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）：我们将开发一组统计技术，以改善突变的人类免疫缺陷病毒1型（HIV-1）对抗返回病毒疗法的反应。这些技术将适用于HIV以外的各种临床决策，在HIV之外，可以使用基因型和表型数据来预测患者的结局。目前，用于预测抗逆转录病毒疗法的临床结局的方法，为了进行药物治疗方法，并未表现出强烈的一致性1,2。这些缺点的两个关键原因是缺乏适当的统计模型，这些模型可以准确地表征突变的许多不同组合的影响，并且缺乏数据的HIV/AIDS患者的统计学意义样本，其数据包括基线临床状况，治疗史，病毒基因组的扫描，病毒基因组和临床结果。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自变量（IVS）相对于测量结果的数量很大。在实践中遇到的艺术和病毒突变的许多不同组合，有限数据的问题更加复杂。为了部分解决此问题，基因安全网络已经开发了一个系统，该系统促进了使用针对每个本地数据源量身定制的软件墨盒，将遗传和临床数据集的聚合到标准化的可计算格式中。该建议集中于有限数据解决方案的另一个方面，即新型统计方法的开发，这些方法与测量样品的数量相比，当潜在预测因子的数量较大时，可以改善结果预测。基因安全网络团队已经开发了3,4算法，这些算法创建了稀疏模型，用于根据病毒遗传序列预测体外药物反应。这些方法的性能要比所有先前发表的算法要好。1,5-7我们的目标是I期项目的目标是I）扩展理论技术，该技术是使用体外数据和II的模型的卓越性能的基础，以实施并获得Stanford病毒学实验室的FDA批准，该系统将在Stanford Virology Lab上产生增强的体外药物敏感性报告，以治疗体外药物的能力研究。在潜在的II期项目中，我们将扩展我们的技术，以建模体外反应，以建模以CD4+和病毒载荷计数测量的更复杂的体内反应。我们将再次寻求FDA批准的II期增强报告系统，该系统将根据遗传和临床数据对治疗医师的方案进行排名。斯坦福病毒学实验室将举办一项临床试验，以证明II期增强报告的功效，以改善的结果和/或降低的治疗成本。该项目将改善用于预测HIV-1药物反应的统计方法，并将促进这些增强模型的使用来更好地治疗决策。开发的统计方法以及基于实验室墨盒增强报告的软件系统将应用于艾滋病毒/艾滋病以外的许多疾病，可以使用复杂的Geno-Pheno模型来增强治疗决策。鉴于世界各地的艺术药物的推出，25由于遗传障碍的低遗传障碍27-33和药物依从性不佳，该病毒的抗性菌株的出现是不可避免的。34艾滋病毒遗传测序的迅速下降的成本迅速下降，基于病毒遗传序列的量度3，而不是更具量度的药物，而不是衡量药物的5个，而不是衡量的遗传序列。预测对抗逆转录病毒疗法反应的模型并没有表现出强烈的一致性，医生对药物方案选择的抗药性报告的解释差异很大。1,2,5该项目将改善预测HIV-1药物反应的统计方法，并将促进这些增强模型的使用来更好地治疗决策。开发的统计方法以及基于实验室墨盒增强报告的软件系统将应用于艾滋病毒/艾滋病以外的许多疾病，在这些疾病中，可以使用复杂的基因型 - 表型模型来增强治疗决策。