Exploration of Flavonoids as Lead Compounds against Ewing Sarcoma through Molecular Docking, Pharmacogenomics Analysis, and Molecular Dynamics Simulations

Discovery of Novel Aldose Reductase Inhibitors via the Integration of Ligand-Based and Structure-Based Virtual Screening with Experimental Validation

Aldose reductase plays a central role in diabetes mellitus (DM) associated complications by converting glucose to sorbitol, resulting in a harmful increase of reactive oxygen species (ROS) in various tissues, such as the heart, vasculature, neurons, eyes, and kidneys. We employed a comprehensive approach, integrating both ligand- and structure-based virtual screening followed by experimental validation. Initially, candidate compounds were extracted from extensive drug and chemical libraries using the DeepChem's GraphConvMol algorithm, leveraging its capacity for robust molecular feature representation. Subsequent refinement employed molecular docking and molecular dynamics (MD) simulations, which are crucial for understanding compound-receptor interactions and dynamic behavior in a simulated physiological environment. Finally, the candidate compounds were subjected to experimental validation of their biological activity using an aldose reductase inhibitor screening kit. The comprehensive approach led to the identification of a promising compound, demonstrating significant potential as an aldose reductase inhibitor. This comprehensive approach not only yields a potential therapeutic intervention for DM-related complications but also establishes an integrated protocol for drug development, setting a new benchmark in the field.

Discovery of GABA Aminotransferase Inhibitors via Molecular Docking, Molecular Dynamic Simulation, and Biological Evaluation

γ-Aminobutyric acid aminotransferase (GABA-AT) is a pyridoxal 5'-phosphate (PLP)-dependent enzyme that degrades γ-aminobutyric (GABA) in the brain. GABA is an important inhibitory neurotransmitter that plays important neurological roles in the brain. Therefore, GABA-AT is an important drug target that regulates GABA levels. Novel and potent drug development to inhibit GABA-AT is still a very challenging task. In this study, we aimed to devise novel and potent inhibitors against GABA-AT using computer-aided drug design (CADD) tools. Since the crystal structure of human GABA-AT was not yet available, we utilized a homologous structure derived from our previously published paper. To identify highly potent compounds relative to vigabatrin, an FDA-approved drug against human GABA-AT, we developed a pharmacophore analysis protocol for 530,000 Korea Chemical Bank (KCB) compounds and selected the top 50 compounds for further screening. Preliminary biological analysis was carried out for these 50 compounds and 16 compounds were further assessed. Subsequently, molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations were carried out. In the results, four predicted compounds, A07, B07, D08, and H08, were found to be highly potent and were further evaluated by a biological activity assay to confirm the results of the GABA-AT activity inhibition assay.

Machine Learning-Based Drug Repositioning of Novel Janus Kinase 2 Inhibitors Utilizing Molecular Docking and Molecular Dynamic Simulation

Machine learning algorithms have been increasingly applied in drug development due to their efficiency and effectiveness. Machine learning-based drug repurposing can contribute to the identification of novel therapeutic applications for drugs with other indications. The current study used a trained machine learning model to screen a vast chemical library for new JAK2 inhibitors, the biological activities of which were reported. Reference JAK2 inhibitors, comprising 1911 compounds, have experimentally determined IC50 values. To generate the input to the machine learning model, reference compounds were subjected to RDKit, a cheminformatic toolkit, to extract molecular descriptors. A Random Forest Regression model from the Scikit-learn machine learning library was applied to obtain a predictive regression model and to analyze each molecular descriptor's role in determining IC50 values in the reference data set. Then, IC50 values of the library compounds, comprised of 1,576,903 compounds, were predicted using the generated regression model. Interestingly, some compounds that exhibit high IC50 values from the prediction were reported to possess JAK inhibition activity, which indicates the limitations of the prediction model. To confirm the JAK2 inhibition activity of predicted compounds, molecular docking and molecular dynamics simulation were carried out with the JAK inhibitor reference compound, tofacitinib. The binding affinity of docked compounds in the active region of JAK2 was also analyzed by the gmxMMPBSA approach. Furthermore, experimental validation confirmed the results from the computational analysis. Results showed highly comparable outcomes concerning tofacitinib. Conclusively, the machine learning model can efficiently improve the virtual screening of drugs and drug development.

