3D-QSAR and docking studies on ursolic acid derivatives for anticancer activity based on bladder cell line T24 targeting NF-kB pathway inhibition

Abstract

Bladder cancer is the common reason for mortality worldwide, and its increasing rate announces as a significant area of research in drug designing. The side effects and toxicity of existing drugs and the consequence of gradual cancer cell resistance against the available therapy make the treatment poor. Globally, there is a continuous high demand to develop new, more potent, and easily affordable drugs against cancer. The current research article illustrates the application of developed three-dimensional quantitative structure-activity relationship (3D-QSAR) based on human bladder cancer cell line T24 in vitro anticancer activity. The derived QSAR model has been used for prediction of natural compounds and analogs with 80% similarity of the most active compound of the dataset. The developed model describes the structure-activity relationship for terpenes and their derivatives at the molecular level. The developed comparative molecular field analysis (CoMFA) model shows a satisfactory cross-validation correlation coefficient (q²) of 0.54 and a regression correlation coefficient (r²) of 0.86. In order to evaluate the compliance with electronic pharmacokinetic parameters, Lipinski’s rule of five filter, absorption, distribution, metabolism, and excretion (ADME) and toxicity of predicted compounds have been calculated. Furthermore, molecular-docking study has been performed to prioritize these predicted compounds based on their docking score and binding pocket similarity through the identified potential anticancer targets. Finally, two compounds T9 and B42 have been identified as the best hit because these two fall within the standard limits of all filters and show a good binding affinity. Conclusively, all satisfactory results strongly suggest that the derived 3D-QSAR model and obtained candidate’s binding structures are reasonable in the prediction of a new antagonist’s activity. The strategy adopted in the present research is expected to be of immense importance and a great support in the identification and optimization of lead in the early and advance drug discovery.     

Synthesis of novel hetero ring fused pyridine derivatives; Their anticancer activity,CoMFA and CoMSIA studies

A series of novel furo[2,3-b]pyridine-2-carboxamide 4a–h/pyrido[3′,2′:4,5]furo[3,2-d] pyrimidin-4(3H)-one derivatives 5a–p were prepared from pyridin 2(1H) one 1 via selective O-alkylation with α-bromoethylester followed by cyclization, then reaction with different aliphatic primary amines to obtain 4 and further reaction with triethyl orthoacetate/triethyl orthoformate. Also prepared novel furo[2,3-b]pyridine-2-carbohydrazide Schiff’s bases 7a–h and pyrido [3′,2′:4,5]furo[3,2-d]pyrimidin-4(3H)-one derivatives 8a–h starting from furo[2,3-b]pyridine carboxylate derivatives 3 by reaction with hydrazine hydrate to form 6 and reaction with diverse substituted aldehydes and cyclization. Products 4a–h, 5a–p, 7a–h and 8a–h were screened against four human cancer cell lines (HeLa, COLO205, Hep G2 and MCF 7) and one normal cell line (HEK 293). Compounds 4e, 4f, 4g, 5h, 7c, 7d, 7e and 7f showed significant anticancer activity against all the cell lines at micro molar concentration and found to be non-toxic to normal cell line. Studies for HeLa, COLO205 and MCF-7 using CoMFA and CoMSIA. Models from 3D-QSAR provided a strong basis for future rational design of more active and selective HeLa, COLO205 and MCF-7 cell line inhibitors.     

