Public HIV Drug Resistance Database

公共艾滋病毒耐药数据库

基本信息

批准号：
7883346
负责人：
ROBERT William SHAFER
金额：
$ 62.98万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2006
资助国家：
美国
起止时间：
2006-05-01 至 2013-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7883346
关键词：
Acute Anti-Retroviral Agents Back CD4 Lymphocyte Count Chronic Clinical Collaborations Communities Data Databases Development Drug Design Drug resistance Evolution Genetic Genotype HIV HIV drug resistance HIV therapy HIV-1 HIV-1 drug resistance In Vitro Infection Knowledge Link London Machine Learning Maps Methods Minor Modeling Molecular Target Multi-Drug Resistance Mutation Outcome Pathway interactions Pattern Peptide Hydrolases Persons Pharmaceutical Preparations Phenotype Phylogenetic Analysis Population Dynamics Population Genetics Predisposition Prevalence Protocols documentation Publishing Recombinants Recording of previous events Research Research Personnel Resistance Treatment Protocols Universities Variant Viral Virus Work antiretroviral therapy base college data exchange fitness flexibility improved in vivo non-drug resistance mutation response transmission process trend

项目摘要

DESCRIPTION (provided by applicant): HIV drug resistance data are critical for HIV drug resistance surveillance, antiretroviral drug design, and the management of persons infected with drug-resistant HIV. Three fundamental types of correlations form the basis of drug resistance knowledge: (1) Correlations between genotypic data with the treatments of persons from whom sequenced HIV-1 isolates have been obtained (genotype-treatment); (2) Correlations between genotype and in vitro drug susceptibility (genotype-phenotype); and (3) Correlations between genotype and the clinical response to a new treatment regimen (genotype-outcome). Accordingly our three plans are as follows: (1) To use phylogenetically-based statistical analysis of correlations between mutations in the targets of HIV therapy (genotype) and antiretroviral treatment to distinguish between drug- and non-drug- related HIV-1 evolution, to identify temporal and geographic patterns in the prevalence of drug resistance, and to identify the spread of viruses with particular patterns of drug-resistance mutations. (2) To use patterns of mutations in the targets of antiretroviral therapy, antiretroviral treatment data, and in vitro drug susceptibility results to help prioritize the clinical development of experimental antiretroviral compounds. (3) To us correlations between genotype-phenotype and genotype-clinical outcome to identify the treatment regimens most likely to be effective in persons with acquired or transmitted drug resistance. In order to accomplish these aims, we will expand the online publicly available Stanford HIV RT and Protease Sequence Database to represent, store, and analyze the diverse forms of data underlying drug resistance knowledge. Standardized protocols for exchanging data with the broad community of researchers studying HIV-1 drug resistance will be refined and validated. Improved methods for representing published drug resistance data, performing temporal queries and knowledge discovery, and for analyzing genotypic resistance data in a phylogenetic context will be implemented. This proposal is also the start of a three-way collaboration between Stanford University, the University College of London (UCL), and Oxford University. UCL is on the forefront of research into the problem of transmitted drug resistance and Oxford University is on the forefront of research on virus evolution and population genetics.

描述（由申请人提供）：HIV 耐药性数据对于 HIV 耐药性监测、抗逆转录病毒药物设计以及耐药 HIV 感染者的管理至关重要。三种基本类型的相关性构成了耐药性知识的基础： (1) 基因型数据与已获得 HIV-1 测序分离株的人的治疗之间的相关性（基因型治疗）； (2)基因型与体外药物敏感性的相关性(基因型-表型)； (3) 基因型与新治疗方案的临床反应之间的相关性（基因型-结果）。因此，我们的三个计划如下：（1）使用基于系统发育的统计分析HIV治疗（基因型）和抗逆转录病毒治疗目标突变之间的相关性，以区分药物相关和非药物相关的HIV-1进化，确定耐药性流行的时间和地理模式，并确定具有特定耐药突变模式的病毒的传播。 (2) 利用抗逆转录病毒治疗靶点的突变模式、抗逆转录病毒治疗数据和体外药物敏感性结果来帮助优先考虑实验性抗逆转录病毒化合物的临床开发。 (3)利用基因型-表型和基因型-临床结果之间的相关性来确定对获得性或传播性耐药性最有可能有效的治疗方案。为了实现这些目标，我们将扩展在线公开的斯坦福 HIV RT 和蛋白酶序列数据库，以表示、存储和分析耐药知识背后的各种数据形式。用于与研究 HIV-1 耐药性的广大研究人员交换数据的标准化协议将得到完善和验证。将实施用于表示已发表的耐药性数据、执行时间查询和知识发现以及在系统发育背景下分析基因型耐药性数据的改进方法。该提案也是斯坦福大学、伦敦大学学院（UCL）和牛津大学三方合作的开始。伦敦大学学院处于传播耐药性问题研究的前沿，牛津大学处于病毒进化和群体遗传学研究的前沿。