EWS/FLI1 Characterization, Activation, Repression, Target Genes and Therapeutic Opportunities in Ewing Sarcoma

Despite their clonal origins, tumors eventually develop into complex communities made up of phenotypically different cell subpopulations, according to mounting evidence. Tumor cell-intrinsic programming and signals from geographically and temporally changing microenvironments both contribute to this variability. Furthermore, the mutational load is typically lacking in childhood malignancies of adult cancers, and they still exhibit high cellular heterogeneity levels largely mediated by epigenetic mechanisms. Ewing sarcomas represent highly aggressive malignancies affecting both bone and soft tissue, primarily afflicting adolescents. Unfortunately, the outlook for patients facing relapsed or metastatic disease is grim. These tumors are primarily fueled by a distinctive fusion event involving an FET protein and an ETS family transcription factor, with the most prevalent fusion being EWS/FLI1. Despite originating from a common driver mutation, Ewing sarcoma cells display significant variations in transcriptional activity, both within and among tumors. Recent research has pinpointed distinct fusion protein activities as a principal source of this heterogeneity, resulting in markedly diverse cellular phenotypes. In this review, we aim to characterize the role of the EWS/FLI fusion protein in Ewing sarcoma by exploring its general mechanism of activation and elucidating its implications for tumor heterogeneity. Additionally, we delve into potential therapeutic opportunities to target this aberrant fusion protein in the context of Ewing sarcoma treatment.

Nelumbo nucifera Leaf Extract Induces Cytotoxicity in Osteosarcoma Saos-2 Cells

Background Osteosarcoma is the eighth most common cancer and its prevalence in children makes it a global concern. Existing medications and treatments like high-dose methotrexate possess harmful side effects. Therefore, novel herbal drugs like Nelumbo nucifera are of utmost importance. Aim  To analyze a novel anticancer herbal drug, Nelumbo nucifera leaf extract for its cytotoxic potential against osteosarcoma.  Materials and method Nelumbo nucifera leaf extract was prepared. Saos-2 Cells (human osteosarcoma cell line) were treated with Nelumbo nucifera leaf extract (25, 50, 75, 100, 125, and 150 µg/ml) for 24 hours which were then subjected to MTT assay, morphological analysis and DAPI staining. Results The results suggested that Nelumbo nucifera leaf extract had a concentration-dependent cytotoxic effect on Saos-2 cell line. The extract significantly reduced the number of viable cells, inhibited proliferation and induced morphological changes in Saos-2 cells.  Conclusion Nelumbo nucifera has the potential to induce cytotoxicity against osteosarcoma cell lines and hence, this study provides a novel therapeutic regimen for the treatment of osteosarcoma.

Oncomeric Profiles of microRNAs as New Therapeutic Targets for Treatment of Ewing's Sarcoma: A Composite Review

Ewing's sarcoma is a rare type of cancer that forms in bones and soft tissues in the body, affecting mostly children and young adults. Current treatments for ES are limited to chemotherapy and/or radiation, followed by surgery. Recently, microRNAs have shown favourable results as latent diagnostic and prognostic biomarkers in various cancers. Furthermore, microRNAs have shown to be a good therapeutic agent due to their involvement in the dysregulation of various molecular pathways linked to tumour progression, invasion, angiogenesis, and metastasis. In this review, comprehensive data mining was employed to explore various microRNAs that might have therapeutic potential as target molecules in the treatment of ES.

Vismodegib Identified as a Novel COX-2 Inhibitor via Deep-Learning-Based Drug Repositioning and Molecular Docking Analysis

Artificial intelligence algorithms have been increasingly applied in drug development due to their efficiency and effectiveness. Deep-learning-based drug repurposing can contribute to the identification of novel therapeutic applications for drugs with other indications. The current study used a trained deep-learning model to screen an FDA-approved drug library for novel COX-2 inhibitors. Reference COX-2 data sets, composed of active and decoy compounds, were obtained from the DUD-E database. To extract molecular features, compounds were subjected to RDKit, a cheminformatic toolkit. GraphConvMol, a graph convolutional network model from DeepChem, was applied to obtain a predictive model from the DUD-E data sets. Then, the COX-2 inhibitory potential of the FDA-approved drugs was predicted using the trained deep-learning model. Vismodegib, an anticancer agent that inhibits the hedgehog signaling pathway by binding to smoothened, was predicted to inhibit COX-2. Noticeably, some compounds that exhibit high potential from the prediction were known to be COX-2 inhibitors, indicating the prediction model's liability. To confirm the COX-2 inhibition activity of vismodegib, molecular docking was carried out with the reference compounds of the COX-2 inhibitor, celecoxib, and ibuprofen. Furthermore, the experimental examination of COX-2 inhibition was also carried out using a cell culture study. Results showed that vismodegib exhibited a highly comparable COX-2 inhibitory activity compared to celecoxib and ibuprofen. In conclusion, the deep-learning model can efficiently improve the virtual screening of drugs, and vismodegib can be used as a novel COX-2 inhibitor.

