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About Indian HIV Drug Resistance Database :

 Development of a National HIV Drug Resistance Database by Generating and Utilizing Clinical, Genotypic and Phenotypic Characterization Data of the Prevalent Drug Resistant Strains of Indian HIV-1 Clade C.  

Objectives :

1) To develop the network of genotyping laboratories and clinical sites that treat HIV infected persons with First line and second line treatment regimens .

2) To develop a national HIV drug resistance database with clinical and treatment history of patients failing on anti-retroviral treatment, genotyping for identifying drug resistance mutations and collection of phenotypic drug resistance data.

3) To develop phenotypic resistance and fitness assays for the Indian HIV-1 Clade C. 

Summary of the proposed research : 

    As very limited data on HIV drug resistance is available in India , generating HIVDR sequence database for Indian HIV-1 Clade C is of prime importance.

The proposed study will attempt to establish network of ART sites and centres where genotypic drug resistance assays are being done to develop a database on pol gene sequences and mutations, to collect the clinical data to link it with genotyping data and to carry out phenotypic resistance assays to assess the association of novel mutations with HIV drug resistance.

The technological tools will be developed for phenotypic drug resistance and replication fitness studies of a subset of Indian HIV-1 Clade C resistant viruses and develop a preliminary database for phenotypic drug resistance in India.

The national HIV drug resistance database in the form of web resource will be developed for submission of genotypic data related to HIV drug resistance in the country and would be utilized for designing Indian clade C-specific algorithms for interpretation of the genotypic data.

Important News


  • Gems of NARI-HIVDR lab
  • HIVDRdb project update
  • Devolopment of HIV-C replication fitness assay
  • Evaluation of a cost effective in-house HIVDR method
  • Revised Guidelines on Initiation of ART
  • Predicting HIV drug resistance from genomic data using deep learning

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