EuResist

The EuResist project aims to develop a European integrated system for the clinical management of antiretroviral drug resistance. The system will predict patient reactions to antiretroviral treatments for HIV, thus helping clinicians to select the most appropriate drugs and drug combinations for any given HIV genetic variant. To this end a huge European integrated data set will be created, linking three of the largest existing resistance databases: ARCA, AREVIR and Karolinska.

While combination antiretroviral treatment has made HIV a treatable condition, eradication of the infection has not yet been achieved. Treatment needs to be administered as a prolonged, possibly lifelong treatment. Long-term toxicity, difficulty in adhering to complex regimes, possible pharmacokinetics problems, and intrinsically limited potency are all factors favouring the selection of drug-resistant viral strains. The development of drug resistance is now a major cause for treatment failure.

EuResist aims to:

  • integrate biomedical information from three large and expanding databases in different European countries collecting the required critical mass of historical and prospective data;
  • develop and validate models for the effective prediction of responses to treatment based on HIV genotype and additional clinical information;
  • make the prediction system available on the web for the optimisation of antiretroviral treatment.

For further information, please visit:
http://www.euresist.org

Project co-ordinator:
Informa S.r.l. (IT)

Partners:

  • Informa S.r.l. (IT)
  • Università degli Studi di Siena (IT)
  • Karolinska Institutet (SE)
  • Universitaetsklinikum Koeln (DE)
  • IBM Israel - Science and technology LTD (IL)
  • Max-Planck Gesellshaft zur Foerderung der Wissenshaften e.v. (DE)
  • MTA KFKI Reszecske-ES Magfizikai KutatoIntezet (HU)
  • Kingston University (UK)

Timetable: from 01/06 – to 06/08

Total cost: € 2.973.355

EC funding: € 2.143.000

Instrument: STREP

Project Identifier: IST-2004-027173

Source: FP6 eHealth Portfolio of Projects

Most Popular Now

Do Fitness Apps do More Harm than Good?

A study published in the British Journal of Health Psychology reveals the negative behavioral and psychological consequences of commercial fitness apps reported by users on social media. These impacts may...

AI Tool Beats Humans at Detecting Parasi…

Scientists at ARUP Laboratories have developed an artificial intelligence (AI) tool that detects intestinal parasites in stool samples more quickly and accurately than traditional methods, potentially transforming how labs diagnose...

Making Cancer Vaccines More Personal

In a new study, University of Arizona researchers created a model for cutaneous squamous cell carcinoma, a type of skin cancer, and identified two mutated tumor proteins, or neoantigens, that...

AI can Better Predict Future Risk for He…

A landmark study led by University' experts has shown that artificial intelligence can better predict how doctors should treat patients following a heart attack. The study, conducted by an international...

A New AI Model Improves the Prediction o…

Breast cancer is the most commonly diagnosed form of cancer in the world among women, with more than 2.3 million cases a year, and continues to be one of the...

AI System Finds Crucial Clues for Diagno…

Doctors often must make critical decisions in minutes, relying on incomplete information. While electronic health records contain vast amounts of patient data, much of it remains difficult to interpret quickly...