Identification of dual kinase inhibitors of CK2 and GSK3β: combined qualitative and quantitative pharmacophore modeling approach

Chemical Probes Review: Choosing the Right Path Towards Pharmacological Targets in Drug Discovery, Challenges and Future Perspectives

Chemical probes are essential for academic research and target validation for disease identification. They facilitate drug discovery, target function investigation, and translation studies. A chemical probe provides starting material that can accelerate therapeutic values and safety measures for identifying any biological target in drug discovery. Essential read outs depend on their versatility in biochemical testing, proving the hypothesis, selectivity, specificity, affinity towards the target site, and valuable in new therapeutic approaches. Disease management will depend upon chemical probes as a primitive tool to ascertain the physicochemical stability for in vivo and in vitro studies useful for clinical trials and industrial application in the future. For cancer research, bacterial infection, and neurodegenerative disorders, chemical probes are integrated circuits which are on pipeline for the drug discovery process Furthermore, pharmacological modulators incorporate activators, crosslinkers, degraders, and inhibitors. Reports accessed depend on their structural, mechanical, biochemical, and pharmacological characterization in drug discovery research. The perspective for designing any chemical probes concludes with the utilization of drug discovery and identification of the potential target. It focuses mainly on evidence-based studies and produces promising results in successfully delivering novel therapeutics to treat cancers and other disorders at the target site. Moreover, natural product pharmacophores like rapamycin, cephalosporin, and α-lactamase are utilized for drug discovery. Chemical probes revolutionize computational-based study design depending on identifying novel targets within the database framework. Chemical probes are the clinical answers for drug development and goforward tools in solving other riddles for scientists and researchers working in this industries.

Pharmacophore development, drug-likeness analysis, molecular docking, and molecular dynamics simulations for identification of new CK2 inhibitors

Protein kinase 2 (CK2), an essential serine/threonine casein kinase, is considered an interesting target for cancer treatments. Different molecular modeling approaches such as pharmacophore modeling, molecular docking, and molecular dynamics simulations have been used to develop new CK2 inhibitors. This study presents a pharmacophore model that was generated by combining and merging the structure-based and ligand-based pharmacophore features and validated using receiver operating characteristic (ROC). Based on validation results revealing good predictive ability, this pharmacophore model was used as a three-dimensional query in a virtual screening simulation. Several compounds with different chemical scaffolds were retrieved as hits, which were further analyzed and refined using several molecular property filters. The obtained compounds were then filtered and compared to the crystallographic ligand on the basis of their predicted docking energies, binding mode, and interactions with CK2 active site residues. This step resulted in a compound with a high pharmacophore fit value and better docking energy. Molecular dynamics simulation indicated stable binding of the predicted compound to CK2 protein, characterized by root mean square deviation (RMSD) and root mean square fluctuation (RMSF) and hydrogen bond. Graphical abstract.

User-Friendly Quantum Mechanics: Applications for Drug Discovery

Quantum mechanics (QM) methods provide a fine description of receptor-ligand interactions and of chemical reactions. Their use in drug design and drug discovery is increasing, especially for complex systems including metal ions in the binding sites, for the design of highly selective inhibitors, for the optimization of bi-specific compounds, to understand enzymatic reactions, and for the study of covalent ligands and prodrugs. They are also used for generating molecular descriptors for predictive QSAR/QSPR models and for the parameterization of force fields. Thanks to the continuous increase of computational power offered by GPUs and to the development of sophisticated algorithms, QM methods are becoming part of the standard tools used in computer-aided drug design (CADD). We present the most used QM methods and software packages, and we discuss recent representative applications in drug design and drug discovery.

Virtual identification of novel peroxisome proliferator-activated receptor (PPAR) α/δ dual antagonist by 3D-QSAR, molecule docking, and molecule dynamics simulation

The therapeutic potential of PPARs antagonists extends beyond diabetes. PPARs antagonists represent a new drug class that holds promise as a broadly applicable therapeutic approach for cancer treatment. Thus, there is a strong need to develop a rational design strategy for creating PPARs antagonists. In this study, three-dimensional quantitative structure-activity relationship (3D-QSAR) models of PPARα receptor (CoMFA-1, q ² = 0.636, r ² = 0.953; CoMSIA-1, q ² = 0.779, r ² = 0.999) and PPARδ receptor (CoMFA-2, q ² = 0.624, r ² = 0.906; CoMSIA-2, q ² = 0.627, r ² = 0.959) were successfully constructed using 35 triazolone ring derivatives. Contour map analysis revealed that the electrostatic and hydrophobic fields played vital roles in the bioactivity of dual antagonists. Molecular docking studies suggested that the hydrogen bonding, electrostatic and hydrophobic interactions all influenced the binding of receptor-ligand complex. Based on the information obtained above, we designed a series of compounds. The docking results were mutually validated with 3D-QSAR results. Three-dimensional-QSAR and absorption, distribution, metabolism, excretion and toxicity (ADMET) predictions indicated that 19 newly designed compounds possessed excellent biological activity and physicochemical properties. In summary, this research could provide theoretical guidance for the structural optimization of novel PPARα and δ dual antagonists. Communicated by Ramaswamy H. Sarma.

Design, synthesis, and evaluation of 4,5,6,7-tetrahydrobenzo[d]thiazole-based novel dual kinase inhibitors of CK2 and GSK3β

Casein kinase 2 (CK2) and glycogen synthase kinase-3beta (GSK3β) are responsible for the phosphorylation of a tumor suppressor protein (PTEN) in a cooperative manner which causes its deactivation. Thus, it is essential to inhibit both kinases simultaneously to prevent PTEN deactivation more efficiently. In this study, we have designed a novel lead from Hit15 which was identified in silico as a dual kinase inhibitor against CK2 and GSK3β through our previous study. The dataset of structural analogs of the lead was designed and confirmed by pharmacophore mapping and molecular docking. The screened analogs were considered further and a series of "tetrahydrobenzo[d]thiazoles" were synthesized. Compound 1g has shown highest dual kinase inhibitory activity at a concentration of 1.9 μM against CK2 and 0.67 μM against GSK3β. Our results suggest that the presence of a carboxyl group at the meta position of the phenyl ring plays a vital role in dual kinase inhibition.

Density functional theory based probe of the affinity interaction of saccharide ligands with extra-cellular sialic acid residues

Changes in glycosylation pattern leads to malignant transformations among the cells. In combination with upregulated actions of sialyltransferases, it ultimately leads to differential expression of sialic acid (SA) at cell surface. Given its negative charge and localization to extracellular domain, SA has been exploited for the development of targeted theranostics using approaches, such as, cationization and appending recognition saccharides on carrier surface. In this study, we have performed quantum mechanical calculations based on density functional theory (DFT) to study the interaction of saccharides with extracellular SA. Gradient-corrected DFT with the three parameter function (B3) was utilized for the calculation of Lee-Yang-Parr (LYP) correlation function. Atomic charge, vibrational frequencies and energy of the optimized structures were calculated through B3LYP. Our calculations demonstrate a stronger galactose-sialic acid interaction at tumour-relevant low pH and hyperthermic condition. These results support the application of pH responsive delivery vehicles and targeted hyperthermic chemotherapy for eradicating solid tumour deposits. These studies, conducted a priori, can guide the formulation scientists over appropriate choice of ligands and their applications in the design of 'smart' theranostic tools.