Structure-activity correlation study of HIV-1 inhibitors: electronic and molecular parameters

Design of novel leads: ligand based computational modeling studies on non-nucleoside reverse transcriptase inhibitors (NNRTIs) of HIV-1

Researchers are on the constant lookout for new antiviral agents for the treatment of AIDS. In the present work, ligand based modeling studies are performed on analogues of substituted phenyl-thio-thymines, which act as non-nucleoside reverse transcriptase inhibitors (NNRTIs) and novel leads are extracted. Using alignment-dependent descriptors, based on group center overlap (SALL, HDALL, HAALL and RALL), an alignment-independent descriptor (S log P), a topological descriptor (Balaban index (J)) and a 3D descriptor dipole moment (μ) and shape based descriptors (Kappa 2 index ((2)κ)), a correlation is derived with inhibitory activity. Linear and non-linear techniques have been used to achieve the goal. Support Vector Machine (SVM, R = 0.929, R(2) = 0.863) and Back Propagation Neural Network (BPNN, R = 0.928, R(2) = 0.861) methods yielded near similar results and outperformed Multiple Linear Regression (MLR, R = 0.915, R(2) = 0.837). The predictive ability of the models are cross-validated using a test dataset (SVM: R = 0.846, R(2) = 0.716, BPNN: R = 0.841, R(2) = 0.707 and MLR: R = 0.833, R(2) = 0.694). It is concluded that the hydrophobicity (S log P) and the polarity (μ) of a ligand and the presence of hydrogen donor (HDALL) moieties are the deciding factors in improving antiviral activity and pharmaco-therapeutic properties. Based on the above findings, a virtual dataset is created to extract probable leads with reasonable antiviral activity as well as better pharmacophoric properties.

A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis

A quantitative-structure activity relationship was obtained by applying Multiple Linear Regression Analysis to a series of 80 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine (HEPT) derivatives with significant anti-HIV activity. For the selection of the best among 37 different descriptors, the Elimination Selection Stepwise Regression Method (ES-SWR) was utilized. The resulting QSAR model (R (2) (CV) = 0.8160; S (PRESS) = 0.5680) proved to be very accurate both in training and predictive stages.

Evolutionary optimization, backpropagation, and data preparation issues in QSAR modeling of HIV inhibition by HEPT derivatives

Artificial neural networks (ANNs) can be utilized to generate predictive models of quantitative structure-activity relationships between a set of molecular descriptors and activity. Evolutionary computation provides a means to appropriately search for the set of weights and bias terms associated with artificial neural networks that minimize selected functions of the error between the actual and desired outputs. This method is demonstrated by evolutionary training of artificial neural networks capable of predicting anti-HIV activity for a set of 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT) derivatives. The results of this work further confirm the growing indication that evolutionary computation can outperform backpropagation as a method of artificial neural network training. The results also indicate the degree to which bias in the initial training and testing data can affect performance and the importance of bootstrapping.

Advances in QSAR studies of HIV-1 reverse transcriptase inhibitors

A review is presented of the recent advances in quantitative structure-activity relationship (QSAR) studies of HIV-1 reverse transcriptase (RT) inhibitors. These inhibitors have been put into two classes: nucleoside RT inhibitors (NRTIs), which are 2',3'-dideoxynucleoside analogues (ddNs), and non-nucleoside RT inhibitors (NNRTIs). For NRTIs (ddNs), which act as competitive inhibitors or alternate substrates of RT and hence interact at the substrate binding site of the enzyme, QSARs have pointed out the major role of the electronic factors governing their activity. For NNRTIs, which bind to a site entirely distinct from the substrate binding site, the activity has been shown to be largely dependent upon the hydrophobic nature of the compounds or substituents. The hydrophobic nature of the active site in the receptor with which the NNRTIs interact provides relatively few possibilities for the molecules to have polar interactions or hydrogen bondings, but QSARs have indicated that NNRTIs do involve some polar interactions and hydrogen bondings with some pockets of the enzyme. QSARs also indicate the significant roles of steric interactions and conformational shape of the molecule.

HEPT derivatives as non-nucleoside inhibitors of HIV-1 reverse transcriptase: QSAR studies agree with the crystal structures

The interest in the non-nucleoside inhibitors (NNIs) to the reverse transcriptase (RT) as anti-AIDS agents has grown in the last ten years. The compound 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT) is the precursor of the most studied class of NNIs, from which hundreds of derivatives have been synthesized and tested. There are at least twelve QSAR studies about the HEPT derivatives as RT inhibitors. Most of the predictions derived by these studies are related to the nature of the active site near the substituents at positions N-1 and C-5, and at the C-6 phenyl ring. The validity of these models has been checked against the 3-D structure of HIV 1 RT-HEPT complexes available. Most of these predictions were confirmed at the molecular level.

