Untitled

Abstract

Quantitative structure-activity relationships (QSAR) and Comparative Molecular Field Analysis (CoMFA) have been applied in order to explain the structural requirements of HIV-1 reverse transcriptase (HIV-1 RT) inhibitory activity of TIBO derivatives on the MT-4 cells. The best QSAR model is satisfactory in both statistical significance and predictive ability. The derived structural descriptors indicate the importance of electronic contributions toward the HIV-1 RT inhibition of this class of compounds. However, it could not reveal any hydrophobic influence because of high collinearity between C2 and log P variables. In order to cope with steric interaction in the correlation, 3D-QSAR was performed using CoMFA. The obtained CoMFA model shows high predictive ability, rcv2 = 0.771, and clearly demonstrates its potential in the steric feature of the molecules through contour maps, explaining a majority (81.8%) of the variance in the data. Consequently, these results can be useful in identifying the structural requirements of TIBO derivatives and helpful for better understanding the HIV-1 RT inhibition. Eventually, they provide a beneficial basis to design new and more potent inhibitors of HIV-1 RT.

MIA–QSAR coupled to principal component analysis-adaptive neuro-fuzzy inference systems (PCA–ANFIS) for the modeling of the anti-HIV reverse transcriptase activities of TIBO derivatives

“…In this work, MIA descriptors of a series of TIBO derivatives were applied to derive QSAR models by using PLS and N-PLS as regression methods. The predictions for external set compounds using the MIA-QSAR/PLS and N-PLS models built were found to be at least comparable to those internal/external validation data of literature, in which q 2 or r pred 2 varied from 0.612 to 0.885 [4][5][6][7][8][9]. The statistics for the PLS/N-PLS-based models are depicted in Table 2.…”

“…Tetrahydroimidazo [4,5,1-jk] [1,4]benzodiazepine (TIBO) derivatives have been successfully used as HIV reverse transcriptase inhibitors [4], and are useful models for deriving novel potent compounds. The first step of rational drug design often consists in performing QSAR modeling based on congeneric structures of ligands; this procedure has been carried out by several researchers for a class of TIBO derivatives [4][5][6][7][8][9].…”

“…In addition to the widely used PLS regression, some mathematical approaches have been coupled to MIA-QSAR in order to achieve progressively more predictive models, such as N-way and non-linear methods [18][19][20][21][22]. In this work, principal component analysis-adaptive neuro-fuzzy inference systems (PCA-ANFIS) technique was applied together with MIA-QSAR to verify the enhancement in predictive ability of the bioactivities of TIBO derivatives when compared to previous works [4][5][6][7][8][9] and to MIA-QSAR models based on well established regression methods.…”

Molecular docking studies on tetrahydroimidazo-[4,5,1-jk][1,4]-benzodiazepinone (TIBO) derivatives as HIV-1 NNRT inhibitors

“…Hannoongbua et al [17,18] have used electronic and molecular properties of the inhibitors as structural descriptors obtained from quantum chemical calculations for studying TIBO derivatives. A detailed QSAR study of TIBO compounds along with other anti-HIV compounds is presented in a review by Hansch et al [19] 3D-QSAR models based on CoMFA and CoMSIA have been used to predict the steric and electrostatic properties of the binding structure of the TIBO complexes [20].…”

A group center overlap based approach for “3D QSAR” studies on TIBO derivatives

“…18 In fact, the TIBO derivatives have been the aims of numerous quantitative structure-activity relationships (QSAR) studies. [19][20][21] Comparison of the different RT-NNI complexes suggest modifications to the TIBO group of inhibitors, which might enhance their binding and hence, potentially, their therapeutic efficacy. [22][23][24] Tivirapine, one of the earliest NNRTI, to enter into the clinical trials, is a TIBO derivative.…”