Hologram and 3D-quantitative structure toxicity relationship studies of azo dyes

Amino azobenzenes are important dyes in the food and textile industry but their application is limited due to their mutagenicity. Computational modeling techniques were used to help understand the factors responsible for mutagenicity, and several quantitative structure toxicity relationship (QSTR) models have been derived. HQSTR (hologram QSTR) analyses indicated that different substituents at sites on both rings contribute to mutagenicity. Fragment parameters such as bond (B) and connectivity(C), as well as donor-acceptor (DA)-based model provide significant results (q² = 0.59, r² = 0.92, ) explaining these harmful effect. HQSTR results indicated that a bulky group at ring “Y” and small group at ring “X” might help to decrease mutagenicity. 3D-QSTR based on comparative molecular field analyses (CoMFA) and comparative molecular similarity index analyses (CoMSIA) are also in agreement with HQSTR. The 3D QSTR studies reveal that steric and electrostatic field effects have a strong relationship with mutagenicity (for CoMFA: q² = 0.51, r² = 0.95, and for CoMSIA: q² = 0.51, r² = 0.93 and ). In summary, negative groups and steric bulk at ring “Y” and small groups at carbon-3 of ring “X” might be helpful in reducing the mutagenicity of azo dyes.     

Structure-based 3D-QSAR studies on thiazoles as 5-HT<Subscript>3</Subscript> receptor antagonists

Structure-based 3D-QSAR studies were performed on 20 thiazoles against their binding affinities to the 5-HT₃ receptor with comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The thiazoles were initially docked into the binding pocket of a human 5-HT_3A receptor homology model, constructed on the basis of the crystal structure of the snail acetylcholine binding protein (AChBP), using the GOLD program. The docked conformations were then extracted and used to build the 3D-QSAR models, with cross-validated values 0.785 and 0.744 for CoMFA and CoMSIA, respectively. An additional five molecules were used to validate the models further, giving satisfactory predictive values of 0.582 and 0.804 for CoMFA and CoMSIA, respectively. The results would be helpful for the discovery of new potent and selective 5-HT₃ receptor antagonists.      

All-Orientation Search and All-Placement Search in Comparative Molecular Field Analysis

Three sets of molecules have been used to study the conventional CoMFA procedure. For all the three test sets, the resulting q² values were observed to vary simply because of the change in the orientation or placement of the aligned molecules. The reason is believed to root in the imperfect sampling of the molecular field. We have introduced two new strategies, all-orientation search (AOS) and all-placement search (APS), to optimize the sampling process. By rotating and translating the molecular aggregate within the grid systematically, all the possible samplings of the molecular field are tested and subsequently the one with the highest q² value can be picked out. We have also demonstrated that the combined application of AOS/APS with GOLPE procedure can yield results better than the ones by using them respectively.Electronic Supplementary Material available.     

CoMFA based de novo design of Pyrrolidine Carboxamides as Inhibitors of Enoyl Acyl Carrier Protein Reductase from Mycobacterium tuberculosis

InhA, the enoyl acyl carrier protein reductase (EACP reductase) from Mycobacterium tuberculosis, is one of the key enzymes involved in the mycobacterial fatty acid elongation cycle and has been validated as an effective target for the development of anti-microbial agents. We report here, comparative molecular field analysis (CoMFA) studies and subsequent de novo ligand design using the LeapFrog program on pyrrolidine carboxamides, which have been reported as selective inhibitors of EACP reductase from Mycobacterium tuberculosis. The CoMFA model, constructed from the inhibitors used in this study has been successfully used to rationalize the structure-activity relationship of pyrrolidine carboxamides. The CoMFA model produced statistically significant results with cross-validated and conventional correlation coefficients of 0.626 and 0.953 respectively. Further, the predictive ability of CoMFA model was determined using a test set which gave predictive correlation coefficient r ² _pred of 0.880, indicating good predictive power. Finally, Leapfrog was used to propose 13 new pyrrolidine carboxamide analogues, based on the information derived from the CoMFA contour maps. The designed molecules showed better predicted activity using the CoMFA model with respect to the already reported systems; hence suggesting that newly proposed molecules in this series of compounds may be more potent and selective toward EACP reductase inhibition.     

