Exploring flavonoid derivatives as potential pancreatic lipase inhibitors for obesity management: An in silico and in vitro study.

Obesity is widely recognized as a major public health issue and is one of the leading causes of death worldwide. Overweight and obesity are prominent lifestyle ailments that not only give rise to additional health issues but also play a role in the development of other chronic diseases, such as cancer, diabetes, metabolic syndrome, and cardiovascular diseases. Orlistat is now the only pharmaceutical drug for the management of obesity. However, prolonged use of orlistat has been associated with detrimental consequences, hence necessitating the development of a new drug with reduced or no adverse reactions. Pancreatic Lipase is a critical enzyme in lipid metabolism. Using naturally occurring compounds as PL inhibitors has garnered significant attention because of their diverse structure and low toxicity. The present work investigates the inhibitory action of flavonoids on PL using in silico and in vitro methods. Thirteen flavonoid derivatives and orlistat were docked with PL. The ADME properties of the flavonoid derivatives were studied, and most of the compounds are in admire range. The stability of the best-docked complexes was checked by REMD. The in silico study demonstrated favorable inhibitory activity of flavonoids compared to orlistat. Consequently, an enzyme inhibitory experiment was conducted to authenticate the in silico results. The lipase inhibitory activity was assessed by using p-nitrophenyl butyrate as the substrate. Kaempferol exhibited significant inhibitory activity against PL, as shown by its IC50 value of 72.7 ± 3 µM. This study proposed a natural drug candidate with promising inhibitory efficacy against PL for obesity.

An In Silico Study on Withania somnifera Bioactives and Curcumin Analogs as Potential Inducers of Smoothened (Smo) Receptor of Sonic Hedgehog (SHH) Pathway to Promote Oligodendrogenesis.

Demyelinating disorder is a subset of neurodegenerative conditions wherein factors such as aging and/or auto-immune attack cause damage and degradation of myelin sheath which enwraps the neuronal axons. Lowered axonal integrity and sub-par conduction of nerve impulses due to impaired action potentials make neurodegeneration imminent as the neurons do not have mitotic ability to replenish their numbers. Oligodendrocytes (OLs) myelinate the axonal segments of neurons and perform neuronal maintenance. Neuroregenerative stem cell therapy exploits this property for remyelination by targeting OL replenishment using in vitro stem cell differentiation protocols for inducing OL lineage cells. But some shortcomings of such protocols are over-reliance on synthetic inducers, lengthy differentiation process, low differentiation efficiency besides being financially expensive. This in silico study sought to identify herbal substitutes of currently available OL-lineage-specific synthetic inducers from a virtual library of curcumin analogs and Withania somnifera bioactives. Smoothened (Smo) receptor belonging to the canonical sonic hedgehog (SHH) signaling pathway promotes in vivo differentiation of OLs as well as their subsequent lineage progression to myelinating OLs. Therefore, we performed pharmacokinetics prediction for the bioactives followed by their molecular docking and molecular dynamics simulation with Smo. From a pool of 1289 curcumin analogs and 80 Withania somnifera-derived bioactives, the best docked ligands were identified as the compounds with PubChem IDs 68815167 and 25880, respectively. Molecular dynamics simulation of these ligands further concluded the Withania somnifera bioactive 25880 to have the best activity with Smo. This compound may be deemed as a potential lead molecule for an agonistic interaction with and activation of Smo to initialize its downstream signaling cascade for enriching OL differentiation.

Structure-guided Design and Optimization of small Molecules as Pancreatic Lipase Inhibitors using Pharmacophore, 3D-QSAR, Molecular Docking, and Molecular Dynamics Simulation Studies.

BACKGROUND: Obesity has now become a global issue due to the increase in the population of obese people. It also substantially impacts the individual's social, financial, and psychological well-being, which may contribute to depression. Being overweight induces many metabolic and chronic disorders, urging many researchers to focus on developing the drug for obesity treatment. Pancreatic lipase inhibitors and natural product/compound-derived pancreatic lipase inhibitors have recently received much attention because of their structural variety and low toxicity. OBJECTIVE: This study aimed to build pharmacophores and QSAR for analyzing the necessary structure of pancreatic lipase inhibitors and designing new molecules with the best activity. METHODS: Ligand-based pharmacophore modeling and Atom-Based 3D-QSAR were carried out using the PHASE module of Schrodinger to determine the critical structural properties necessary for pancreatic lipase (PL) inhibitory activity. A total of 157 phytoconstituents and a standard drug, orlistat, were selected for the present study. Considering the important features for pancreatic lipase inhibition, 15 new molecules were designed and subjected to molecular docking studies and molecular dynamics simulations. The activity of designed molecules was predicted using the Atom- Based QSAR tool of the PHASE module. RESULTS: The top docked score molecule is structure-7 with a docking score of -6.094 Kcal/mol, whereas the docking score of orlistat and tristin is -3.80Kcal/mol and -5.63Kcal/mol, respectively. CONCLUSION: The designed molecules have a high docking score and good stability, are in the desirable ADME range and are derived from natural products, so they might be used as lead molecules for anti-obesity drug development.

