Oral presentation at:
The XI International HIV Drug Resistance Workshop
Seville, 2-5 July 2002
The International HIV Resistance Response Database Initiative: A
New Global Collaborative Approach to Relating Viral Genotype and Treatment
to Clinical Outcome.
B.A. Larder, V.DeGruttola, S. Hammer, R. Harrigan, S. Wegner, D. Winslow
& M. Zazzi. On behalf of the HIV Resistance Response Database Initiative
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.
Conclusion: 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