Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings

Abstract

We developed computational models that predict virological response to ART without a genotype with comparable accuracy to genotyping with rule-based interpretation. These models have the potential to help optimize antiretroviral therapy for patients in RLSs where genotyping is not generally available.

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

“…While the models were not as accurate for Indian cases as they were with cases from the countries that provided the training data, a phenomenon seen in previous studies, their predictive accuracy was comparable to that seen historically from genotyping with rules-based interpretation [11]. …”

“…These models were developed and validated during 2011 using methodology described in detail elsewhere [10, 11]. The models were trained to estimate the probability of virological response, defined as a follow-up plasma viral load of less than 400 copies of HIV RNA/mL, this being the lower limit of detection of some assays in use at the time that the data were collected by the various collaborating clinics.…”

“…The HIV Resistance Response Database Initiative (RDI) has developed computational models that use genotype, viral load, CD4 count, and treatment history variables to predict the response to a new drug combination following virological failure with approximately 80% accuracy [9–11]. The models have been used to power a free experimental web-based HIV treatment response prediction system (HIV-TRePS) assessed by experienced HIV physicians in two clinical pilot studies as a useful aid to clinical practice [12].…”

“…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].…”

Predicting live birth for poor ovarian responders: the PROsPeR concept

“…Finally, through adequate calibration, PROsPeR eliminates the centre variability. Although these principles have not previously been applied to ART predictive models, they are commonly used in other pathologies, including oncology, human immunodeficiency virus and acquired immune deficiency syndrome (Barretina et al, 2012;Revell et al, 2013). Development of population and treatment-specific models enable comparison of different treatments and thus identify the medication associated with the highest probability of success for an individual patient.…”