Identification of Novel EGFR Inhibitors for the Targeted Therapy of Colorectal Cancer Using Pharmacophore Modelling, Docking, Molecular Dynamic Simulation and Biological Activity Prediction.

BACKGROUND: Colorectal cancer (CRC) is considered the second deadliest cancer in the world. One of the reasons for the occurrence of this cancer is the deregulation of the Epidermal Growth Factor Receptor (EGFR), which plays a critical role in regulating cell division, persistence, differentiation, and migration. The overexpression of the EGFR protein leads to its dysregulation and causes CRC. OBJECTIVES: Hence, this work aims to identify and validate novel EGFR inhibitors for the treatment of colorectal cancer employing various computer aided techniques such as pharmacophore modeling, docking, molecular dynamic simulation and Quantitative Structure-Activity Relationship (QSAR) analysis. METHODS: In this work, a shared-featured ligand-based pharmacophore model was generated using the known inhibitors of EGFR. The best model was validated and screened against ZincPharmer and Maybridge databases, and 143 hits were obtained. Pharmacokinetic and toxicological properties of these hits were studied, and the acceptable ligands were docked against EGFR. The best five protein-ligand complexes with binding energy less than -5 kcal/mol were selected. The molecular dynamic simulation studies of these complexes were conducted for 100 nanoseconds (ns), and the results were analyzed. The biological activity of this ligand was calculated using QSAR analysis. RESULTS: The best complex with Root Mean Square Deviation (RMSD) 3.429 Å and Radius of Gyration (RoG) 20.181 Å was selected. The Root Mean Square Fluctuations (RMSF) results were also found to be satisfactory. The biological activity of this ligand was found to be 1.38 µM. CONCLUSION: This work hereby proposes the ligand 2-((1,6-dimethyl-4-oxo-1,4-dihydropyridin-3-yl)oxy)-N- (1H-indol-4-yl)acetamide as a potential EGFR inhibitor for the treatment of colorectal cancer. The wet lab analysis must be conducted, however, to confirm this hypothesis.

Modelling of AgrA inhibitors to combat anti-microbial resistance in Staphylococcus aureus.

Staphylococcus aureus is a Gram-positive bacterium found on human skin that causes skin and soft tissue infections, as well as pneumonia, osteomyelitis, and endocarditis. The prevalence of antibiotic resistant strains has made the treatments less effective. An efficient alternate method for battling these contagious diseases is anti-virulence strategy. The AgrA protein, a key activator of Accessory Gene Regulator system in S. aureus, is vital to the virulence of the organism and, consequently, its pathogenesis. Using a Machine Learning algorithm, the Support Vector Machine (SVM), and a ligand-based pharmacophore modelling method, prediction models of AgrA inhibitors were developed. The metrics of the SVM model were inadequate, hence it was not used for virtual screening. For ligand-based pharmacophore modelling, 14 of 29 compounds were removed from the active set due to a lack of shared pharmacophore properties, and 504 compounds were designated as decoys. A 3D pharmacophore model was created using LigandScout 4.4.5, with a fit score of 57.48, including a positive ionizable group, one hydrogen bond donor, and three hydrogen bond acceptors. The model after further validation was used to virtually screen an external database which resulted in six hits. These compounds were docked with the AgrA domain crystal structure to determine the inhibitor activity. Further, each docked complex was subjected to a 100 ns molecular dynamics simulation. CID238 and CID20510252 demonstrated potent inhibitory binding interactions and hence can be used to develop AgrA inhibitors in future after proper validation.Communicated by Ramaswamy H. Sarma.

In silico discovery of novel calcineurin inhibitors using ligand-based 3-D pharmacophore modelling and molecular dynamics simulation.

Calcineurin is a serine-threonine protein phosphatase that is activated with the binding of calmodulin in the presence of increased calcium concentration and has a major role in various signaling pathways. Its role in regulating homeostasis, developmental processes, and different disease progression has already been reported. The dysregulated Ca2+/calcineurin/NFAT1-4 pathway is observed in Autoimmune disorders and hence the use of Calcineurin inhibitors like Cyclosporin A (CsA) and Tacrolimus (FK506) is widely done in such cases. Recent studies indicate the uncontrolled overexpression of the Calcineurin protein in the pathophysiological pathway of neurodegenerative diseases. The in vitro and animal model studies with standard calcineurin inhibitors (CnIs), which are widely labeled as immunosuppressant drugs, have shown a significant reduction of neurodegeneration in respective models. These results compel the identification of novel calcineurin inhibitors against neurodegenerative diseases. With this scenario, the present work focuses on the computer-aided identification of novel CnIs via ligand-based 3-D pharmacophore modelling. Known CnIs, CsA, and FK506, were used to build the pharmacophore models which were validated and screened against external databases to retrieve possible hits. Docking investigations, pharmacokinetic properties, and molecular dynamics simulations along with toxicity predictions were performed on the hits that were obtained. According to the study, a total of 5 molecules ILB 162, ILB 005, ILB 439, ILB 390, and ILB 198, were found to be the best calcineurin inhibitors with binding affinity in the range of -9.7 to -9.0 Kcal/mol with 1MF8 (PDB). The stability of interactions of these molecules was further validated via Molecular dynamics simulation studies to confirm these to be the potential calcineurin-inhibiting molecules. HIGHLIGHTSCalcineurin inhibitors can be a novel therapeutic candidate against neurodegenerative diseases.The identification of novel Calcineurin inhibitors was done in silico using ligand-based 3-D pharmacophore modelling using Ligand Scout Essential 4.4. software.The model could identify 440 hits from various external databases like PubChem (2432 molecules), ChemSpider, MayBridge, DrugBank, and e-Drug 3D by Cheminformatic Tools and Databases for Pharmacology.Out of which 5 molecules: ILB 162, ILB 005, ILB 439, ILB 390, and ILB 198, were found to be the best calcineurin inhibitors with binding affinity in the range of -9.7 to -9.0 Kcal/mol with 1MF8 (PDB) which were further confirmed to be the best CnI candidates via Molecular dynamics simulation studies.Communicated by Ramaswamy H. Sarma.

Drug-likeness of Phytic Acid and Its Analogues.

Inositol hexakisphosphate is known to be the phosphorous reserve in plants particularly in the seeds. Though it has been known for its antinutrient properties for many years, recent research shed light to reveal it as a novel anticancer agent. Hence the present study investigates the drug-likeness of phytic acid and its analogues through bioinformatics methods. Two potential cancer drug targets such as mitogen activated kinase and inositol 1,4,5-triphosphate receptor are included in the study. Out of 50 selected analogues of phytic acid, 42 structures interact well with the chosen drug targets. The best interacting structures are 1-diphosinositol pentakisphosphate and 2,3,4,5,6-pentaphosphonooxycyclohexyl dihydrogen phosphate. For both of these structures, the negative binding energy obtained was -49.5 KJ/mol; this affirms the stability of the complex. ADME properties are also predicted to assess the drug-like properties of the compounds. The structure activity relationship model is generated for 12 compounds with experimental IC50 values.