Modelling response to HIV therapy without a genotype: an argument for viral load monitoring in resource-limited settings

Abstract

In the absence of widespread access to individualized laboratory monitoring, which forms an integral part of HIV patient management in resource-rich settings, the roll-out of highly active antiretroviral therapy (HAART) in resource-limited settings has adopted a public health approach based on standard HAART protocols and clinical/immunological definitions of therapy failure. The cost-effectiveness of HIV-1 viral load monitoring at the individual level in such settings has been debated, and questions remain over the long-term and population-level impact of managing HAART without it. Computational models that accurately predict virological response to HAART using baseline data including CD4 count, viral load and genotypic resistance profile, as developed by the Resistance Database Initiative, have significant potential as an aid to treatment selection and optimization. Recently developed models have shown good predictive performance without the need for genotypic data, with viral load emerging as by far the most important variable. This finding provides further, indirect support for the use of viral load monitoring for the long-term optimization of HAART in resource-limited settings.

Clinical Evaluation of the Potential Utility of Computational Modeling as an HIV Treatment Selection Tool by Physicians with Considerable HIV Experience

“…11,20 In addition the models are able to identify alternative combinations of antiretroviral drugs that are predicted to be effective for a substantial proportion of cases that resulted in virologic failure in the clinic and that were predicted to fail by the models. 21 These results suggest that computational models may have a useful role as an aid to antiretroviral treatment decisionmaking. Before making such models available, however, it is clearly important to test their potential utility in clinical practice and collect input from HIV physicians with regard to the design of the interface through which such models could be accessed.…”

“…In the meantime, an updated version of the system incorporating suggestions made by physicians in these studies is being made available as an experimental tool and a version that does not require genotypic resistance information is in development for resource-limited settings where these assays are not available. 21 …”

Bioinformatical Assistance of Selecting Anti-HIV Therapies: Where Do We Stand?

“…HLA type) is just emerging. Several approaches for providing information on patient history to the system have been investigated [17,19], some of them even affording the omission of explicit information on the viral genotype [20,21]. Furthermore, sampling based on new-generation sequencing will afford a higher-resolution image of the viral population harbored by the patient [22].…”

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

“…Thus, alternative models have been developed that do not require a genotype but rely on CD4 counts, viral loads, and treatment history for their predictions. This has resulted in only a small loss of performance to a level of accuracy at least comparable to that of using genotypic sensitivity scores (from genotyping with rules-based interpretation) as a predictor of response [11, 13–15]. These models are able to predict most of the cases where the salvage regimen selected in the clinic failed and are also able to identify alternative regimens comprising locally available drugs that are predicted to be effective [16, 17].…”