Repurposing FDA-approved drugs as HIV-1 integrase inhibitors: an in silico investigation

The Human Immunodeficiency Virus (HIV) infection is a global pandemic that has claimed 33 million lives to-date. One of the most efficacious treatments for naive or pretreated HIV patients is the HIV integrase strand transfer inhibitors (INSTIs). However, given that HIV treatment is life-long, the emergence of HIV strains resistant to INSTIs is an imminent challenge. In this work, we showed two best regression QSAR models that were constructed using a boosted Random Forest algorithm (r(2) = 0.998, q(10CV)(2) = 0.721, q(external_test)(2) = 0.754) and a boosted K* algorithm (r(2) = 0.987, q(10CV)(2) = 0.721, q(external_test)(2) = 0.758) to predict the pIC(50) values of INSTIs. Subsequently, the regression QSAR models were deployed against the Drugbank database for drug repositioning. The top-ranked compounds were further evaluated for their target engagement activity using molecular docking studies and accelerated Molecular Dynamics simulation. Lastly, their potential as INSTIs were also evaluated from our literature search. Our study offers the first example of a large-scale regression QSAR modelling effort for discovering highly active INSTIs to combat HIV infection. Communicated by Ramaswamy H. Sarma

Automated summary

This study demonstrates the use of regression QSAR modeling to predict the activity of INSTIs and applies the models to drug repositioning. The top-ranked compounds are further evaluated for their target engagement activity using molecular docking and accelerated Molecular Dynamics simulation, and their potential as INSTIs is assessed through literature search.