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

Novel HIV-1 Non-nucleoside Reverse Transcriptase Inhibitors: A Combinatorial Approach

Highly active antiretroviral therapy (HAART) is one of the most effective means for fighting against HIV-infection. HAART primarily targets HIV-1 reverse transcriptase (RT), and 14 of 28 compounds approved by the FDA as anti-HIV drugs act on this enzyme. HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs) hold a special place among HIV RT inhibitors owing to their high specificity and unique mode of action. Nonetheless, these drugs show a tendency to decrease their efficacy due to high HIV-1 variability and formation of resistant virus strains tolerant to clinically applied HIV NNRTIs. A combinatorial approach based on varying substituents within various fragments of the parent molecule that results in development of highly potent compounds is one of the approaches aimed at designing novel HIV NNRTIs. Generation of HIV NNRTIs based on pyrimidine derivatives explicitly exemplifies this approach, which is discussed in this review.

Computational drug design strategies applied to the modelling of human immunodeficiency virus-1 reverse transcriptase inhibitors

Reverse transcriptase (RT) is a multifunctional enzyme in the human immunodeficiency virus (HIV)-1 life cycle and represents a primary target for drug discovery efforts against HIV-1 infection. Two classes of RT inhibitors, the nucleoside RT inhibitors (NRTIs) and the nonnucleoside transcriptase inhibitors are prominently used in the highly active antiretroviral therapy in combination with other anti-HIV drugs. However, the rapid emergence of drug-resistant viral strains has limited the successful rate of the anti-HIV agents. Computational methods are a significant part of the drug design process and indispensable to study drug resistance. In this review, recent advances in computer-aided drug design for the rational design of new compounds against HIV-1 RT using methods such as molecular docking, molecular dynamics, free energy calculations, quantitative structure-activity relationships, pharmacophore modelling and absorption, distribution, metabolism, excretion and toxicity prediction are discussed. Successful applications of these methodologies are also highlighted.

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.

Hypoiodite-mediated cyclopropanation of alkenes

An efficient, transition metal-free procedure for the cyclopropanation of alkenes using malononitrile and the LiI-tBuOCl combination under mild reaction conditions is described. The reaction mechanism most likely involves tBuOI generated in situ from LiI and tBuOCl. The utility of this new methodology has been demonstrated by the synthesis of a potential HIV-1 RT inhibitor.

Microwave- and ultrasound-assisted synthesis of some acyclonucleobases based on a uracil moiety using dmap as base

In this study, 1-(bromoalkanoyl)-1H-pyrimidine-2,4-diones, (2,4-dioxo-pyrimidin-1-yl)-oxo-alkanoic acids, and bis(2,4-dioxo-3,4-dihydro-2H-pyrimidin-1-yl)-alkanones were successfully prepared via electrophilic substitution of uracil and its derivatives. High yields and pure products were obtained when microwave and ultrasound methodologies were used for undertaking the reactions. Importantly, the use of 4-dimethylaminopyridine in the present investigation gave rise to higher conversions of the starting material and afforded facile access to regioselective N-1 products.

Residue-ligand interaction energy (ReLIE) on a receptor-dependent 3D-QSAR analysis of S- and NH-DABOs as non-nucleoside reverse transcriptase inhibitors

A series of 74 dihydroalkoxybenzyloxopyrimidines (DABOs), a class of highly potent non-nucleoside reverse transcriptase inhibitors (NNRTIs), was retrieved from the literature and studied by receptor-dependent (RD) three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis to derive RD-3D-QSAR models. The descriptors in this new method are the steric and electrostatic interaction energies of the protein-ligand complexes (per residue) simulated by molecular dynamics, an approach named Residue-Ligand Interaction Energy (ReLIE). This study was performed using a training set of 59 compounds and the MKC-442/RT complex structure as reference. The ReLIE-3D-QSAR models were constructed and evaluated by genetic algorithm (GA) and partial least squares (PLS). In the best equations, at least one term is related to one of the amino acid residues of the p51 subunit: Asn136, Asn137, Glu138, and Thr139. This fact implies the importance of interchain interaction (p66-p51) in the equations that best describe the structure-activity relationship for this class of compounds. The best equation shows q² = 0.660, SE(cv) = 0.500, r² = 0.930, and SEE = 0.226. The external predictive ability of this best model was evaluated using a test set of 15 compounds. In order to design more potent DABO analogues as anti-HIV/AIDS agents, substituents capable of interactions with residues like Ile94, Lys101, Tyr181, and Tyr188 should be selected. Also, given the importance of the conserved Asn136, this residue could become an attractive target for the design of novel NNRTIs with improved potency and increased ability to avoid the development of drug-resistant viruses.