Computational Exploration of the Effects of Mutations on GABA Aminotransferase in GABA Aminotransferase Deficiency

Gamma-aminobutyric acid (GABA) transaminase-also called GABA aminotransferase (GABA-AT)-deficiency is a rare autosomal recessive disorder characterized by a severe neonatal-infantile epileptic encephalopathy with symptoms such as seizures, hypotonia, hyperreflexia, developmental delay, and growth acceleration. GABA transaminase deficiency is caused by mutations in GABA-AT, the enzyme responsible for the catabolism of GABA. Mutations in multiple locations on GABA-AT have been reported and their locations have been shown to influence the onset of the disease and the severity of symptoms. We examined how GABA-AT mutations influence the structural stability of the enzyme and GABA-binding affinity using computational methodologies such as molecular dynamics simulation and binding free energy calculation to understand the underlying mechanism through which GABA-AT mutations cause GABA-AT deficiency. GABA-AT 3D model depiction was carried out together with seven individual mutated models of GABA-AT. The structural stability of all the predicted models was analyzed using several tools and web servers. All models were evaluated based on their phytochemical values. Additionally, 100 ns MD simulation was carried out and the mutated models were evaluated using RMSD, RMSF, R_g, and SASA. gmxMMPBSA free energy calculation was carried out. Moreover, RMSD and free energy calculations were also compared with those obtained using online web servers. Our study demonstrates that P152S, Q296H, and R92Q play a more critical role in the structural instability of GABA-AT compared with the other mutated models: G465R, L211F, L478P, and R220K.

Investigation of Flavonoid Scaffolds as DAX1 Inhibitors against Ewing Sarcoma through Pharmacoinformatic and Dynamic Simulation Studies

Dosage-sensitive sex reversal, adrenal hypoplasia critical region, on chromosome X, gene 1 (DAX1) is an orphan nuclear receptor encoded by the NR0B1 gene. The functional study showed that DAX1 is a physiologically significant target for EWS/FLI1-mediated oncogenesis, particularly Ewing Sarcoma (ES). In this study, a three-dimensional DAX1 structure was modeled by employing a homology modeling approach. Furthermore, the network analysis of genes involved in Ewing Sarcoma was also carried out to evaluate the association of DAX1 and other genes with ES. Moreover, a molecular docking study was carried out to check the binding profile of screened flavonoid compounds against DAX1. Therefore, 132 flavonoids were docked in the predicted active binding pocket of DAX1. Moreover, the pharmacogenomics analysis was performed for the top ten docked compounds to evaluate the ES-related gene clusters. As a result, the five best flavonoid-docked complexes were selected and further evaluated by Molecular Dynamics (MD) simulation studies at 100 ns. The MD simulation trajectories were evaluated by generating RMSD, hydrogen bond plot analysis, and interaction energy graphs. Our results demonstrate that flavonoids showed interactive profiles in the active region of DAX1 and can be used as potential therapeutic agents against DAX1-mediated augmentation of ES after in-vitro and in-vivo evaluations.

Computational Exploration of Licorice for Lead Compounds against Plasmodium vivax Duffy Binding Protein Utilizing Molecular Docking and Molecular Dynamic Simulation

Plasmodium vivax (P. vivax) is one of the human's most common malaria parasites. P. vivax is exceedingly difficult to control and eliminate due to the existence of extravascular reservoirs and recurring infections from latent liver stages. Traditionally, licorice compounds have been widely investigated against viral and infectious diseases and exhibit some promising results to combat these diseases. In the present study, computational approaches are utilized to study the effect of licorice compounds against P. vivax Duffy binding protein (DBP) to inhibit the malarial invasion to human red blood cells (RBCs). The main focus is to block the DBP binding site to Duffy antigen receptor chemokines (DARC) of RBC to restrict the formation of the DBP-DARC complex. A molecular docking study was performed to analyze the interaction of licorice compounds with the DARC binding site of DBP. Furthermore, the triplicates of molecular dynamic simulation studies for 100 ns were carried out to study the stability of representative docked complexes. The leading compounds such as licochalcone A, echinatin, and licochalcone B manifest competitive results against DBP. The blockage of the active region of DBP resulting from these compounds was maintained throughout the triplicates of 100 ns molecular dynamic (MD) simulation, maintaining stable hydrogen bond formation with the active site residues of DBP. Therefore, the present study suggests that licorice compounds might be good candidates for novel agents against DBP-mediated RBC invasion of P. vivax.