Development of a credible 3D-QSAR CoMSIA model and docking studies for a series of triazoles and tetrazoles containing 11β-HSD1 inhibitors

2,5-Disubstituted furan derivatives containing imidazole, triazole or tetrazole moiety as potent α-glucosidase inhibitors

α-Glucosidase inhibitors (AGIs) are oral antidiabetic drugs, preferably used in treating type 2 diabetes mellitus, that delay the absorption of carbohydrates from the gastrointestinal system. In this work, 2,5-disubstituted furan derivatives containing imidazole, triazole or tetrazole moiety (III-01 ∼ III-45) were synthesized and characterized by elemental analysis, HRMS, ¹H NMR, ¹³C NMR and single crystal X-ray. Their inhibitory activity against α-glucosidase was screened. The most promising inhibitors were compound III-11 (IC₅₀ = 6.0 ± 1.1 μM), III-16 (IC₅₀ = 2.2 ± 0.2 μM) and III-39 (IC₅₀ = 4.6 ± 1.9 μM), respectively. Kinetic study revealed that compounds III-11 and III-39 were uncompetitive inhibitors against α-glucosidase. Meanwhile, III-16 (Ki = 5.1 ± 0.7 μM) was a competitive inhibitor. Furthermore, molecular docking studies indicated that the existence of the azole group played a critically important role in hydrogen bond interaction with α-glucosidase. Significantly, in vivo toxicity towards HEK293 cells, RAW264.7 cells and HepG2 cells suggested that compounds III-11 and III-39 possessed non-toxicity, that could be considered as potential candidates for further development of novel antidiabetic drugs.

Molecular modelling of quinoline derivatives as telomerase inhibitors through 3D-QSAR, molecular dynamics simulation, and molecular docking techniques

Rising mortality due to cancer has led to the development and identification of newer targets and molecules to cure the disease. Telomerase is one of the attractive targets for design of many chemotherapeutic drugs. This research highlights the designing of novel telomerase inhibitors using ligand-based (3D-QSAR) and structure-based (molecular docking and molecular dynamics simulation) approaches. For the development of the 3D-QSAR model, 37 synthetic molecules reported earlier as telomerase inhibitors were selected from diversified literature. Three different alignment methods were explored; among them, distill alignment was found to be the best method with good statistical results and was used for the generation of QSAR model. Statistically significant CoMSIA model with a correlation coefficient (r²_ncv) value of 0.974, leave one out (q²) value of 0.662 and predicted correlation coefficient (r²_pred) value of 0.560 was used for the analysis of QSAR. For the MDS study, A-chain of telomerase was stabilised for 50 ns with respect to 1-atm pressure, with an average temperature of 299.98 k and with potential energy of 1,145,336 kJ/m converged in 997 steps. Furthermore, the behaviour study of variants towards the target revealed that active variable gave better affinity without affecting amino acid sequences and dimensions of protein which was accomplished through RMSD, RMSF and Rg analysis. Results of molecular docking study supported the outcomes of QSAR contour maps as ligand showed similar interactions with surrounded amino acids which were identified in contour map analysis. The results of the comprehensive study might be proved valuable for the development of potent telomerase inhibitors.

Targeting Tumors Using Peptides

To penetrate solid tumors, low molecular weight (Mw < 10 KDa) compounds have an edge over antibodies: their higher penetration because of their small size. Because of the dense stroma and high interstitial fluid pressure of solid tumors, the penetration of higher Mw compounds is unfavored and being small thus becomes an advantage. This review covers a wide range of peptidic ligands—linear, cyclic, macrocyclic and cyclotidic peptides—to target tumors: We describe the main tools to identify peptides experimentally, such as phage display, and the possible chemical modifications to enhance the properties of the identified peptides. We also review in silico identification of peptides and the most salient non-peptidic ligands in clinical stages. We later focus the attention on the current validated ligands available to target different tumor compartments: blood vessels, extracelullar matrix, and tumor associated macrophages. The clinical advances and failures of these ligands and their therapeutic conjugates will be discussed. We aim to present the reader with the state-of-the-art in targeting tumors, by using low Mw molecules, and the tools to identify new ligands.

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

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.

Computational prediction models for assessing endocrine disrupting potential of chemicals

Endocrine disrupting chemicals (EDCs) mimic natural hormones and disrupt endocrine function. Humans and wildlife are exposed to EDCs might alter endocrine functions through various mechanisms and lead to an adverse effects. Hence, EDCs identification is important to protect the ecosystem and to promote the public health. Leveraging in-vitro and in-vivo experiments to identify potential EDCs is time consuming and expensive. Hence, quantitative structure-activity relationship is applied to screen the potential EDCs. Here, we summarize the predictive models developed using various algorithms to forecast the binding activity of chemicals to the estrogen and androgen receptors, alpha-fetoprotein, and sex hormone binding globulin.

Insight into the structural requirements of pyrimidine-based phosphodiesterase 10A (PDE10A) inhibitors by multiple validated 3D QSAR approaches

Schizophrenia is a complex disorder of thinking and behaviour (0.3-0.7% of the population is affected). The over-expression of phosphodiesterase 10A (PDE10A) enzyme may be a potential target for schizophrenia and Huntington's disease. Because 3D QSAR analysis is one of the most frequently used modelling techniques, in the present study, five different 3D QSAR tools, namely CoMFA, CoMSIA, kNN-MFA, Open3DQSAR and topomer CoMFA methods, were used on a dataset of pyrimidine-based PDE10A inhibitors. All developed models were validated internally and externally. The non-commercial Open3DQSAR produced the best statistical results amongst 3D QSAR tools. The structural interpretations obtained from different methods were thoroughly analysed and were justified on the basis of information obtained from the crystal structure. Information from one method was mostly validated by the results of other methods and vice versa. In the current work, the use of multiple tools in the same analysis revealed more complete information about the structural requirements of these compounds. On the basis of the observations of the 3D QSAR studies, 12 new compounds were designed for better PDE10A inhibitory activity. The current investigation may help in further designing new PDE10A inhibitors with promising activity.