A European Multidisciplinary Network for the study of HIV and other infectious deseases.

The EuResist Project

The EuResist Network was born from the EuResist project.

The EuResist project (2006-2008) received a grant from the European Commission (contract IST-2004-027173 Eur 2,143,000) to develop an integrated European system for computer-based clinical management of antiretroviral drug resistance.

The resulting system (the EuResist combined prediction engine) provides the clinicians with internet-based prediction of clinical response to antiretroviral treatment in HIV patients, thus helping to choose the best drugs and drug combinations for any given HIV genetic variant. To this end, a large integrated data set has been created, uniting some of the largest existing resistance databases.

Development of antiretroviral drug resistance is a major cause of treatment failure in HIV-infected patients. Although recent advances in molecular diagnostics have made available standardised systems for monitoring the development of drug resistance mutations, inferring the drug susceptibility profile from genotype is still inaccurate for clinical purposes.


The EuResist project proposed a novel approach to predicting the in vivo efficacy of antiretroviral drug regimens against a given HIV, based on the use of viral genotype data integrated with treatment response data collected in clinical practice.

The EuResist Network

EuResist Network GEIE is a European Economic Interest Grouping composed by:

  1. Karolinska Institutet (Stockholm, Sweden)

  2. Max Planck Institute for Informatics (Saarbrücken, Germany)

  3. University of Siena (Italy)

  4. InformaPRO s.r.l. (Rome, Italy)

  5. Cologne University (Germany).


The EuResist Network is a nonprofit partnership with the following objectives:

  1. to promote joint research and scientific projects on behalf of its Members;  

  2. to coordinate the dissemination and exploitation of the results derived from the EuResist project and from the other activities of the Grouping.


EuResist Network is continously expanding with new partners in Europe and in the world. Partnership is based on mutual scientific collaboration and data sharing.


The Network is supported by different private and public sources in order to carry on its activities and to provide free services to the public.

EuResist project results

  • The largest HIV resistance database available. EuResist's first key achievement was the creation of the EuResist Integrated Data Base (EIDB), which is now among the largest databases of HIV genotypes and clinical response available. It integrates data from the project partners (ARCA from Italy, AREVIR from Germany and Karolinska from Sweden), and has recently expanded with data coming from Luxembourg, Belgium (Leuven) , Spain (Badalona) and Portugal (Lisbon). The data collected include demographics, viral loads, CD4 counts, treatment history and HIV genotypes for almost 50.000 patients.

  • EuResist Resistance prediction system: better than the state of the art. 


A standard datum for training the engines has been defined based on the notion of Treatment Change Episode (TCE). In its essential form, a TCE comprises the baseline viral load and genotype at the start of a new treatment, the treatment itself and a follow-up viral load obtained while still on that treatment. The time intervals in the EuResist TCE are compliant with the Forum for HIV Research definition focusing on short-term 8-week response. Data mining techniques have discovered several additional features and derived features that can have an impact on response to treatment and improve the accuracy of the prediction engine.

Three engines have been developed and found to perform similarly but not identically in validation tests. Techniques such as Support Vector Machines, Fuzzy logic, Case Based Reasoning, and Random Forests have been investigated, while finally all of the three engines have ended up with using Logistic Regression models for the classification of the therapies as successes or failures. However, the three engines use different approaches to derive such models from the EuResist database plus derived features providing extra information not directly contained in the standard datum.

Several sophisticated information fusion methods have been studied and compared to deliver one single combined prediction starting from the three individual engines. Finally a simple mean combiner has been chosen, which shows a better learning curve than the individual prediction systems or more complex information fusion methods. Based on validation studies done with historical cases, the final EuResist Prediction engine currently predicts the correct outcome in 76% of cases. Interestingly, a recent analysis has also indicated that the accuracy of the prediction of the 24-week outcome equals that of the 8-week outcome even without retraining the system with 24-week follow-up data.

The EuResist engine has been compared with the state-of-the-art tools available for assisting the selection of antiretroviral therapy in clinical practice, such as ANRS (France), Rega (Belgium) and HIVdb (Stanford, USA).

Different methods produce different types of predictions and this complicates the comparison. In order to allow for reasonably fair comparisons among the systems, careful statistical analysis has been performed. Comparison results show that EuResist prediction is more accurate than state-of-the-art systems.

Background. While combination antiretroviral therapy has made HIV infection a treatable condition, eradication of infection is not yet achievable and antiretroviral therapy needs to be administered as a prolonged, possibly lifelong treatment . Long-term toxicity, difficulty in adhering to complex regimens, possible pharmacokinetics problems, and intrinsically limited potency are all factors favoring the selection of drug-resistant viral strains . Development of drug resistance is nowadays a major cause for treatment failure.

The "old" solution. The basic method for defining the drug resistance profile of a given virus population implies culturing the virus in the presence of each single drug and measuring the extent of virus replication in a well controlled virus-cell system. Unfortunately, considerable efforts are required to develop and standardize a test of this kind (‘phenotypic' assay) to be performed on a routine basis. Indeed, reliable drug resistance phenotyping is currently provided only by specialized companies at very high cost . Moreover, evolution of resistance in vivo can be different from what observed in vitro and inferring clinical resistance from in vitro data is not straightforward, particularly for some drugs.

Recent advances in molecular diagnostics have made available a more practical and cost-effective approach to drug resistance testing, based on direct sequencing of the relevant regions of the virus genome (genotype). Mutations detected in the virus genotype are then used to infer the drug susceptibility profile based on known correlations between genotype and phenotype.

A number of algorithms (e.g. stanford, retrogram, trugene, anrs, Rega) have been developed in recent years based on sets of rules which assign each drug to one of different resistance categories depending on the presence of defined mutations or combinations of mutations. These methods, still immersed in a phenotype-based theoretical framework, provide predictions which can be inaccurate for clinical purposes, particularly considering that both phenotypic and genotypic assays provide an estimate of susceptibility to single drugs while three- or four-drug combinations are commonly used in vivo.

Fig. 1 - Comparison of the Stanford, REGA and ANSR systems on EuResist data

​The EuResist project objectives

  1. To integrate biomedical information from three large genotype-response correlation databases, thus collecting the required critical mass of data on the clinical implications of HIV drug resistance.

  2. To develop and validate a number of different engines for effective prediction of the response to treatment based on the integrated biomedical information.

  3. To combine the different engines into a predictive system and make it publicly available on the web, with a sponsor based exploitation schema.