Novel Statistical Methods for Improving the Prediction of HIV-1 Response to ART a

改善 HIV-1 对 ART 反应预测的新统计方法

• 批准号: 7167195
• 负责人: Matthew Rabinowitz
• 金额: $ 17.89万
• 依托单位: NATERA, INC.
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-06-15 至 2006-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7167195
• 关键词: genetics; model; phenotype

DESCRIPTION (provided by applicant): We will develop a set of statistical techniques that improve the prediction of the response of mutated Human Immunodeficiency Virus Type 1 (HIV-1) to anti-retroviral therapy. These techniques will have applicability to a wide array of clinical decisions beyond HIV where genotypic and phenotypic data may be used to predict patient outcomes. Current approaches to predicting clinical outcomes of anti-retroviral therapy, for the purpose of drug regimen selection, do not demonstrate strong concordance1,2. Two key reasons for these shortcomings are the lack of suitable statistical models that accurately characterize the affect of the many different combinations of mutations, and the lack of statistically significant samples of HIV/AIDS patients whose data includes baseline clinical status, treatment history, scans of the viral genomes, and clinical outcomes. It is often the case that the potential genetic and phenotypic predictors and their interactions result in a number of independent variables (IVs) that is large relative to the number of measured outcomes. The problem of limited data is compounded by the many different combinations of ART and viral mutations that are encountered in practice. To address this problem in part, Gene Security Network has already developed a system that facilitates the aggregation of genetic and clinical data sets into standardized computable format, using software cartridges tailored to each local source of data. This proposal focuses on the other aspect of the solution to limited data, namely the development of novel statistical methods that improve outcome prediction when the number of potential predictors is large compared to the number of measured samples. The Gene Security Network team has developed and published3,4 algorithms that create sparse models for predicting in vitro drug response based on viral genetic sequences. These approaches performed better than all previously published algorithms.1,5-7 Our aims for the phase I project are i) to extend the theoretical technique that underlies the superior performance of our models using in vitro data and ii) to implement and obtain FDA approval for a system at the Stanford Virology Lab that will produce enhanced in vitro drug susceptibility reports for treating physicians. In the potential phase II project, we will extend our techniques for modeling the in vitro response to modeling the more complex in vivo responses measured in terms of CD4+ and viral load counts. We would again seek FDA approval for the enhanced reporting system for phase II, which will rank regimens for the treating physician based on genetic and clinical data. A clinical trial would be hosted by the Stanford Virology Lab to demonstrate efficacy of the phase II enhanced reports in terms of improved outcomes and/or reduced cost of treatment. This project will improve the statistical methods used in predicting HIV-1 drug response, and will facilitate the use of these enhanced models for better treatment decisions. The statistical methods developed, and the software system for enhanced reporting based on laboratory cartridges, will have application to many diseases beyond HIV/AIDS where complex geno-pheno models can be used to enhance treatment decisions. Given the rollout of ART drugs around the world,25 the emergence of resistant strains of the virus is inevitable, both due to the low genetic barrier to resistance27-33 and to poor drug adherence.34 The rapidly decreasing cost of HIV genetic sequencing35 makes the selection of drugs based on viral genetic sequence an attractive option, rather than the more costly and involved in vitro phenotype measurement.36,37 However, current models for predicting response to anti-retroviral therapy do not demonstrate strong concordance, and physicians interpretation of resistance reports for drug regimen selection vary considerably.1,2,5 This project will improve the statistical methods for predicting HIV-1 drug response, and will facilitate the use of these enhanced models for better treatment decisions. The statistical methods developed, and the software system for enhanced reporting based on laboratory cartridges, will have application to many diseases beyond HIV/AIDS where complex genotype-phenotype models can be used to enhance treatment decisions.

描述（由申请人提供）：我们将开发一套统计技术，以改进对突变的人类免疫缺陷病毒1型（HIV-1）对抗逆转录病毒治疗反应的预测。这些技术将适用于HIV以外的广泛临床决策，其中基因型和表型数据可用于预测患者预后。目前预测抗逆转录病毒治疗临床结果的方法，以药物方案选择为目的，并没有显示出很强的一致性1,2。造成这些缺陷的两个关键原因是缺乏合适的统计模型来准确描述许多不同的突变组合的影响，以及缺乏具有统计意义的HIV/AIDS患者样本，这些样本的数据包括基线临床状态、治疗史、病毒基因组扫描和临床结果。通常情况下，潜在的遗传和表型预测因子及其相互作用会导致许多相对于测量结果数量较大的独立变量（IVs）。数据有限的问题由于在实践中遇到的许多不同的抗逆转录病毒治疗和病毒突变组合而变得更加复杂。为了在一定程度上解决这个问题，基因安全网络已经开发了一个系统，该系统使用针对每个本地数据源量身定制的软件盒，将遗传和临床数据集聚合成标准化的可计算格式。该建议侧重于解决有限数据的另一个方面，即开发新的统计方法，当潜在预测因子的数量比测量样本的数量大时，改进结果预测。基因安全网络团队已经开发并发布了3,4种算法，这些算法创建了稀疏模型，用于基于病毒基因序列预测体外药物反应。这些方法比以前发表的所有算法都表现得更好。1,5-7我们第一阶段项目的目标是I)扩展理论技术，这是我们模型使用体外数据的卓越性能的基础；ii)在斯坦福病毒学实验室实施并获得FDA批准的系统，该系统将为治疗医生提供增强的体外药物敏感性报告。在潜在的II期项目中，我们将扩展我们的体外反应建模技术，以模拟更复杂的体内反应，根据CD4+和病毒载量计数测量。我们将再次寻求FDA对II期强化报告系统的批准，该系统将根据遗传和临床数据为治疗医生排列方案。斯坦福大学病毒学实验室将主持一项临床试验，以证明II期增强报告在改善结果和/或降低治疗成本方面的有效性。该项目将改进用于预测HIV-1药物反应的统计方法，并将促进这些增强模型的使用，以做出更好的治疗决策。所开发的统计方法和基于实验室药盒的增强报告软件系统将适用于艾滋病毒/艾滋病以外的许多疾病，在这些疾病中，复杂的基因现象模型可用于加强治疗决策。鉴于抗逆转录病毒药物在世界各地的推广，出现耐药病毒株是不可避免的，这既是由于耐药的遗传屏障较低27-33，也是由于药物依从性差34HIV基因测序成本的迅速下降35使得基于病毒基因序列的药物选择成为一个有吸引力的选择，而不是更昂贵且涉及体外表型测量的药物然而，目前预测抗逆转录病毒治疗反应的模型并没有显示出很强的一致性，医生对药物方案选择的耐药报告的解释差异很大1,2,5该项目将改进预测HIV-1药物反应的统计方法，并将促进这些增强模型的使用，以做出更好的治疗决策。开发的统计方法和基于实验室药盒的增强报告软件系统将适用于艾滋病毒/艾滋病以外的许多疾病，在这些疾病中，复杂的基因型-表型模型可用于加强治疗决策。