出现通常被认为是不可预测的。因此，抗病毒治疗 策略主要是经验性的。这一建议的前提是， 每个病人体内的突变体在很大程度上是可以预测的。我们发表的和初步的研究表明， 在开始治疗之前，可以基于病毒蛋白的序列特性准确地预测病毒蛋白。在 在本研究中，我们将以HIV-1包膜糖蛋白（Envs）为例。几个广泛 靶向Env的中和抗体（BNAb）治疗剂在临床试验中显示出巨大的前景。然而，在这方面， 这些药物的逃逸通常发生在治疗后，对于不同的患者，逃逸的速率不同。这个目标 我们的工作是提高我们对艾滋病毒感染者进行个性化BNAb治疗的能力， 确定每个患者对每种药物产生耐药性的可能性的工具。 这一建议的中心假设是，感染患者的每一群HIV-1都有一个内在的， 从每种治疗药物中逃脱的可能性。这种可能性是共享的病毒， 血液和潜伏病毒的水库，这往往是耐药突变体的来源，出现后， 疗法 为了检验上述假设，我们将首先确定HIV-1从BNAb逃逸的速率是否对每种BNAb都是特异性的。 感染病人的病毒群。为此，我们将在体外测试不同菌株的逃逸， 从不同时间点采集的样本中分离的患者。然后我们将测试我们的能力 模型来预测每种菌株的逃逸率和逃逸地点。基于这些研究，我们的模型将 改进和应用，以确定他们的能力，预测耐药率在四个临床试验的BNAbs， 人类接下来，我们将确定抗性突变体的出现是否是由它们的适应性驱动的（即， 在治疗前出现的可能性更高）或通过它们的抗性（即，高复制能力 治疗）。这些知识对于我们在治疗前使用患者样本的能力至关重要， 病毒适应度曲线来预测逃避治疗然后，我们将研究健身概况是否 BNAb逃逸位点的数量是每个病毒群的一个持久特性， 病人的时间。最后，我们将从外周血细胞中诱导潜伏的HIV-1的生长，并比较它们的 BNAb逃逸位点的适应性特征与血液中循环的病毒的适应性特征。 待开发的模型有可能通过以下方式对患者的治疗做出重要贡献： 抗病毒剂。他们将为个性化抗病毒药物奠定基础， 每一个病毒群在病人身上产生抗药性。