Molecular modeling study for the design of novel acetyl-CoA carboxylase inhibitors using 3D QSAR,molecular docking and dynamic simulations

Acetyl-CoA carboxylase (ACC) enzyme plays an important role in the regulation of biosynthesis and oxidation of fatty acids. ACC is a recognized drug target for the treatment of obesity and diabetes. Combination of ligand and structure-based in silico methods along with activity and toxicity prediction provides best lead compounds in the drug discovery process. In this study, a data-set of 100 ACC inhibitors were used for the development of comparative molecular field analysis (CoMFA) and comparative molecular similarity index matrix analysis (CoMSIA) models. The generated contour maps were used for the design of novel ACC inhibitors. CoMFA and CoMSIA models were used for the predication of activity of designed compounds. In silico toxicity risk prediction study was carried out for the designed compounds. Molecular docking and dynamic simulations studies were performed to know the binding mode of designed compounds with the ACC enzyme. The designed compounds showed interactions with key amino acid residues important for catalysis, and good correlation was observed between binding free energy and inhibition of ACC.     

Structure–activity relationship analysis of carbobicyclo and oxabicyclo succinimide analogs as potential androgen receptor antagonists

Prostate cancer (PCa) is a frequently diagnosed male cancer and the second leading cause of cancer-related death in many countries. Due to various amino acid mutations that occurred in the ligand binding domain of androgen receptor (AR), the patients were observed insensitive, even resistant to the marketed antiandrogens such as bicalutamide and enzalutamide, which emphasizes the urgent need for novel antiandrogens to solve drug resistance problem. Recently a series of carbobicyclo and oxabicyclo succinimide analogs were reported to effectively antagonize AR. In this study, to explore the structural requirements for these AR antagonists, we performed quantitative structure–activity relationship analysis on carbobicyclo and oxabicyclo succinimide analogs by using two-dimensional multiple linear regressions (MLR) method and three-dimension comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. The obtained models show satisfactory results with proper reliabilities and powerful external predictability. Moreover, the CoMFA and CoMSIA contour maps can intuitively represent key features associated with bioactivities. These models may offer guidance for the rational design and modification of new lead compounds for antiandrogens.     

Virtual identification of novel PPARα/γ dual agonists by 3D-QSAR,molecule docking and molecular dynamics studies

Abstract

Peroxisome proliferator-activated receptors (PPARs) are considered important targets for the treatment of Type 2 diabetes (T2DM). To accelerate the discovery of PPAR α/γ dual agonists, the comparative molecular field analysis (CoMFA) were performed for PPARα and PPARγ, respectively. Based on the molecular alignment, highly predictive CoMFA model for PPARα was obtained with a cross-validated q² value of 0.741 and a conventional r² of 0.975 in the non-cross-validated partial least-squares (PLS) analysis, while the CoMFA model for PPARγ with a better predictive ability was shown with q² and r² values of 0.557 and 0.996, respectively. Contour maps derived from the 3D-QSAR models provided information on main factors towards the activity. Then, we carried out structural optimization and designed several new compounds to improve the predicted biological activity. To investigate the binding modes of the predicted compounds in the active site of PPARα/γ, a molecular docking simulation was carried out. Molecular dynamic (MD) simulations indicated that the predicted ligands were stable in the active site of PPARα/γ. Therefore, combination of the CoMFA and structure-based drug design results could be used for further structural alteration and synthesis and development of novel and potent dual agonists. Abbreviations DM diabetes mellitus

T2DM type 2 diabetes

PPARs peroxisome proliferator-activated receptors

LBDD ligand based drug design

3D-QSAR three-dimensional quantitative structure activity relationship

CoMFA comparative molecular field analysis

PLS partial least square

LOO leave-one-out

q² cross-validated correlation coefficient

ONC optimal number of principal components

r² non-cross-validated correlation coefficient

SEE standard error of estimate

F the Fischer ratio

r²_pred predictive correlation coefficient

DBD DNA binding domain

MD molecular dynamics

RMSD root-mean-square deviation

RMSF root mean square fluctuations

Communicated by Ramaswamy H. Sarma