what we do

Background

Combination antiretroviral therapy is capable of suppressing HIV replication and preventing disease progression for a number of years. However in most patients, at some point there will be a loss of viral control often involving the development of drug resistance, which will require a change to the drug regimen. The selection of drugs for the next regimen is made from a list of around 25 from six classes and, in well-resourced settings, is commonly informed by genotypic antiretroviral resistance testing (GART) indicating to which drugs the virus should still be sensitive. However this is expensive, can miss resistant virus that exists at low levels and requires sophisticated interpretation. There are several interpretation systems in common use but the shortcomings are that they do not always agree with each other and will only indicate to which individual drugs the virus might be resistant or sensitive, not give a relative indication of the effectiveness of different combinations. Critically, resistance testing is beyond the reach of many healthcare providers around the world, making the individual optimisation of salvage therapy a major challenge.

The HIV Resistance Response Database Initiative (RDI), an independent not-for-profit research group, has developed computational models to predict virological response to a change of antiretroviral therapy following virological failure. The models are trained using real treatment response data from hundreds of physicians, treating hundreds of thousands of patients around the world. Currently the RDI database holds treatment and outcome data on over 150,000 patients, making it one of the largest HIV treatment databases in the world.

Early models used viral loads, CD4 counts, treatment history and resistance mutations from GART to predict response. In dozens of studies over many years these models have regularly demonstrated approximately 80% accuracy when tested with independent data sets and have been evaluated by expert HIV clinicians as being a useful clinical tool.1-2

More recently the RDI has developed models that do NOT require GART, for use in settings where it is unaffordable.3 The latest versions of these models have shown similar accuracy to modelling with GART in cross study comparisons and have even demonstrated significantly superior accuracy to the use of genotypic sensitivity scores derived from GART using rules-based interpretation. 4

The RDI’s models are used to power the RDI’s HIV Treatment Response Prediction System, or HIV-TRePS, which is available free of charge over the Internet. Healthcare professionals can use the system to:

  • Obtain estimates of the probability of virological response (plasma viral load <50 copies HIV RNAL/ml) for regimens of their choosing, at the follow-up time of their choosing
  • Model the probability of response for all combinations in clinical use and identify the ones most likely to give a response
  • Add in their local drug costs and identify the regimens most likely to provide a response within a given price cap

In a pilot study using cases of salvage therapy at a hospital in India, the models identified regimens with a higher probability of response and substantially lower costs than those used in the clinic at for the great majority of cases.5

References

  1. Revell AD, Wang D, Boyd MA, Emery S, Pozniak A, De Wolf F, et al. The development of an expert system to predict virological response to HIV therapy as part of an online treatment support tool. AIDS 2011;25:1855-1863.
  2. Larder, BA, Revell, AD, Mican J, Agan BK, Harris M, Torti C, et al. Clinical Evaluation of the Potential Utility of Computational Modeling as an HIV Treatment Selection Tool by Physicians with Considerable HIV Experience. AIDS Patient Care and STDs 2011; 25(1):29-3
  3. Revell AD, Wang D, Wood R et al. Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings. J Antimicrob Chemother 2013; 68(6):1406-14
  4. Revell AD, Wang DW, Wood R, et al. An update to the HIV-TRePS system: the development of new computational models that do not require a genotype to predict HIV treatment outcomes. J Antimicrob. Chemother. 2014; 69:1104-1110
  5. Revell AD, Alvarez-Uria G, Wang D, Pozniak A, Montaner JSG, Lane HC, Larder BA. Potential Impact of a Free Online HIV Treatment Response Prediction System for Reducing Virological Failures and Drug Costs after Antiretroviral Therapy Failure in a Resource-Limited Setting. BioMed Res Int 2013; doi 10.1155/2013/579741

 

Concept Sheet PDF RDI - Concept Sheet

An Introduction to TRePS An Introduction to HIV-TRePS

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