Novel quinoline and naphthalene derivatives as potent antimycobacterial agents.

We have designed and synthesized both the quinoline and naphthalene based molecules influenced by the unique structural make-up of mefloquine and TMC207, respectively. These compounds were evaluated for their anti-mycobacterial activity against drug sensitive Mycobacterium tuberculosis H37Rv in vitro at single-dose concentration (6.25 microg/mL). The compounds 22, 23, 26 and 27 inhibited the growth of M. tuberculosis H37Rv 99%, 90%, 98% and 91% respectively. Minimum inhibitory concentration of compounds 22, 23, 26 and 27 was found to be 6.25 microg/mL. Our molecular modeling and docking studies of designed compounds showed hydrogen bonding with Glu-61, Tyr-64 and Asn-190 amino acid residues at the putative binding site of ATP synthase, these interactions were coherent as shown by Mefloquine and TMC207, where hydrogen bonding was found with Tyr-64 and Glu-61 respectively. SAR analysis indicates importance of hydroxyl group and nature of substituents on piperazinyl-phenyl ring was critical in dictating the biological activity of newly synthesized compounds.

Pharmacophore generation and atom-based 3D-QSAR of novel 2-(4-methylsulfonylphenyl)pyrimidines as COX-2 inhibitors.

Cyclooxygenase-2 (COX-2) inhibitors are widely used for the treatment of pain and inflammatory disorders such as rheumatoid arthritis and osteoarthritis. A series of novel 2-(4-methylsulfonylphenyl)pyrimidine derivatives has been reported as COX-2 inhibitors. In order to understand the structural requirement of these COX-2 inhibitors, a ligand-based pharmacophore and atom-based 3D-QSAR model have been developed. A five-point pharmacophore with four hydrogen bond acceptors (A) and one hydrogen bond donor (D) was obtained. The pharmacophore hypothesis yielded a 3D-QSAR model with good partial least-square (PLS) statistics results. The training set correlation is characterized by PLS factors (r (2) = 0.642, SD = 0.65, F = 82.7, P = 7.617 e - 12). The test set correlation is characterized by PLS factors (Q (2) (ext) = 0.841, RMSE = 0.24,Pearson-R = 0.91). A docking study revealed the binding orientations of these inhibitors at active site amino acid residues (Arg513, Val523, Phe518, Ser530, Tyr355, His90) of COX-2 enzyme. The results of ligand-based pharmacophore hypothesis and atom-based 3D-QSAR give detailed structural insights as well as highlights important binding features of novel 2-(4-methylsulfonylphenyl)pyrimidine derivatives as COX-2 inhibitors which can provide guidance for the rational design of novel potent COX-2 inhibitors.

3D-QSAR of histone deacetylase inhibitors: hydroxamate analogues.

The histone deacetylase enzyme has increasingly become an attractive target for developing novel anticancer drugs. Hydroxamates are a new class of anticancer agents reported to act by selective inhibition of the histone deacetylase (HDAC) enzyme. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were employed to study three-dimensional quantitative structure-activity relationships (3D-QSARs). QSAR models were derived from a training set of 40 molecules. An external test set consisting of 17 molecules was used to validate the CoMFA and CoMSIA models. All molecules were superimposed on the template structure by atom-based, multifit and the SYBYL QSAR rigid body field fit alignments. The statistical quality of the QSAR models was assessed using the parameters r(2)(conv), r(2)(cv) and r(2)(pred). In addition to steric and electronic fields, ClogP was also taken as descriptor to account for lipophilicity. The resulting models exhibited a good conventional r(2)(conv) and cross-validated r(2)(cv) values up to 0.910 and 0.502 for CoMFA and 0.987 and 0.534 for CoMSIA. Robust cross-validation by 2 groups was performed 25 times to eliminate chance correlation. The CoMFA models exhibited good external predictivity as compared to that of CoMSIA models. These 3D-QSAR models are very useful for design of novel HDAC inhibitors.

3D-QSAR of histone deacetylase inhibitors as anticancer agents by genetic function approximation.

Histone deacetylases (HDACs) play a critical role in gene transcription and are implicated in cancer therapy and other diseases. Inhibitors of HDACs induce cell differentiation and suppress cell proliferation in the tumor cells. Although many such inhibitors have been designed and synthesized, but selective inhibitors for HDAC isoforms are lacking. Various hydroxamic acid analogues have been reported as HDAC inhibitors. Here, we report a three-dimensional quantitative structure-activity relationship (3D-QSAR) study performed using genetic function approximation (GFA) for this class of molecules. QSAR models were generated using a training set of 39 molecules and the predictive ability of final model was assessed using a test set of 17 molecules. The internal consistency of the final QSAR model was 0.712 and showed good external predictivity of 0.585. The results of the present QSAR study indicated that molecular shape analysis (MSA). thermodynamic and structural descriptors are important for inhibition of HDACs.