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Poly (ADP-ribose) polymerases (PARPs) play diverse roles in various cellular processes that involve DNA repair and programmed cell death. Amongst these polymerases is PARP-1 which is the key DNA damage-sensing enzyme that acts as an initiator for the DNA repair mechanism. Dihydroorotate dehydrogenase (DHODH) is an enzyme in the pyrimidine biosynthetic pathway which is an important target for anti-hyperproliferative and anti-inflammatory drug design. Since these enzymes share a common role in the DNA replication and repair mechanisms, it may be beneficial to target both PARP-1 and DHODH in attempts to design new anti-cancer agents. Benzimidazole derivatives have shown a wide variety of pharmacological activities including PARP and DHODH inhibition. We hereby report the design, synthesis and bioactivities of a series of benzimidazole derivatives as inhibitors of both the PARP-1 and DHODH enzymes.

Background: The diagnosis of knee joint tuberculosis, especially in early stages of synovial disease, has more often been based on clinicoradiological suspicion, with no single test claiming to be a dependable rapid diagnostic test with high sensitivity and specificity. Nuclear amplification tests in vogue like the polymerase chain reaction have shown variable sensitivity and false positivity rates in various studies. We evaluated the role of Amplified Mycobacterium tuberculosis Direct Test (AMTDT) or Genprobe in the diagnosis of knee joint tuberculosis in early, especially, early synovitis and arthritis cases. Patients and Methods: Thirty two patients of suspected knee joint tuberculosis were subjected to diagnostic arthroscopy during the study period. The synovial fluid and tissue were subjected to mycobacterial culture, histopathology, and AMTDT. A comparative analysis of the sensitivity and specificity of this new test with culture and histopathology was done and the time taken for reporting was calculated for each test. Results: Out of 32 tissue samples, 8 were found to be positive with mycobacterial culture [Lowenstein Jensen (LJ)/Bactec], 11 were positive with histopathology, and 5 were found to positive with AMTDT. The sensitivity of AMTDT was found to be 62.5% and specificity was 100% with a P value of 0.083. The results were obtained earliest with AMTDT with a mean reporting time of 1.2 days, while the results of histopathology were obtained in a mean time of 6.8 days, BacT alert in 22.5 days, and conventional LJ medium culture results in 48.6 days. Conclusion: AMTDT or Genprobe is a rapid diagnostic test for early diagnosis of tubercular arthritis, but has low sensitivity in knee joint tuberculosis. Nuclear amplification tests are still far from being a single promising alternative to conventional tests in cases of joint tuberculosis. Routine use of arthroscopic biopsies in all suspected cases is helpful in the early diagnosis of knee joint tuberculosis.

HIV-1 protease is an obligatory enzyme in the replication process of the HIV virus. The abundance of structural information on HIV-1PR has made the enzyme an attractive target for computer-aided drug design strategies. The daunting ability of the virus to rapidly generate resistant mutants suggests that there is an ongoing need for new HIV-1PR inhibitors with better efficacy profiles and reduced toxicity. In the present investigation, molecular modeling studies were performed on a series of 54 cyclic urea analogs with symmetric P2/P2' substituents. The binding modes of these inhibitors were determined by docking. The docking results also provided a reliable conformational superimposition scheme for the 3D-QSAR studies. To gain insight into the steric, electrostatic, hydrophobic and hydrogen-bonding properties of these molecules and their influence on the inhibitory activity, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed. Two different alignment schemes viz. receptor-based and atom-fit alignment, were used in this study to build the QSAR models. The derived 3D-QSAR models were found to be robust with statistically significant r(2) and r(2)(pred) values and have led to the identification of regions important for steric, hydrophobic and electronic interactions. The predictive ability of the models was assessed on a set of molecules that were not included in the training set. Superimposition of the 3D-contour maps generated from these models onto the active site of enzyme provided additional insight into the structural requirements of these inhibitors. The CoMFA and CoMSIA models were used to design some new inhibitors with improved binding affinity. Pharmacokinetic and toxicity predictions were also carried out for these molecules to gauge their ADME and safety profile. The computational results may open up new avenues for synthesis of potent HIV-1 protease inhibitors.

