Oral presentation at:
The XI International HIV Drug Resistance Workshop
2nd July 2002 - 5th July 2002
Seville, Spain
Larder BA, DeGruttola V, Hammer S, Harrigan R, Wegner S, Winslow S & Zazzi M. On behalf of the HIV Resistance Response Database Initiative (RDI)
Background
HIV drug resistance testing has become widely accepted as an important part of HIV clinical management, with genotyping the predominant methodology. However, there is imperfect understanding of the relationship between complex genotypes and clinical response. Thus, the development of reliable algorithms to predict outcome from genotype in relation to therapy will require analysis of large amounts of quality controlled data. Our objective was to develop a relational database to correlate HIV-1 drug resistance-associated genotypic data with response to antiretroviral agents. This is an academic initiative, where the data will be collectively 'owned' and a controlled by contributing institutions.
Methods
Individuals with expertise in HIV drug resistance, bioinformatics and statistics, from academia, industry and state institutions worldwide are participating. All other groups who would like to contribute data are invited to participate. Patient data including baseline genotypes, treatment histories and virological and clinical outcome are being collected. A number of sophisticated analytical techniques are being applied to an initial dataset, including recursive partitioning and neural networks. Power calculations are being performed to estimate the approximate number of required data points.
Results
An initial core team has been assembled, comprising individuals from North America, UK and Italy and has established broad terms of reference. An Oracle database has been constructed and is initially being populated with data from over 3500 patients. Power calculations have been performed to estimate the size of the database required to make statistically reliable predictions of virological response to different antiretrovirals. The results indicate that sample sizes between 2000 and 4000 are required for any specific regimen (or groups of regimens with similar genotype profiles) to detect moderate effects on HIV-1 RNA associated with particular patterns of mutations (detailed results reported elsewhere). Neural network models have been constructed using the prototype database (data reported in detail elsewhere). The results demonstrate that it is possible to use this approach to predict with accuracy VL change and VL trajectory from complex input variables.
Conclusions
By combining data, expertise and resources on a global basis, the RDI has the potential to provide a mechanism by which individual genotypes can be used to predict response to different antiretroviral drug combinations with a high degree of confidence. It is intended to make the database widely accessible to scientists and clinicians via the Internet.
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