HIV incidence testing in an evolving epidemic: Identification of accurate multi-assay algorithms that include serosignatures from a novel antibody profiling system.

不断发展的流行病中的艾滋病毒发病率检测：识别准确的多重检测算法，其中包括来自新型抗体分析系统的血清签名。

• 批准号: 10053294
• 负责人: SUSAN H ESHLEMAN
• 金额: $ 69.51万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2022-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10053294
• 关键词: AIDS prevention; AIDS/HIV problem; Address; Algorithms; Anti-Retroviral Agents; Antibodies; Antibody Response; Bioinformatics; Biological Assay; Biological Markers; Blood specimen; CD4 Lymphocyte Count; Clinical Trials; Cohort Studies; Cross-Sectional Studies; DNA sequencing; Data; Disease; Drug usage; Early treatment; Enzyme Immunoassay; Epidemic; Evaluation; Frequencies; Guidelines; HIV; HIV Infections; HIV prevention trial; HIV serology; Health Benefit; Human; Immunoassay; Immunoprecipitation; Incidence; Individual; Infection; Intervention; Laboratories; Libraries; Life; Measures; Methods; Monitor; Oligonucleotides; Peptides; Performance; Phage Display; Pharmaceutical Preparations; Population; Population Heterogeneity; Populations at Risk; Prevention; Proteome; Public Health; Research; Research Personnel; Resources; Risk; Sampling; Science; Serology; Statistical Models; Surveillance Methods; System; Testing; Treatment Efficacy; Viral; Viral Load result; Virus; Woman; Work; antiretroviral therapy; base; biomarker discovery; cohort; cost; cost effective; design; efficacy evaluation; experience; follow-up; high risk; improved; next generation; next generation sequencing; novel; pre-exposure prophylaxis; pregnant; preventive intervention; repository; scale up; sexual HIV transmission; therapy design; treatment guidelines

PROJECT SUMMARY Accurate HIV incidence estimates are critical for monitoring the HIV/AIDS epidemic and evaluating interventions for HIV prevention. We have developed multi-assay algorithms (MAAs) that provide accurate incidence estimates. However, there are new challenges in this field of research. With increasing use of antiretroviral drugs for HIV treatment and prevention and a push towards early treatment initiation, more individuals, including those with recent infection, will be virally suppressed. This will impact cross-sectional incidence testing: higher rates of viral suppression will increase misclassification with standard serologic incidence assays; low viral load (VL) will no longer serve as biomarker for non-recent infection; and use of HIV diversity assays for incidence testing will be problematic, since it may not be possible to analyze samples with low VLs. Our hypothesis is that well-characterized samples, novel assays, and statistical modeling can be used to develop methods that provide accurate cross-sectional incidence estimates in the evolving landscape of HIV treatment and prevention. The Specific Aims of this project are: Aim 1: Expand a repository of well-characterized samples with information on the duration of HIV infection; use these samples to evaluate performance of HIV incidence assays. Our repository includes >17,000 samples from individuals with known duration of infection. We will continue to expand this repository, focusing on key populations and settings with high rates of viral suppression. These samples repository will be used to evaluate serologic HIV incidence assays. Aim 2: Use massively multiplexed VirScan assay to identify serosignatures that discriminate between recent and non-recent HIV infection. VirScan uses phage display, immuno-precipitation, and next generation sequencing to measure antibody reactivity to >3,300 HIV peptides. We will test samples from Aim 1 with VirScan and will use the data to identify “serosignatures” that distinguish between recent and non-recent infection, independent of VL. We will also use VirScan data to develop multi-peptide immunoassays (EIAs). Aim 3: Develop MAAs for HIV incidence estimation and validate the top-performing MAAs using independent sample sets from cohort studies and clinical trials with known HIV incidence. Data from Aims 1 and 2 will be used to identify MAAs that maximize accuracy and minimize cost of cross-sectional incidence testing. The performance of MAAs and VirScan-based EIAs will be validated by comparing incidence estimates obtained with these methods to those observed in longitudinal follow-up in cohorts and clinical trials. Based on our prior work and preliminary data, we believe that these studies will identify accurate, cost- effective methods for cross-sectional HIV incidence estimation for use in diverse populations and settings. This work will have direct public health benefit, providing improved methods for surveillance of the HIV/AIDS epidemic, targeting prevention interventions, and design and evaluation of HIV prevention trials.

