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 20 or so from six classes. In well-resourced settings, the selection of drugs 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 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 or budget 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 250,000 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.1-3
The RDI has also 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 consistently demonstrated significantly superior accuracy to the use of genotypic sensitivity scores derived from GART using rules-based interpretation. 3,4
Most recently, we have developed models that predict the absolute change in viral load over time, following the introduction of a new regimen, for use in settings where the definition of response is greater than <50 copies. The predictions of these models correlate with the actual changes over time with a coefficient of around 0.7.5
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) or the change in viral load over time for regimens of their choosing, at the follow-up time of their choosing
- Model the probability of response or the change in viral load over time for all combinations in clinical use and identify the ones most likely to give a response
- Add 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.6
References
- Revell AD, Wang D, Boyd MA, 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.
- Larder, BA, Revell, AD, Mican J, 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
- Revell AD, Wang D, Perez-Elias M-P, et al. 2018 update to the HIV-TRePS system: the development of new computational models to predict HIV treatment outcomes, with or without a genotype, with enhanced usability for low-income settings. Journal of Antimicrobial Chemotherapy; 73(8):2186-2196.
- 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
- Revell AD, Wang D, Perez-Elias MJ et al. Predicting virological response to HIV treatment over time: a tool for settings with different definitions of virological response. JAIDS (in press)
- 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
RDI - Concept Sheet