Investigation of the clinical prognostic value for therapy, failure of HIV-1 drug resistance results combined with other clinical, virological and immunological parameters.

Choosing an optimal antiretroviral therapy is dependent on many factors, such as: the potency of a regimen; therapy adherence; side effects; individual therapy, toxicity and drug resistance history; but also an appropriate interpretation of resistance test results. We were at the forefront of developing genotypic drug resistance interpretation systems to assist clinical virologists and clinicians in their decision making. Originally, the Rega algorithm was based on published genotype-phenotype and genotype-clinical response data, as well as our own research results and expert opinion. More recently, we are also including information learned through our own data mining research. This datamining research is only possible through our continuous international collaborations with clinical centers that are providing data for analysis. In this context, we experienced the need to develop a database to communicate data with partners and at the same time we implemented tools to facilitate the individual patient follow-up of patients by clinicians and the queries and analyses performed by researchers. We would like to stay at the forefront in this research and to continue to make the difference for our patients. We feel the need for a deeper understanding of the resistance and adherence issues, and for further improvements of our clinical tools, therefore, our goals for the next few years will be: 1. To collect and manage demographic, clinical, virological and immunological data on HIV patients in a database system, and improve the system for clinical and research purposes. 2. To investigate the epidemiology of HIV drug resistance. 3. To investigate HIV evolution under in vivo drug selective pressure. 4. To characterize the in vitro phenotypic effect of observed or predicted mutations or combinations of mutations. 5. To investigate how therapy response prediction systems can be improved by complementing resistance data with other clinical information. 6. To improve and evaluate decision support software on independent datasets.

Keywords:Clinical virology

Disciplines:Scientific computing, Bioinformatics and computational biology, Public health care, Public health services, Microbiology, Systems biology, Laboratory medicine, Immunology