项目摘要 准确的HIV发病率估计对于监测HIV/AIDS流行和评估至关重要 预防艾滋病毒的干预措施。我们已经开发了提供准确的多选算法（MAA） 发病率估计。但是，这一研究领域存在新的挑战。随着越来越多的使用 用于艾滋病毒治疗和预防的抗逆转录病毒药物，并推动早期治疗倡议，更多 包括近期感染的人，几乎将被抑制。这将影响横截面 发病率测试：较高的病毒抑制率将与标准血清学的错误分类增加 发病率;低病毒负荷（VL）将不再用作非幼儿感染的生物标志物；和艾滋病毒的使用 发病率测试的多样性评估将是有问题的，因为可能无法分析样本 低VLS。我们的假设是，特征良好的样本，新颖的测定和统计建模可以是 用于开发提供准确的横截面事件估计值的方法 艾滋病毒治疗和预防。该项目的具体目的是： AIM 1：扩展一个特征良好的样本的存储库，并提供有关HIV感染持续时间的信息； 使用这些样本评估HIV事件评估的性能。我们的存储库包括> 17,000 来自已知感染持续时间的个体的样本。我们将继续扩展此存储库， 专注于具有高抑制率的关键人群和设置。这些样品存储库将 用于评估血清学HIV事件测定法。 AIM 2：使用大量多重的VirScan测定 和非肉体艾滋病毒感染。 VirScan使用噬菌体显示，免疫预测和下一代 测量对> 3,300 HIV肽的抗体反应性的测序。我们将从AIM 1中测试样本 VirScan，并将使用数据来识别区分最近和非续签的“血清签名” 感染，独立于VL。我们还将使用VirScan数据来开发多肽免疫测定（EIA）。 目标3：开发用于艾滋病毒发病率估计的MAA，并使用独立验证表现最佳的MAA 来自队列研究和临床试验的样本集，具有已知的HIV入射。 AIM 1和2的数据将是 用于识别最大化精度并最大程度地减少横截面事件测试成本的MAA。这 通过比较获得的事件估计，将验证MAA和基于VirScan的EIA的性能 这些方法是在队列和临床试验中纵向随访中观察到的方法。 根据我们先前的工作和初步数据，我们认为这些研究将确定准确，成本 - 在潜水员种群和设置中使用的横断面HIV事件估计的有效方法。 这项工作将具有直接的公共卫生益处，为监视艾滋病毒/艾滋病提供了改进的方法 流行病，针对预防干预措施以及艾滋病毒预防试验的设计和评估。

项目成果

Sample size methods for estimating HIV incidence from cross-sectional surveys.

• DOI: 10.1111/biom.12336
• 发表时间: 2015-12
• 期刊: Biometrics
• 影响因子: 1.9
• 作者: Konikoff J;Brookmeyer R
• 通讯作者: Brookmeyer R

Intensifying Antiretroviral Therapy With Raltegravir and Maraviroc During Early Human Immunodeficiency Virus (HIV) Infection Does Not Accelerate HIV Reservoir Reduction.

• DOI: 10.1093/ofid/ofv138
• 发表时间: 2015-12
• 期刊: Open forum infectious diseases
• 影响因子: 4.2
• 作者: Ostrowski M;Benko E;Yue FY;Kim CJ;Huibner S;Lee T;Singer J;Pankovich J;Laeyendecker O;Kaul R;Kandel G;Kovacs C
• 通讯作者: Kovacs C

Cross-Sectional HIV Incidence Estimation with Missing Biomarkers.

缺少生物标志物的横截面艾滋病毒发病率估计。

• DOI: 10.1515/scid-2017-0003
• 发表时间: 2018
• 期刊: Statistical communications in infectious diseases
• 作者: Morrison,Doug;Laeyendecker,Oliver;Konikoff,Jacob;Brookmeyer,Ron
• 通讯作者: Brookmeyer,Ron