Fellowship Site: University of Zimbabwe
U.S. Institution: Stanford University
Project Title: Genotypic drug resistance monitoring for optimal anti-retroviral therapy in Zimbabwe
HIV drug resistance monitoring plays a very key role in the selection and sequencing of antiretroviral drugs in public health treatment regimens. Optimizing the sequencing of antiretroviral therapy is a bottleneck in the switching of patients failing 2nd line therapy to 3rd line. Affordable can genotyping and bioinformatics provide data on drug resistance to optimize future treatment options, monitor the evolution of resistance in individuals and communities and anticipate the dynamics of the epidemic in marginalized and at risk populations.
An understanding of drug resistance mutation profiles associated with various regimens is critical to the sequencing of antiretroviral therapy. More data is still needed on the drug resistance patterns associated with protease inhibitor (PI) failure in light of numerous observations of patients that fail PI based therapies without protease (PR) drug resistance mutations, despite good adherence. There are suggestions of the potential involvement of mutations outside the protease gene in the reduction in PI activity such as mutations within the gag cleavage sites and env. However, the contributions of these mutations have not yet been validated.
Thus the overall objective of the proposed work is to leverage on the advances in sequencing and bioinformatics technologies in order to provide better real time monitoring of HIV drug resistance in Zimbabwe for improved patient treatment outcomes.
This work will leverage on collaborations that has been built between a 3rd line antiretroviral therapy referral treatment site in Harare, the Biomedical research & training institute, the African Institute of Biomedical science & Technology and the Stanford University Centre for AIDS Research. The aim of the collaboration is to aid in the improvement of the disease outcomes in patients being switched to 3rd line through genotype based optimization of ART.
Patients failing 2nd line therapy will be genotyped. Genotype, treatment and clinical data will be curated in an online collaborative database and analyzed to determine the mutation profiles associated with various second line regimens being used in Zimbabwe.
In parallel, highly multiplexed HIV whole genome analysis will be performed using next generation sequencing on archived samples. The data generated will be used to ascertain the contribution of mutations outside PR towards the reduction of PI susceptibility. In addition, it will also be used for advanced phylogenetic analysis to estimate population viral diversity to assess the effects of treatment and other interventions on the trajectory of the epidemic.
It is anticipated that through this work we will be able to build bioinformatics capacity in Zimbabwe for HIV drug resistance monitoring and surveillance and optimize therapies. In additions, through Phylogenetic analysis we will have sensitive real time data on the effects of treatment on the epidemic.