Identification of common genes in obesity and cancer through network interaction and targeting those genes by virtual screening approach.

Obesity may have an effect on cancer outcomes, resulting in global inequalities in cancer survival and death. Microarray data analysis was done to identify differentially expressed genes (DEGs) in obese and cancer patients. Total 1977 differentially expressed genes among obesity and gastric cancer, breast cancer, pancreatic cancer, and colorectal cancer were used to build a gene interaction network, which was then analyzed by using Cytoscape software. It has been identified that JUN, CXCL12, and LEP genes show a higher degree and stress, and play an important role in obesity and cancer progression. Further, CXCL12 and LEP were taken for virtual screening study with coumarin and its derivatives to develop a drug against obesity and cancer. The interactions of CXCL12 and LEP with coumarins were studied by molecular docking and it shows good interaction as well as docking score as compared to the standard one. The ADME properties were predicted to check the drug-likeness activity of coumarins and the most of the drug-likeness activities are in admire range. The Binding free energy of the docked complex was calculated by performing MM-GBSA. The molecular docking, ADME properties prediction, and MM-GBSA was performed on Maestro 12.6. The top docked score compounds were further subjected to molecular dynamic simulation to check the stability by using GROMACS. The MM-PBSA study was performed to calculate the binding energy components as well as the energy contributions of specific amino acids. The resultant compounds could be a potent anti-obesity and anti-cancer drug.Communicated by Ramaswamy H. Sarma.

Development of QSAR model using machine learning and molecular docking study of polyphenol derivatives against obesity as pancreatic lipase inhibitor.

In developed countries and developing countries, obesity/overweight is considered a major problem, in fact, it is now recognized as a major metabolic disorder. Additionally, obesity is connected with other metabolic diseases, including cardiovascular disorders, type 2 diabetes, some types of cancer, etc. Therefore, the development of novel drugs/medications for obesity is essential. The best target for treating obesity is Pancreatic Lipase (PL), it breaks 50-70% triglycerides into monoglycerol and free fatty acids.The major aim of this in silico study is to generate a QSAR model by using Multiple Linear Regression (MLR) and to inhibit pancreatic lipase by polyphenol derivatives mainly flavonoids, plant secondary metabolites shows good inhibitory activity against PL, maybe with less unpleasant side effects.In this in silico study, a potent inhibitor was found through calculating drug likness, QSAR (Quantitative structure-activity relationship) and molecular docking. The docking was performed in Maestro 12.0 and the ADME (absorption, distribution, metabolism, and excretion) properties (drug-likeness) of compounds/ligands were predicted by the Qikprop module of Maestro 12.0. The QSAR model was developed to show the relationship between the chemical/structural properties and the compound's biological activity. We have found the best interaction between pancreatic lipase and flavonoids. The best docked compound is Epigallocatechin 3,5,-di-O-gallate with docking score -10.935 kcal/mol .All compounds also show drug-likeness activity.The developed model has satisfied all internal and external validation criteria and has square correlation coefficient (r2) 0.8649, which shows its predictive ability and has good acceptability, predictive ability, and statistical robustness.Communicated by Ramaswamy H. Sarma.

An integrative analysis of GEO data to identify possible therapeutic biomarkers of prostate cancer and targeting potential protein through Zea mays phytochemicals by virtual screening approaches.

Prostate cancer (PC) is a prevalent type of cancer among men. Delaying the treatment of patients with upgraded or upstaged cancer may lead to unmanageable circumstances. The aim of this study is to contribute to the finding of biomarkers that are specific to PC and identify drug candidates derived from plants. The information about cancer is critical for clinicians to make decisions about patient treatment in the era of precision medicine. Advances in genomics technology have opened up new possibilities for identifying genes that are associated with cancer, including PC. This study identifies novel differentially expressed genes for PC. The seven PC microarray datasets were selected from the National Center for Biotechnology Information (NCBI)/Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) were found based on a fold change of |logFC| ≥ 1 and an adjusted p-value of <0.05. The DEGs were further studied using several bioinformatics tools, including STRING, CytoHubba, SRplot, Coremine Medical database, FunRich and GeneMANIA, cBioPortal. The six new potential biomarkers, GAGE2A, GAGE12G, GAGE2E, GAGE13, GAGE12F and CSAG1 were identified. These biomarkers are associated with biological processes (BPs) such as cell division, and gene expression regulation, so these genes may have a crucial role in PC progression and may serve as potential biomarkers for PC. A total of 497 phytochemicals from corn plants have been screened against the target protein and found LTS0176591 as the best lead molecule with docking score of -6.31 kcal/mol. Further, molecular mechanics-generalized born surface area (MM-GBSA), molecular dynamics simulation, principal component analysis (PCA), free energy landscape (FEL) and molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA) were carried out to validate the findings.Communicated by Ramaswamy H. Sarma.