Quantitative structure-activity relationships (QSAR) have been applied for decades in the development of relationships between physicochemical properties of chemical substances and their biological activities to obtain a reliable statistical model for prediction of the activities of new chemical entities. The fundamental principle underlying the formalism is that the difference in structural properties is responsible for the variations in biological activities of the compounds. In the classical QSAR studies, affinities of ligands to their binding sites, inhibition constants, rate constants, and other biological end points, with atomic, group or molecular properties such as lipophilicity, polarizability, electronic and steric properties (Hansch analysis) or with certain structural features (Free-Wilson analysis) have been correlated. However such an approach has only a limited utility for designing a new molecule due to the lack of consideration of the 3D structure of the molecules. 3D-QSAR has emerged as a natural extension to the classical Hansch and Free-Wilson approaches, which exploits the three-dimensional properties of the ligands to predict their biological activities using robust chemometric techniques such as PLS, G/PLS, ANN etc. It has served as a valuable predictive tool in the design of pharmaceuticals and agrochemicals. Although the trial and error factor involved in the development of a new drug cannot be ignored completely, QSAR certainly decreases the number of compounds to be synthesized by facilitating the selection of the most promising candidates. Several success stories of QSAR have attracted the medicinal chemists to investigate the relationships of structural properties with biological activity. This review seeks to provide a bird's eye view of the different 3D-QSAR approaches employed within the current drug discovery community to construct predictive structure-activity relationships and also discusses the limitations that are fundamental to these approaches, as well as those that might be overcome with the improved strategies. The components involved in building a useful 3D-QSAR model are discussed, including the validation techniques available for this purpose.

Since the recognition of HIV-1 integrase as a novel and rational target for HIV therapeutics, remarkable progress has been made in the development of integrase inhibitors. Computational techniques have played a critical role in accelerating research in this area. However, most previous computational studies were based solely on ligand information. In the present work, we describe the application of one of our recently developed receptor-based 3D-quantitative structure activity relationships (QSAR) methods, i.e. comparative residue interaction analysis (CoRIA), in exploring the events involved in ligand-integrase binding. In this methodology, the non-bonded interaction energies (van der Waals and Coulombic) of the inhibitors with individual active site residues of the integrase enzyme are calculated and, along with other thermodynamic descriptors, are correlated with biological activity using chemometric methods. Different combinations of descriptors were used to develop three types of QSAR models, all of which were found to be statistically significant by internal and external validation. This is the first report of such a dedicated receptor-based 3D-QSAR approach being applied to comprehend the integrase-inhibitor recognition process. In addition, the study was performed on 13-different series of inhibitors, thereby exploring the most structurally diverse data set ever used in understanding the inhibition of HIV-1 integrase. The major advantage of this technique is that it can quantitatively extract crucial residues and identify the nature of interactions between the ligand and receptor that modulate activity. The models suggest that Asp64, Thr66, Val77, Asp116, Glu152 and Lys159 are the key residues influencing the binding of ligands with the integrase enzyme, and the majority of these results are in line with earlier studies. The approach facilitates easy lead-to-hit conversion and design of novel inhibitors by optimisation of the interaction of ligands with these specific residues of the integrase enzyme.

A simple and directly applicable 3D-QSAR method, termed Comparative Occupancy Analysis (CoOAn), has been developed. The method is based on the comparison of local occupancies of fragments of an aligned set of molecules in a 3D-grid space. The formalism commendably extracts the crucial position-specific molecular features and correlates them quantitatively to their biological endpoints. The method has been effectively applied and efficaciously validated on three large and diverse datasets?thrombin, glycogen phosphorylase b (GPB), and thermolysin inhibitors. Several robust and statistically significant predictive 3D-QSAR models were developed while simultaneously considering the influence of grid spacing on the accuracy of the results. The models, generated by the G/PLS chemometric method, not only unswervingly identified the obligatory chemical features but advantageously detected those that are unfavourable or detrimental for the molecular activity. The CoOAn models can profitably be used to optimize existing molecules as well as to design new leads with more desirable (and/or less detrimental) features. The activity-modulating features (together with their distance-constraints) extracted by the methodology can also be incorporated into a pharmacophore-type query to search a chemical database for novel leads.

