A new study published in the Journal of Antimicrobial Chemotherapy explores computational models that can predict human responses to HIV therapy, which could reduce treatment failure.
The study's objective was to develop models that could predict response to antiretroviral therapy (ART), which previously has been done be genotypic HIV drug-resistance testing, which is unavailable in many resource-limited settings (RLSs).
"This is the first time this approach has been tried with real cases of treatment failure from resource-limited settings," Julio Montaner, former President of the International AIDS Society, Director of the BC Centre for Excellence in HIV & AIDS, based in Vancouver, Canada and an author on the paper, said in an announcement accompanying the study. "The results show that using sophisticated computer based algorithms we can effectively put the experience of treating thousands of patients into the hands of the under-resourced physician with potentially huge benefits."
The models were developed by the HIV Resistance Response Database Initiative (RDI) using information from thousands of clinics around the world. They were able to identify alternative, three-drug regimen, comprised of locally available drugs "that were predicted to produce a virological response for a substantial proportion of the treatment failures observed." Their figures of accuracy compared favorably to genotyping--70 percent in some cases, 60 to 64 percent in others--compared to an accuracy of roughly 60 to 65 percent for genotyping, according to the announcement.
"We developed computational models that predict virological response to ART without a genotype with comparable accuracy to genotyping with rule-based interpretation," the study's authors said. "These models have the potential to help optimize antiretroviral therapy for patients in RLSs where genotyping is not generally available."
Computational models can serve a variety of health populations. Last month, for instance, researchers detailed their discovery of a computer algorithm that can quantify the level of risk of breast cancer in women in the Journal of the National Cancer Institute.
To learn more:
- read the study
- read the announcement
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