Application of ant colony optimization in development of models for prediction of anti-HIV-1 activity of HEPT derivatives

Quantitative structure-activity relationship models were derived for 107 analogs of 1-[(2-hydroxyethoxy) methyl]-6-(phenylthio)thymine, a potent inhibitor of the HIV-1 reverse transcriptase. The activities of these compounds were investigated by means of multiple linear regression (MLR) technique. An ant colony optimization algorithm, called Memorized_ACS, was applied for selecting relevant descriptors and detecting outliers. This algorithm uses an external memory based upon knowledge incorporation from previous iterations. At first, the memory is empty, and then it is filled by running several ACS algorithms. In this respect, after each ACS run, the elite ant is stored in the memory and the process is continued to fill the memory. Here, pheromone updating is performed by all elite ants collected in the memory; this results in improvements in both exploration and exploitation behaviors of the ACS algorithm. The memory is then made empty and is filled again by performing several ACS algorithms using updated pheromone trails. This process is repeated for several iterations. At the end, the memory contains several top solutions for the problem. Number of appearance of each descriptor in the external memory is a good criterion for its importance. Finally, prediction is performed by the elitist ant, and interpretation is carried out by considering the importance of each descriptor. The best MLR model has a training error of 0.47 log (1/EC(50)) units (R(2) = 0.90) and a prediction error of 0.76 log (1/EC(50)) units (R(2) = 0.88).

A ligand's-eye view of protein binding

Docking tools created for structure-based design and virtual screening have also been used to automate ligand alignment for comparative molecular field analysis (CoMFA). Models based on such alignments have been compared with those obtained based solely on shared ligand substructures, but such comparisons have generally failed to distinguish between conformational specification (alignment in the internal coordinate space) and embedding in a shared external frame of reference (Cartesian alignment). Here, large sets of inhibitors were docked into two cyclooxygenase and two reverse transcriptase crystal structures, and the poses generated were evaluated in terms of the CoMFA models they produced. Realigning the conformers obtained by docking by rigid-body rotation and translation to overlay their common substructures improved model statistics and interpretability, provided the protein structure used for docking was reasonably appropriate to the ligands being considered.

3D QSAR studies on binding affinities of coumarin natural products for glycosomal GAPDH of Trypanosoma cruzi

Drug design strategies based on Comparative Molecular Field Analysis (CoMFA) have been used to predict the activity of new compounds. The major advantage of this approach is that it permits the analysis of a large number of quantitative descriptors and uses chemometric methods such as partial least squares (PLS) to correlate changes in bioactivity with changes in chemical structure. Because it is often difficult to rationalize all variables affecting the binding affinity of compounds using CoMFA solely, the program GRID was used to describe ligands in terms of their molecular interaction fields, MIFs. The program VolSurf that is able to compress the relevant information present in 3D maps into a few descriptors can treat these GRID fields. The binding affinities of a new set of compounds consisting of 13 coumarins, for one of which the three-dimensional ligand-enzyme bound structure is known, were studied. A final model based on the mentioned programs was independently validated by synthesizing and testing new coumarin derivatives. By relying on our knowledge of the real physical data (i.e., combining crystallographic and binding affinity results), it is also shown that ligand-based design agrees with structure-based design. The compound with the highest binding affinity was the coumarin chalepin, isolated from Rutaceae species, with an IC50 value of 55.5 microM towards the enzyme glyceraldehyde-3-phosphate dehydrogenase (gGAPDH) from glycosomes of the parasite Trypanosoma cruzi, the causative agent of Chagas' disease. The proposed models from GRID MIFs have revealed the importance of lipophilic interactions in modulating the inhibition, but without excluding the dependence on stereo-electronic properties as found from CoMFA fields.

Development of neural network QSPR models for Hansch substituent constants. 2. Applications in QSAR studies of HIV-1 reverse transcriptase and dihydrofolate reductase inhibitors

In this paper, the applications of a Hansch substituent constant predictor(1) to Quantitative Structure-Activity Relationships (QSAR) studies of E. coli dihydrofolate reductase (DHFR) inhibitors 2,4-diamino-5-(substituted-benzyl)pyrimidines as well as HIV-1 reverse transcriptase (RT) inhibitors 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT) derivatives are demonstrated. Both data sets contain functional groups for which the substituent constants (pi, MR, F and R) could not be found in standard substituent constant tables. The substituent constant predictor allowed us to derive predicted pi, MR, F and R values for all substituents in both data sets, thus enabling the generation of easily interpretable QSAR models of comparable or better predictivity than previous models.

Neural networks: Accurate nonlinear QSAR model for HEPT derivatives

A nonlinear quantitative structure-anti-HIV-1-activity relationship (QSAR) study was investigated in a series of 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine] (HEPT) derivatives acting as nonnucleoside reverse transcriptase inhibitors (NNRTIs). This QSAR study has been undertaken by a three-layered neural network (NN) using molecular descriptors known to be responsible for the anti-HIV-1 activity. The usefulness of the model and the nonlinearity of the relationship between molecular descriptors and anti-HIV-1 activity have been clearly demonstrated. The obtained model outperforms those given in the literature in both the fitting and predictive stages. NN analysis yielded predicted activities in excellent agreement with the experimentally obtained values (R(2) = 0.977, predictive r(2) = 0.862). The effect of each molecular feature on the anti-HIV-1 activity variation has been clearly elucidated.