Prediction of HIV drug resistance through in silico approach

Tammanna Bhajantri S. Pushkala   

Open Access   

Published:  Jun 11, 2024

DOI: 10.7324/JABB.2024.166453
Abstract

The development of drug resistance continues to be one of the most significant obstacles in the fight against human immunodeficiency virus type 1 (HIV-1) infection. Due to its exceptional replication kinetics, HIV is able to evade the selection pressure of the human immune system and the current combination drug therapy. Given that there are so many distinct mutations and mutational patterns that may confer drug resistance, it can be challenging to interpret the results of genotypic assays designed to detect them. The quantitative evaluation of resistance or susceptibility at the phenotypic level is made possible by cell culture studies. Nevertheless, the procedure is time-consuming and expensive. This study concentrates on the prediction of HIV drug resistance using an innovative “in silico” method that employs three potent resistance prediction tools: HIVdb, HIV-GRADE, and Geno2pheno[resistance]. These tools play a crucial role in the evaluation of HIV drug resistance, enabling clinicians and researchers to make informed decisions regarding antiretroviral (ARV) therapy. This study investigates the integration of these tools, emphasizing their individual strengths and collective utility in providing accurate and exhaustive HIV drug resistance predictions. Through a comprehensive analysis of genotypic data, this study seeks to improve our understanding of HIV drug resistance profiles, ultimately contributing to the optimization of ARV treatment strategies for HIV-positive individuals.
 


Keyword:     HIV-1 Drug resistance HIVdb HIV-GRADE Geno2pheno[resistance]


Citation:

Bhajantri T, Pushkala S. Prediction of HIV drug resistance through in silico approach. J App Biol Biotech. 2024. Online First. http://doi.org/10.7324/JABB.2024.166453

Copyright: Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike license.

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