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GLOBALART

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Improved prediction of HIV drug resistance in different viral subtypes by bioinf...

Improved prediction of HIV drug resistance in different viral subtypes by bioinformatic analysis of genetic and clinical data Since the advent of the HIV epidemic, more than 25 million people have died from AIDS. Antiretroviral therapy (ART) has dramatically decreased to mortality and morbidity in high income countries. However, the virus adapts rapidly... ver más

30/06/2013

45K€

Presupuesto del proyecto: 45K€

Líder del proyecto

KAROLINSKA INSTITUTET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2013-06-30 No tenemos la información de la convocatoria

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1 Participantes

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Líder del proyecto

KAROLINSKA INSTITUTET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 45K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Since the advent of the HIV epidemic, more than 25 million people have died from AIDS. Antiretroviral therapy (ART) has dramatically decreased to mortality and morbidity in high income countries. However, the virus adapts rapidly during suboptimal therapy and over 200 mutations are associated with resistance to one or more drugs. Drug resistance is thus a major threat to the long-term outcome of ART. The EuResist project is the worlds largest database and has created a prediction engine, based on a combination of HIV genetic data, treatment history and clinical response, from over 33,000 patients from six European countries. The amount of data and participating countries, are ontinuously increasing. Given the viral DNA sequence and patient clinical data, EuResist can recommend the therapy most likely to effectively suppress the viral load, and avoid drug combinations likely to fail due to resistance. Currently, the data is mainly retrieved from HIV subtype B, since this viral type is the most common in Europe. One of the main aims of the project is therefore to enhance the prediction for other subtypes, e.g. subtype C, which is the most common in Africa and world-wide. This will be achieved through addition of new data from patients infected with different viral subtypes, both from Sweden and from collaborating institutes in Ethiopia, Tanzania and South Africa. Furthermore, we will perform thorough bioinformatic analysis of mutational patterns in relation to the viral subtype, where the ultimate goal is to create a valid treatment prediction based on clinical data and treatment history, without the need for viral DNA sequence data. If this is achieved it will be a highly important contribution to the fight against AIDS in low and middle-income countries.

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