Cholesteryl ester transfer protein inhibitors in coronary heart disease: Validated comparative QSAR modeling of N, N-disubstituted trifluoro-3-amino-2-propanols

Design and development of novel therapeutics for coronary heart disease treatment based on cholesteryl ester transfer protein inhibition - in silico approach

Cholesteryl ester transfer protein (CETP) belongs to the group of enzymes which inhibition have the application in the treatment of cardiovascular diseases. This study presents QSAR modeling for a set of compounds acting as CETP inhibitors based on the Monte Carlo optimization with SMILES notation and molecular graph-based descriptors, and field-based 3D modeling. A 3D QSAR model was developed for one random split into the training and test sets, whereas conformation independent QSAR models were developed for three random splits, with the results suggesting there is an excellent correlation between them. Various statistical approaches were used to assess the statistical quality of the developed models, including robustness and predictability, and the obtained results were very good. This study used a novel statistical metric known as the index of ideality of correlation for the final assessment of the model, and the results that were obtained suggested that the model was good. Also, molecular fragments which account for the increases and/or decreases of a studied activity were defined and then used for the computer-aided design of new compounds as potential CETP inhibitors. The final assessment of the developed QSAR model and designed inhibitors was done using molecular docking, which revealed an excellent correlation with the results from QSAR modeling.Communicated by Ramaswamy H. Sarma.

Integrating regression and classification-based QSARs with molecular docking analyses to explore the structure-antiaromatase activity relationships of letrozole-based analogs

Aromatase is a multienzyme complex responsible for the biosynthesis of estrogen and its level has been found higher in breast cancer. Although the designing strategy of aromatase inhibitors (AIs) has continued for more than four decades, it may still be in demand to design highly effective and less toxic AIs. In this study, several chemometric approaches have been used to explore the important structural features of a series of letrozole-based analogs for their promising aromatase inhibitory activity. All techniques are statistically validated individually and in turn, validated with each other along with the structure–activity relationship (SAR) observations. The imidazole ring has been found to interact with the heme iron, whereas the triazole ring system has not shown any interaction. Moreover, imidazole function is better than 1,2,3-triazole, whereas 1,2,3-triazole is better than the 1,2,5-triazole ring system. Additionally, a bulky aryl substitution in the azole ring along with the orientation of the azole nitrogens and the cyanophenyl function has an essential role in the inhibition of aromatase. Furthermore, a cyano group substituted at the phenyl moiety interacts with Arg115, Met374, and Ser478 at the enzyme active site to form hydrogen bonding interactions. These observations are useful for designing potential AIs in the future.

Structural findings of cinnolines as anti-schizophrenic PDE10A inhibitors through comparative chemometric modeling

Schizophrenia is a complex psychiatric disorder associated with the distortion of striatopallidal neurotransmission of central nervous system. Phosphodiesterase10A (PDE10A) enzyme plays crucial role in cellular signaling pathways in schizophrenia. Inhibition of this enzyme may facilitate better treatment of this disease. 2D-QSAR, HQSAR, pharmacophore mapping, molecular docking, and 3D-QSAR analyses were performed on 81 cinnoline derivatives having PDE10A inhibitory activity. 2D-QSAR models were developed by multiple linear regression and partial least square analyses using both atom based and whole molecular descriptors. The best model, having considerable internal (q(2) = 0.812) and external (R(2)(pred) = 0.691) predictabilities, demonstrated importance of atom-based topological and whole molecular E-state as well as 3D topological indices. The best HQSAR model was also found to be statistically significant (q(2) = 0.664, R(2)(pred) = 0.513) and it highlighted some important structural features. PHASE-based pharmacophore hypothesis showed the importance of three hydrogen bond acceptor and one each of ring aromatic and hydrophobic features for higher activity. 3D-QSAR CoMFA and CoMSIA models were generated on two different types of alignment procedures-(1) pharmacophore (PHASE) based and (2) docking (GLIDE) based. GLIDE-based alignment produced better results for both CoMFA (Q(2) = 0.578; R(2)(pred) = 0.841) and CoMSIA (Q(2) = 0.610; R(2)(pred) = 0.824) methods. Molecular dynamics (MDs) simulations were performed for two ligand-receptor complexes and these simulations explored some crucial factors for higher activity. These findings of MD simulations were consistent with the interpretations obtained from other methods of analyses. The current study may help in designing new PDE10A inhibitors of this class.