Artificial intelligence identifies effective drugs for HIV patients whose treatment is failing

CABO, MEXICO; 12th June 2003 New data presented for the first time today at the 12th International Workshop on HIV Drug Resistance demonstrated that artificial intelligence (AI) could find effective treatments for patients whose drug therapy is failing. The system identified potentially effective drug combinations for patients who were continuing to fail on therapy, despite having their combinations of HIV drugs changed by their physicians according to current clinical practice.

"These patients had high viral loads and were failing because of drug resistance, despite multiple changes to their treatment and the use of current resistance tests", commented Professor Julio Montaner MD, Professor of Medicine and Chair in AIDS Research, at the BC Centre, University of British Columbia, Canada. "Today's results hold out the possibility of being able to reverse the process of treatment failure for such patients, using artificial intelligence to help us identify the best possible drug combination for the individual."

A 'Neural Network' mathematical model that predicts how patients will respond to different combinations of drugs with 79% accuracy was fed the genetic codes of the HIV from these failing patients. In every case it identified an alternative drug combination that it predicted would be effective. On average these alternative treatments were predicted to reduce the amount of virus in the patients' bloodstream by over 99%. Based on its 79% accuracy, had the system been used to select new drug combinations for these patients, 110 of the139 patients might have responded to treatment instead of failing.

"We were very excited when we saw the scale of these results" commented Dr Brendan Larder, Chair of the RDI's Scientific Core Group. "This is the first time that we have used this technique specifically to look for alternative treatments for patients experiencing multiple treatment failures. While the results of this small study will need to be confirmed, they certainly hold out great promise."

The study, which was conducted by the HIV Resistance Database Initiative (RDI), employed the AI technique called Neural Networks to model the relationships between changes in the genetic code, the genotype, of HIV and the response of patients to different drug combinations. The technique mimics the connections in the human brain and can 'learn' as it analyses. Data collected by the BC Centre for Excellence from 351 patients in Canada, who had received a great deal of HIV drug therapy, were used for the analysis. These data included the genetic codes (genotypes) of the patients' HIV, the drugs used to treat them and changes in the amount of virus in the bloodstream (viral load) following treatment.

The most accurate Neural Network model overall was able to predict the direction of viral load change to different drug combinations with 79% accuracy. When this model was then used to look for effective drug combinations for patients whose actual treatment was failing, based on the genotype of their virus, it successfully did so in all 139 cases.

The study was originally designed to evaluate the accuracy of Neural Network models developed using data from uncontrolled clinical cohorts. Previous results have demonstrated the accuracy of the technique but used highly regulated data from tightly controlled clinical trials and the objective was to compare the accuracy of models using much more variable data. The results showed that the models were at least as accurate, if not more accurate, than those developed using clinical trial data. The magnitude of changes in viral load predicted by the most accurate model in this study were very highly correlated with the actual changes experienced by the patients, with an R2 value of 0.75. This compares to an R2 value of 0.55 for the models developed using clinical trial data.

One of the main reasons why HIV treatment fails is that mutations in the genetic code of HIV cause resistance to HIV drugs. Tests are already available to read these genetic changes. The RDI's goal is to enhance the predictive power of such tests by relating genetic changes in HIV directly to virological response to drug therapy, using the largest collection of clinical data of its kind in the world.


RDI Limited is registered in the UK as a not-for-profit company limited by guarantee and with no share capital. The RDI is headquartered in London UK, with research based in Cambridge, UK.

The RDI is supported by grants and funds from the BC Centre for Excellence in HIV/AIDS in Vancouver, Canada (a Founding Academic Member of the RDI); SAIC-Frederick, for a study in collaboration with the US National Institute of Allergy and Infectious Diseases; and Gilead Sciences Inc.

Data provided to the RDI does not contain any information relating to patient identity.

The RDI Scientific Core Group comprises the following scientists and clinicians:

1. Brendan Larder - Cambridge, UK, Chair
2. Victor DeGruttola - Harvard School of Public Health, Boston, MA, USA
3. Richard Harrigan - BC Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada
4. Julio Montaner - BC Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada
5. Scott Wegner - US Military HIV/AIDS Program, Washington, DC, USA
6. Maurizio Zazzi - HIV Monitoring Service, University of Siena, Italy

Date published: 12th June 2003


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