HIV Drug Resistance Database

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PROJECT SUMMARY HIV drug resistance (HIVDR) is a threat to the success of antiretroviral (ARV) therapy (ART) and a major barrier to the elimination of AIDS as a public health problem. Persons with HIV who develop virological failure (VF) during ART are at high risk of developing HIVDR and transmitting a drug-resistant virus to others while persons primarily infected with a drug-resistant virus are at high risk of developing VF and a further increase in HIVDR. Comprehensive, accurate, and publicly available HIVDR data are essential for population-based monitoring of acquired and transmitted HIVDR, for the management of HIV-infected patients, and for identifying overall drug-development needs. A public database that curates, annotates, synthesizes, and disseminates data from HIVDR studies will make it possible to identify and characterize the HIVDR mutations most relevant to surveillance, clinical management, and drug development, and will expedite research into the mechanisms of HIVDR and the predictors of response to the newest ARV regimens. The Stanford HIV Drug Resistance Database (HIVDB) provides a unique conceptual framework for addressing data-intensive questions about the main molecular targets of HIV therapy: reverse transcriptase, protease, integrase, and capsid. HIVDB’s sequence analysis programs have also become integrated into the workflows of many research laboratories worldwide. Accomplishing the Aims of this proposal will assist researchers engaged in HIVDR surveillance, ART clinical trials, and ARV development by enabling them to identify gaps in the published literature, incorporate contributions from HIVDB into novel analyses, and discover new knowledge. Our first Aim will involve expanding HIVDB as a resource that provides the scientific foundations of the clinical and epidemiological significance of HIVDR mutations and that address gaps in HIVDR knowledge including the correlates of resistance to recently approved ARVs, established ARVs used for new indications, and the long-acting ARVs that will be used for prevention and treatment. We will implement strategies to increase data sharing to help ensure the long-term sustainability of this project. Our second Aim will involve extending the logic of HIVDB’s genotypic resistance test interpretation program to predict the virological response to ARV combinations and common ART regimens. We will demonstrate and promote the use of our sequence analysis software for the analysis of other pathogenic viruses for which antiviral therapy is available. Our third Aim will involve converting HIVDB into a fully open source and transferable project. Accomplishing this aim will support researchers using HIVDB to advance their research by enabling them to seamlessly integrate HIVDR data into their research. Accomplishing this aim will also foster the long-term sustainability of the HIVDB project.