Regression QSAR Models for Predicting HIV-1 Integrase Inhibitors

Abstract

The Human Immunodeficiency Virus (HIV) infection is a global pandemic that has claimed 33 million lives to date. One of the most efficacious treatment for naive or pre-treated HIV patients is with the HIV integrase strand transfer inhibitors (INSTIs). However, given that HIV treatment is life-long, the emergence of HIV-1 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 [r2 = 0.998, q2(10CV) = 0.721, q2(external_test) = 0.754] and a boosted K* algorithm [r2 = 0.987, q2(10CV) = 0.721, q2(external_test) = 0.758] to predict the pIC50 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 their potential as INSTIs 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.

A computational overview of integrase strand transfer inhibitors (INSTIs) against emerging and evolving drug-resistant HIV-1 integrase mutants

“…These strict requirements are addressed by employing several computational techniques, like molecular docking (Tewtrakul et al 2015 ; Ercan et al 2019 ; Arslan 2019 ; Dogan and Durdagi 2021 ; Esmaeili et al 2021 ), pharmacophore modelling (Martin 2014 ; Bhatt et al 2014 ; Xue et al 2014 ; Islam and Pillay 2016 ), molecular dynamics (MD) simulation (Chen et al 2014 ; Islam and Pillay 2016 ; Chitongo et al 2019 ; Samorlu et al 2019 ), quantitative structure–activity relationships (QSAR) (Gupta et al 2013 ; Reddy et al 2013 ; Zhou et al 2021 ; Xuan et al 2021 ), and many more. The human cells lack structural or functional integrase homologues, making IN a desirable drug target for HAART (Siwe-Noundou et al 2019 ); further IN depicts greater tolerability, high efficacy, and fewer drug–drug interactions as compared to other HAART classes (Brooks et al 2019 ).…”

“…Although the mentioned INSTIs share various properties, including mechanism of action, antiviral potency, good tolerability, and safety profile, they have other significant traits that differ from each other, like pharmacokinetics, interactions, resistance profile, and pharmacodynamics (Blanco et al 2015 ; Roulet et al 2018 ), refer to Table 1 . However, the advent of INSTI-resistant strains is an impending task (Xuan et al 2021 ). Although numerous structurally diverse INSTIs have been reported, like coumarin derivatives, quinolone acids, diketo acids, salicylhydrazide derivatives, aryl cyclic compounds, sulphur nitrogen thiazepines, diazonaphthalene derivatives, pyrimidine ketones, benzenesulfonamides, and so on, the need to develop novel molecules still persists (Bhatt et al 2014 ; Zhou et al 2021 ).…”