3D-quantitative structure-activity relationships of HEPT derivatives as HIV-1 reverse transcriptase inhibitors, based on Ab initio calculations

Comparative molecular field analysis (CoMFA) has been applied to a large set of 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT) analogues. The starting geometry of HEPT was obtained from crystallographic data of HEPT/HIV-1 reverse transcriptase (RT) complexes. The structures of 101 HEPT derivatives were considered and fully optimized by ab initio molecular orbital calculations at the HF/3-21G level. The best CoMFA model is satisfactory in both statistical significance and predictive ability. It shows excellent, high predictive ability as r2cv = 0.858. The derived model indicates the importance of steric contributions (64.4%) as well as electrostatic interactions for the HIV-1 RT inhibition. In addition, steric and electrostatic contour maps from this analysis agree well with the experimentally observed trend that there are steric interactions between the side chain of HEPT and an aromatic ring of Tyr181. It is concluded that a moderately sized group at C5 enhances contact with Tyr181 enough to push it into a position which renders the protein nonfunctional, but a smaller group has insufficient steric requirements to do this and a larger group renders the ligand too large for the cavity. The mutation-induced resistance of reverse transcriptase is explained by this analysis. The obtained results not only lead to a better understanding of structural requirements of this set of compounds for the inhibition but also enable the suggestions for new and more potent drugs.

Three-dimensional quantitative structure-activity relationships study on HIV-1 reverse transcriptase inhibitors in the class of dipyridodiazepinone derivatives, using comparative molecular field analysis

A three-dimensional quantitative structure-activity relationships (3D QSAR) method, Comparative Molecular Field Analysis (CoMFA), was applied to a set of dipyridodiazepinone (nevirapine) derivatives active against wild-type (WT) and mutant-type (Y181C) HIV-1 reverse transcriptase. The starting geometry of dipyridodiazepinone was taken from X-ray crystallographic data. All 75 derivatives, divided into a training set of 53 compounds and a test set of 22 molecules, were then constructed and full geometrical optimizations were performed, based on a semiempirical molecular orbital method (AM1). CoMFA was used to discriminate between structural requirements for WT and Y181C inhibitory activities. The resulting CoMFA models yield satisfactory predictive ability regarding WT and Y181C inhibitions, with r2 cv = 0.624 and 0.726, respectively. CoMFA contour maps reveal that steric and electrostatic interactions corresponding to the WT inhibition amount to 58.5% and 41.5%, respectively, while steric and electrostatic effects have approximately equal contributions for the explanation of inhibitory activities against Y181C. The contour maps high-light different characteristics for different types of wild-type and mutant-type HIV-1 RT. In addition, these contour maps agree with experimental data for the binding topology. Consequently, the results obtained provide information for a better understanding of the inhibitor-receptor interactions of dipyridodiazepinone analogs.

Use of artificial neural networks in a QSAR study of anti-HIV activity for a large group of HEPT derivatives

Anti-HIV activity for a set of 107 inhibitors of the HIV-1 reverse transcriptase, derivatives of 1-[2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT), was modeled with the aid of chemometric techniques. The activity of these compounds was estimated by means of multiple linear regression (MLR) and artificial neural network (ANN) techniques and compared with the previous works. The results obtained using the MLR method indicate that the anti-HIV activity of the HEPT derivatives depends on the reverse of standard shadow area on the YZ plane and the ratio of the partial charges of the most positive atom to the most negative atom of the molecule. The best computational neural network model was a fully-connected, feed-forward method with a 6-6-1 architecture. The mean-square error for the prediction set using this network was 0.372 compared with 0.780 obtained using the MLR technique. Comparison of the quality of the ANN of this work with different MLR models shows that ANN has a better predictive power.

Quantitative structure-activity relationships and comparative molecular field analysis of TIBO derivatised HIV-1 reverse transcriptase inhibitors

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.

Conformational study of the HIV-1 reverse transcriptase inhibitor 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT)

The conformations of the HIV-1 reverse transcriptase inhibitor 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT) are calculated by semiempirical and mainly by ab initio methods in order to estimate the potential for the rotation around the carbon sulfur single bond. The results are compared to X-ray structures of HEPT associated to the HIV-1 reverse transcriptase. The NMR spectra of the compound are calculated to obtain some information about its structure in solution. The structure of HEPT in the complex is analysed to study the intermolecular interactions between the inhibitor and the surrounding protein, which determine the geometry of the inhibition complex.

QSAR based on multiple linear regression and PLS methods for the anti-HIV activity of a large group of HEPT derivatives

Quantitative structure-activity relationships have been developed for a set of 107 inhibitors of the HIV-1 reverse transcriptase, derivatives of a recently reported HIV-1 specific lead: 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT). The activity of these compounds was investigated by means of multiple linear regression (MLR) and PLS regression techniques and topological indexes as well as several tabulated physicochemical substituent constants were used as predictor variables. The results obtained indicate that the anti-HIV activity of the HEPT derivatives is strongly dependent on hydrophobic factors as expressed by the Hansch constant (sigma pi (R1+R2)), and especially dependent on the geometric factors mainly accounted for by the 1 chi N (R2) and 4 chi pN molecular connectivity indexes and also for the molecular volume (Vx), the Taft steric constant (Es(2R1)), and the Verloop parameter for the smallest width value (B1(3R1)). Besides, for this data set, comparison of the quality of MLR and PLS models show that PLS is a better approach to MLR for improving the interpretability of the data and also to exhibit models with a better predictive quality.