The blood-brain barrier (BBB) is the single most important factor hindering the development of neurotherapeutics. Experimental methods of BBB permeation determination are cumbersome and expensive; thus, in silico methods for BBB permeation prediction gained momentum in recent past. Most of the approaches seem to achieve >80% accuracy in prediction as well as gained insights into the molecular determinants of passive BBB permeation. However, none of the methods account for the role of active transport and efflux systems, predominantly because of lack of experimental data. Accuracy and predictability of in silico modeling can be increased further by incorporating the data emerging from the noninvasive methods of measuring the distribution of compounds within the brain.

Quantitative Structure-Activity Relationships (QSAR) are being used since decades for prediction of biological activity, lead optimization, classification, identification and explanation of the mechanisms of drug action, and prediction of novel structural leads in drug discovery. Though the technique has lived up to its expectations in many aspects, much work still needs to be done in relation to problems related to the rational design of peptides. Peptides are the drugs of choice in many situations, however, designing them rationally is a complicated task and the complexity increases with the length of their sequence. In order to deal with the problem of peptide optimization, one of our recently developed QSAR formalisms CoRIA (Comparative Residue Interaction Analysis) is being expanded and modified as: reverse-CoRIA (rCoRIA) and mixed-CoRIA (mCoRIA) approaches. In these methodologies, the peptide is fragmented into individual units and the interaction energies (van der Waals, Coulombic and hydrophobic) of each amino acid in the peptide with the receptor as a whole (rCoRIA) and with individual active site residues in the receptor (mCoRIA) are calculated, which along with other thermodynamic descriptors, are used as independent variables that are correlated to the biological activity by chemometric methods. As a test case, the three CoRIA methodologies have been validated on a dataset of diverse nonamer peptides that bind to the Class I major histocompatibility complex molecule HLA-A*0201, and for which some structure activity relationships have already been reported. The different models developed, and validated both internally as well as externally, were found to be robust with statistically significant values of r(2) (correlation coefficient) and r(2)(pred) (predictive r(2)). These models were able to identify all the structure activity relationships known for this class of peptides, as well uncover some new relationships. This means that these methodologies will perform well for other peptide datasets too. The major advantage of these approaches is that they explicitly utilize the 3D structures of small molecules or peptides as well as their macromolecular targets, to extract position-specific information about important interactions between the ligand and receptor, which can assist the medicinal and computational chemists in designing new molecules, and biologists in studying the influence of mutations in the target receptor on ligand binding.

A set of 25 coumarin-4-acetic acid benzylidene hydrazides were synthesized and characterized by NMR, IR and mass spectroscopic techniques. The compounds were evaluated for their anti-tubercular activity against Mycobacterium tuberculosis H37Rv strain using the BACTEC 460 system to determine percentage inhibition. To understand the relationship between structure and activity, a 3D-QSAR analysis has been carried out by Comparative Molecular Field Analysis (CoMFA). Several statistically significant CoMFA models were generated. The CoMFA model generated with database alignment was the best in terms of overall statistics. The CoMFA contours provide a good insight into the structure activity relationships of the compounds reported herein. 2008 Elsevier Masson SAS. All rights reserved.

This paper has two objectives: first to develop an in silico model for the prediction of blood brain barrier permeability of new chemical entities and second to find the role of active transport specific to the P-glycoprotein (P-gp) substrate probability in blood brain barrier permeability. An Artificial Neural Network (ANN) model has been developed to predict the ratios of the steady-state concentrations of drugs in the brain to those in the blood (logBB) from their molecular structural parameters. Seven descriptors including P-gp substrate probability have been used for model development. The developed model is able to capture a relationship between P-gp and logBB. The predictive ability of the ANN model has also been compared with earlier computational models.