Integration of viral genomics with clinical data to predict response to anti-HIV treatment.

Aharoni, E.; Altmann, A.; Borgulya, G.; D'Autilia, R.; Incardona, F.; Kaiser, R. and Kent, Carmel (2008). Integration of viral genomics with clinical data to predict response to anti-HIV treatment. In: 1st Africa Conference, 2007.

URL: https://cordis.europa.eu/project/id/027173

Abstract

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

In line with this approach, the present proposal aims at- integrating biomedical information from three large genotype-response correlation databases, thus collecting the required critical mass of data.- developing and validating an array of different engines for effective prediction of the response to treatment based on the integrated biomedical information; different methods will be studied to realize the prediction engines (Case-Based Reasoning, machine learning, and others).- combining the different engines into a predictive system and make it publicly available on the web, with a sponsor based exploitation schema.

The EuResist integrated data set, resulting from the merging of some of the largest existing resistance databases, will be the largest in the world. The vastness of genetic and clinical data that will be made available will lead to qualitatively new approaches in data analysis. The EuResist Combined Prediction System will be innovative, based on novel or state-of-the-art methods, some of which have not been applicable before due to insufficient amount of data.

Expected advantages of this system include not only more effective care for patients but also significant decrease in global therapy management costs (which are very high).The project can also be considered as a pilot for hepatitis (HCV and HBV) where development of drug resistance is foreseen too.

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