3D-QSAR models on clinically relevant K103N mutant HIV-1 reverse transcriptase obtained from two strategic considerations

Chemical structure and correlation analysis of HIV-1 NNRT and NRT inhibitors and database-curated, published inhibition constants with chemical structure in diverse datasets

Human immunodeficiency virus (HIV-1) reverse transcriptase is a major target for designing anti-HIV drugs. Developed inhibitors are divided into non-nucleoside analog reverse-transcriptase inhibitors (NNRTIs) and nucleoside analog reverse-transcriptase inhibitors (NRTIs) depending on their mechanism. Given that many inhibitors have been studied and for many of them binding affinity constants have been calculated, it is beneficial to analyze the chemical landscape of these families of inhibitors and correlate these inhibition constants with molecular structure descriptors. For this, the HIV-1 RT data was retrieved from the ChEMBL database, carefully curated, and original literature verified, grouped into NRTIs and NNRTIs, analyzed using a hierarchical scaffold classification method and modelled with best multi-linear regression approach. Analysis of the HIV-1 NNRTIs subset results in ten different common structural parent types of oxazepanone, piperazinone, pyrazine, oxazinanone, diazinanone, pyridine, pyrrole, diazepanone, thiazole, and triazine. The same analysis for HIV-1 NRTIs groups structures into four different parent types of uracil, pyrimide, pyrimidione, and imidazole. Each scaffold tree corresponding to the parent types has been carefully analyzed and examined, and changes in chemical structure favorable to potency and stability are highlighted. For both subsets, descriptive and predictive QSAR models are derived, discussed and externally validated, revealing general trends in relationships between molecular structure and binding affinity constants in structurally diverse datasets. Data and QSAR models are available at the QsarDB repository (http://dx.doi.org/10.15152/QDB.202).

Synthesis, structure-activity relationships, and docking studies of N-phenylarylformamide derivatives (PAFAs) as non-nucleoside HIV reverse transcriptase inhibitors

A series of N-phenylarylformamide derivatives (PAFAs) with anti-wild-type HIV-1 activity (EC(50) values) ranging from 0.3 nM to 5.1 nM and therapeutic index (TI) ranging from 10 616 to 271 000 were identified as novel non-nucleoside reverse transcriptase inhibitors. Among them, compound 13g (EC(50) = 0.30 nM, TI = 184 578), 13l (EC(50) = 0.37 nM, TI = 212 819), 13m (EC(50) = 0.32 nM, TI = 260 617) and 13r (EC(50) = 0.27 nM, TI = 271 000) displayed the highest activity against this type virus nearly as potent as lead compound GW678248. Moreover, all of them were also active to inhibit the double mutant strain A(17) (K103N + Y181C) with EC(50) values of 0.29 μM, 0.14 μM, 0.10 μM and 0.27 μM, respectively. In particular, compound 13m, which showed broad-spectrum anti-HIV activity, was also effective to inhibit the HIV-2 ROD replication within 4.37 μM concentration.

3D-QSAR and docking studies on the HEPT derivatives of HIV-1 reverse transcriptase

Three-dimensional Quantitative Structure Activity Relationship (3D-QSAR) has been derived for a set of HEPT derivatives of HIV-1 reverse transcriptase (RT) using Comparative Molecular Field Analysis (CoMFA). The CoMFA models have been developed using two different alignment procedures such as common substructure and bioactive conformation. The CoMFA model I is derived from a common substructure procedure that includes steric and electrostatic fields with the cross-validated q(2) and the non-cross-validated r(2) value of 0.86 and 0.97, respectively. The same for the CoMFA model II that is derived based on the bioactive conformation are 0.19 and 0.77, respectively. It is evident from the results that the common substructure-based alignment model has good statistical significance when compared with that of bioactive conformation for the selected systems in this study. The docking study revealed that the conformational flexibility observed at the R3 position favors different orientations of the substitution at the active site of HIV-1 RT and thereby leads to inconsistency in the CoMFA alignment based on bioactive conformation.

Study on the interaction between HIV reverse transcriptase and its non-nucleoside inhibitor nevirapine by capillary electrophoresis

The HIV reverse transcriptase (RT) is an important antiviral target for the chemotherapy of AIDS because of its key role in virus replication. Nevirapine is a first generation of non-nucleoside reverse transcriptase inhibitors (NNRTIs), which is usually used for the therapy of AIDS. In this study, a high-performance analytical method based on capillary electrophoresis (CE) to investigate interactions between HIV RT and nevirapine was developed. Samples containing HIV RT and nevirapine at various ratios were incubated at 37 degrees C for 45 min and then separated by CE with Tris-acetate buffer at pH 7.3 containing 0.15% SDS. Both qualitative and quantitative characterizations of the binding were determined by CE for the first time. The binding constants of the interactions between HIV RT and nevirapine were calculated as (3.25+/-0.16)x10(4) and (1.25+/-0.07)x10(2) M(-1) by Scatchard analysis. HIV RT and nevirapine have two binding sites. The presented methodology should be generally applicable to study the interactions between HIV RT and nevirapine quantitatively and qualitatively.

Therapeutic strategies underpinning the development of novel techniques for the treatment of HIV infection

The HIV replication cycle offers multiple targets for chemotherapeutic intervention, including the viral exterior envelope glycoprotein, gp120; viral co-receptors CXCR4 and CCR5; transmembrane glycoprotein, gp41; integrase; reverse transcriptase; protease and so on. Most currently used anti-HIV drugs are reverse transcriptase inhibitors or protease inhibitors. The expanding application of simulation to drug design combined with experimental techniques have developed a large amount of novel inhibitors that interact specifically with targets besides transcriptase and protease. This review presents details of the anti-HIV inhibitors discovered with computer-aided approaches and provides an overview of the recent five-year achievements in the treatment of HIV infection and the application of computational methods to current drug design.