The EuResist project, funded under the EU's Sixth Framework Programme (FP6), aims to give clinicians the tools they need to predict what the response will be to antiretroviral treatment among HIV patients.
Nowadays, an HIV infection is no longer a death sentence: the number of approved treatments grows each year. However, according the experts, the virus is becoming resistant to an increasing number of drugs and is developing different strains. In order to ensure the effectiveness of anti-HIV drugs, a combination or 'cocktail' of drugs must be used according to the progress of an individual's virus and as resistance to the drugs change.
"Monitoring the history of treatments and the progress of the virus itself is crucial to successful patient care," says Boaz Carmeli, Manager of Healthcare and Life Science at the IBM Haifa Research Lab, one of the partners in the project. "Tapping into knowledge garnered from a huge collection of data will help doctors take into account the patient, the virus, the viral mutations, and the current stage of the disease."
The project will use an innovative approach to predict the efficiency of anti-retroviral drug regimens, developing a number of prediction engines, including evolutionary models, mutual information-based data mining and case-based reasoning.
Clinicians will then, finally, be able to select the best drugs and cocktails for treating their patients. The consortium will be using EuResist's biomedical information integration technology to collect data from three leading HIV databases in Europe: ARCA of Italy, AREVIR of Germany and that of Sweden's Karolinska Infectious Diseases and Clinical Virology Department.
The data includes treatment histories, treatment response information, and the sequence of the relevant part of the HIV genome (genotype). The resulting EuResist integrated data set is expected to be the largest in the world.
"If we look closely at the current patient's blood work, virus stage, family history, race, and so forth - and then compare it to the thousands of people who have been treated over the years, we can see what was done, what worked, and what didn't," notes Professor Maurizio Zazzi, EuResist scientific coordinator and Professor of Microbiology at the University of Siena School of Medicine.
On the basis of this history data, EuResist can predict how the virus will respond to a certain cocktail. "This method not only provides a huge saving in costs, it also means a patient's chances for successful treatment are not dependant on their doctor's individual knowledge," Professor Zazzi added.
A particularly beneficial aspect of EuResist will be web interaction. A physician is able to input a patient's information and status and then get a summary of what is known about this specific virus stage, along with a prediction of what treatment is likely to help the patient.
The EuResist database currently has access to information from over 17,000 patients and, according to the project partners, the project results so far are showing a tremendous success rate of 75%.
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