Computer models predict how patients will respond to HIV drugs

March 13, 2013, Oxford University Press

Results of a study published online in the Journal of Antimicrobial Chemotherapy today (Thursday), demonstrate that computer models can predict how HIV patients whose drug therapy is failing will respond to a new treatment. Crucially for patients in poorer countries, the models do not require the results of expensive drug resistance tests to make their predictions. The study also showed that the models were able to identify alternative drug combinations that were predicted to work in cases where the treatment used in the clinic had failed, suggesting that their use could avoid treatment failure.

When a patient's begin to fail in well-resourced countries a genotypic resistance test is performed to identify that cause the virus to become resistant to certain drugs. The results are used to predict whether the patient will respond to different drugs in a new treatment. These tests are generally not available in resource-limited settings. Today's study shows that computer models can predict how such a will respond, with comparable accuracy, without the need for such tests.

"This is the first time this approach has been tried with real cases of from resource-limited settings", commented Julio Montaner, former President of the International AIDS Society, Director of the BC Centre for Excellence in & , based in Vancouver, Canada and an author on the paper. "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 gathered from many thousands of patients in hundreds of clinics around the world. They make their predictions of the probability of the drugs reducing the level of virus below a low limit from the patient's treatment history, the CD4 count(i) and the viral load. The models were most accurate (over 70%) when they were used to make predictions for new cases from the clinics that provided the data used in their training. However they were 60-64% accurate when tested with cases from southern Africa, 57% for India and 67% for Romania where resources are very limited. This compares favourably with accuracy of around 60-65% for genotyping, with interpretation using standard algorithms.

The models were also able to identify alternative, three-drug regimens, comprising locally available drugs that were predicted to produce a virological response for a substantial proportion of the treatment failures observed. This proportion ranged from 75% in Southern Africa, where the number of drugs available was highly restricted, to 93% in Romania and 99% in India. In all cases from the resource-limited countries, the models were able to identify regimens with a higher predicted probability of success than the regimen that failed.

"These results suggest that use of the system could help to avoid significant numbers of treatment failures", commented lead author Andrew Revell of the RDI. "The superiority of the models with cases from 'familiar' settings indicates that the full potential of this system to aid treatment decision-making could be realised by the collection of data from resource-limited settings and the use of these data to train the next generation of models."

The models are now available free of charge on the RDI web site at

It should be noted that, as the study was retrospective, no firm claims can be made for the clinical benefit that use of the system as a treatment support tool could provide. Nevertheless, the results were positive for clinics and cohorts in many different countries across five disparate regions of the world, which is very encouraging in terms of generalizability. The results also suggest that this approach has the potential to reduce virological failure and improve patient outcomes in less well-resourced countries.

Explore further: Drug designer: New tool reveals mutations that cause HIV-drug resistance

More information: 'Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings' by Andrew D. Revell, Dechao Wang, Robin Wood, Carl Morrow, Hugo Tempelman, Raph Hamers, Gerardo Alcarez-Uria, Adrian Streinu-Cercel, Luminita Ene, Annemarie Wensing, Frank De Wolf, Mark Nelson, Julio Montaner, H. Lane, and Brendan Larder, Journal of Antimicrobial Chemotherapy, DOI: 10.1093/jac/dkt041

Related Stories

Drug designer: New tool reveals mutations that cause HIV-drug resistance

July 8, 2011
Protease inhibitor drugs are one of the major weapons in the fight against HIV, the virus that causes AIDS, but their effectiveness is limited as the virus mutates and develops resistance to the drugs over time. Now a new ...

Antiretroviral therapy has tripled the proportion of adults achieving undetectable levels of HIV

October 9, 2011
Over the past decade in western Europe there has been a dramatic improvement in the ability of antiretroviral therapy to keep HIV under control in adults with virological failure to drugs from all three of the original antiretroviral ...

Recommended for you

Researchers find latent HIV reservoirs inherently resistant to elimination by CD8+ T-cells

January 22, 2018
The latest "kick-and-kill" research to eliminate the HIV virus uncovered a potential obstacle in finding a cure. A recent study by researchers at the George Washington University (GW) found that latent HIV reservoirs show ...

HIV-1 genetic diversity is higher in vaginal tract than in blood during early infection

January 18, 2018
A first-of-its-kind study has found that the genetic diversity of human immunodeficiency virus type 1 (HIV-1) is higher in the vaginal tract than in the blood stream during early infection. This finding, published in PLOS ...

War in Ukraine has escalated HIV spread in the country: study

January 15, 2018
Conflict in Ukraine has increased the risk of HIV outbreaks throughout the country as displaced HIV-infected people move from war-affected regions to areas with higher risk of transmission, according to analysis by scientists.

Researchers offer new model for uncovering true HIV mortality rates in Zambia

January 12, 2018
A new study that seeks to better ascertain HIV mortality rates in Zambia could provide a model for improved national and regional surveillance approaches, and ultimately, more effective HIV treatment strategies.

New drug capsule may allow weekly HIV treatment

January 9, 2018
Researchers at MIT and Brigham and Women's Hospital have developed a capsule that can deliver a week's worth of HIV drugs in a single dose. This advance could make it much easier for patients to adhere to the strict schedule ...

New long-acting, less-toxic HIV drug suppresses virus in humanized mice

January 8, 2018
A team of Yale researchers tested a new chemical compound that suppresses HIV, protects immune cells, and remains effective for weeks with a single dose. In animal experiments, the compound proved to be a promising new candidate ...


Please sign in to add a comment. Registration is free, and takes less than a minute. Read more

Click here to reset your password.
Sign in to get notified via email when new comments are made.