PUBLIC HIV DRUG RESISTANCE DATABASE

公共艾滋病毒耐药数据库

• 批准号: 9060859
• 负责人: ROBERT William SHAFER
• 金额: $ 76.29万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-05-01 至 2018-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9060859
• 关键词: Acquired Immunodeficiency Syndrome; Address; Adopted; Algorithms; Anti-Retroviral Agents; Antiretroviral drug resistance; Antiretroviral resistance; Applications Grants; Clinical; Clinical Trials; Collection; Country; Coupled; Data; Data Correlations; Databases; Development; Drug resistance; Epidemiologist; Funding; Future; Genetic; Genotype; Geographic Information Systems; HIV; HIV drug resistance; HIV-1; HIV-1 drug resistance; In Vitro; Individual; Knowledge; Laboratories; Licensing; Meta-Analysis; Methods; Modeling; Molecular; Molecular Cloning; Monitor; Morbidity - disease rate; Multi-Drug Resistance; Mutation; Nature; Pattern; Persons; Pharmaceutical Preparations; Phenotype; Population; Predisposition; Publishing; Reagent; Recombinants; Regimen; Research; Resistance; Resources; Risk; Source; South African; Testing; Treatment Protocols; United States National Institutes of Health; Variant; Viral; Virus; Work; acquired drug resistance; antiretroviral therapy; data sharing; drug development; evidence base; experience; improved; innovation; insight; model organisms databases; mortality; non-nucleoside reverse transcriptase inhibitors; novel; pandemic disease; population based; prevent; programs; public health relevance; research study; resistance mechanism; resistance mutation; response; success; tool; trend

DESCRIPTION (provided by applicant): Comprehensive, accurate, and publicly available HIV-1 antiretroviral (ARV) drug resistance data is essential for population-based monitoring of acquired and transmitted drug resistance in resource-limited regions, for guiding salvage ARV therapy in well-resourced regions, and for identifying overall ARV- development needs. However, the variability of viruses that comprise the HIV-1 pandemic and the high mutation rate of HIV-1 make it difficult to quantify transmitted and acquired drug resistance, to optimally interpret HIV-1 genotypic resistance tests, and to identify those ARV-resistant variants most relevant to the development of future ARVs. The Stanford HIV Drug Resistance Database (HIVDB) is the only publicly available source for three data correlations underlying HIV-1 drug resistance knowledge: (1) Correlations between the genetic sequences of the enzymatic targets of ARV therapy - PR, RT, and IN - with the ARV treatments of persons from whom sequenced HIV-1 variants are obtained; (2) Correlations between genotype and in vitro drug susceptibility; and (3) Correlations between genotype and the virological response to a new ARV treatment regimen. By emphasizing the collection, annotation, dissemination, and analysis of three main types of data, HIVDB facilitates meta-analyses in which data from many published studies and clinical trials can be effectively synthesized. The specific aims of this competing renewal for funding HIVDB are (1) To develop standardized genotypic methods to monitor the extent of transmitted and acquired drug resistance and to determine whether these methods can be applied across all HIV-1 subtypes; (2) To expand HIVDB by collecting, annotating, and disseminating the genotype-treatment, genotype-phenotype, and genotype-virological response data that inform genotypic resistance test interpretation. Coupled with this aim, we will improve the online framework for representing and describing the evidence basis associated with each genotypic resistance interpretation; and (3) To identify and preliminarily characterize representative and novel ARV-resistant variants that may help guide the development of future ARVs. Innovative aspects of this proposal include (1) the development of widely available and widely used online programs to facilitate data sharing and consistent analytic approaches across laboratories in many countries; (2) the development and implementation of regression methods and correlation network analyses to the study of HIV-1 drug resistance mutations; and (3) the use of data recruitment and curation methods adopted from model organism databases to create a sustainable model for the continuation and expansion of HIVDB.

描述(由申请人提供)：全面、准确和公开的HIV-1抗逆转录病毒(ARV)耐药性数据对于在资源有限的地区以人群为基础监测获得性和传播性耐药性、指导资源丰富地区的抢救ARV治疗以及确定总体ARV开发需求至关重要。然而，构成HIV-1大流行的病毒的变异性和HIV-1的高突变率使得量化传播和获得性耐药性、以最佳方式解释HIV-1基因耐药性测试以及识别与未来抗逆转录病毒药物发展最相关的抗逆转录病毒变异变得困难。斯坦福艾滋病毒耐药性数据库(HIVDB)是支持HIV-1耐药性知识的三个数据相关性的唯一公开来源：(1)ARV治疗的酶靶标-PR、RT和IN-的遗传序列与获得HIV-1序列变异的人的ARV治疗之间的相关性；(2)基因与体外药物敏感性之间的相关性；以及(3)基因与新ARV治疗方案的病毒学反应之间的相关性。通过强调收集、注释、传播和分析三种主要类型的数据，HIVDB促进了荟萃分析，其中可以有效地综合来自许多已发表的研究和临床试验的数据。这次为HIVDB提供资金的竞争更新的具体目的是(1)开发标准化的基因分型方法来监测传播和获得性耐药性的程度，并确定这些方法是否可以应用于所有HIV-1亚型；(2)通过收集、注释和传播基因治疗、基因表型和基因病毒学反应数据来扩大HIVDB，为基因耐药测试解释提供信息。结合这一目标，我们将改进在线框架，以表示和描述与每个基因耐药解释相关的证据基础；以及(3)识别和初步表征可能有助于指导未来抗逆转录病毒药物发展的具有代表性的和新型的抗逆转录病毒变异。这项建议的创新方面包括(1)开发广泛可用和广泛使用的在线程序，以促进许多国家的实验室之间的数据共享和一致的分析方法；(2)开发和实施回归方法和相关网络分析，以研究艾滋病毒-1耐药性突变；以及(3)使用从模式生物数据库中采用的数据招募和管理方法，为HIVDB的持续和扩展创建一个可持续的模式。