In silico drug repurposing approach to predict most effective HAART for HIV drug resistance variants prevalent in the Indian HIV-positive population.

HIV epidemics still exist as a major global public health burden, especially in middle- and low-income countries. Given the lack of approved vaccines, antiretroviral therapy (ART) remains the primary approach to reduce the mortality and morbidity linked to this disease. Effective treatment for HIV-1 requires the simultaneous administration of multiple drugs. However, the virus can show resistance to antiretroviral drugs, resulting in treatment failure. Therefore, this study focused on assessing the prevalence of mutations within the Indian HIV-positive population. After assessing the data, we intended to identify the most effective highly active ART (HAART) regimens for individuals with drug-resistant variants. Furthermore, our analysis revealed a spectrum of HIV mutations, with varying effects on protein stability. The significance of this analysis lies in its potential to optimize HAART selection for HIV-positive individuals by accounting for both prevalence and stability-altering mutations. By considering mutation effects on protein stability, we can modify treatment regimens, increasing the likelihood of therapy success and diminishing the risk of resistance. Moreover, this study contributes to the broader field of drug repurposing, offering insights into the rational design of antiretroviral therapies.