Exploration of Potential Ewing Sarcoma Drugs from FDA-Approved Pharmaceuticals through Computational Drug Repositioning, Pharmacogenomics, Molecular Docking, and MD Simulation Studies

Toxicity and therapeutic property of dioxopiperidin derivative SKT40 demonstrated in-vivo zebrafish model due to inflammatory bowel disease

Inflammatory bowel disease (IBD) encompasses chronic disorders that cause severe inflammation in the digestive tract. This study evaluates (E)-3-(3,4-dichlorophenyl)-N-(2,6-dioxopiperidin-3-yl) acrylamide (named SKT40), a derivative of dioxopiperidinamide, as a potential novel treatment for IBD. The pharmacological activity of SKT40 indicated positive interactions using network pharmacology and molecular docking in silico. In vivo, adult and larval zebrafish were tested to evaluate the effectiveness of SKT40 at different concentrations (7.5 μM, 10 μM, 15 μM) in preventing dextran sulfate sodium (DSS)-induced intestinal inflammation. The administration of SKT40 resulted in positive effects by reducing reactive oxygen species (ROS), lipid peroxidation, and cell apoptosis in zebrafish larvae. SKT40 demonstrated a significant reduction in intestinal damage in adult zebrafish by increasing antioxidant enzymes that combat the causes of IBD, such as superoxide dismutase (SOD), catalase (CAT), glutathione-S-transferase (GST), and glutathione peroxidase (GPx). It also reduces cellular damage and inflammation, as indicated by decreased levels of lactate dehydrogenase (LDH) and malondialdehyde (MDA). Gene expression analysis identified downregulation in gene expression of inflammatory mediators such as TNF-α, IL-1β, COX-2, and IL-6. Histopathological analysis showed tissue repair from DSS-induced damage and indicated reduced hyperplasia of goblet cells. These findings suggest that SKT40 effectively treats intestinal damage, highlighting its potential as a promising candidate for IBD therapy.

Identification of Potential Tryptase Inhibitors from FDA-Approved Drugs Using Machine Learning, Molecular Docking, and Experimental Validation

This study explores the innovative use of machine learning (ML) to identify novel tryptase inhibitors from a library of FDA-approved drugs, with subsequent confirmation via molecular docking and experimental validation. Tryptase, a significant mediator in inflammatory and allergic responses, presents a therapeutic target for various inflammatory diseases. However, the development of effective tryptase inhibitors has been challenging due to the enzyme's complex activation and regulation mechanisms. Utilizing a machine learning model, we screened an extensive FDA-approved drug library to identify potential tryptase inhibitors. The predicted compounds were then subjected to molecular docking to assess their binding affinity and conformation within the tryptase active site. Experimental validation was performed using RBL-2H3 cells, a rat basophilic leukemia cell line, where the efficacy of these compounds was evaluated based on their ability to inhibit tryptase activity and suppress β-hexosaminidase activity and histamine release. Our results demonstrated that several FDA-approved drugs, including landiolol, laninamivir, and cidofovir, significantly inhibited tryptase activity. Their efficacy was comparable to that of the FDA-approved mast cell stabilizer nedocromil and the investigational agent APC-366. These findings not only underscore the potential of ML in accelerating drug repurposing but also highlight the feasibility of this approach in identifying effective tryptase inhibitors. This research contributes to the field of drug discovery, offering a novel pathway to expedite the development of therapeutics for tryptase-related pathologies.

Virtual Screening of Flavonoids against Plasmodium vivax Duffy Binding Protein Utilizing Molecular Docking and Molecular Dynamic Simulation

BACKGROUND

Plasmodium vivax (P. vivax) is one of the highly prevalent human malaria parasites. Due to the presence of extravascular reservoirs, P. vivax is extremely challenging to manage and eradicate. Traditionally, flavonoids have been widely used to combat various diseases. Recently, biflavonoids were discovered to be effective against Plasmodium falciparum.

METHODS

In this study, in silico approaches were utilized to inhibit Duffy binding protein (DBP), responsible for Plasmodium invasion into red blood cells (RBC). The interaction of flavonoid molecules with the Duffy antigen receptor for chemokines (DARC) binding site of DBP was investigated using a molecular docking approach. Furthermore, molecular dynamic simulation studies were carried out to study the stability of top-docked complexes.

RESULTS

The results showed the effectiveness of flavonoids, such as daidzein, genistein, kaempferol, and quercetin, in the DBP binding site. These flavonoids were found to bind in the active region of DBP. Furthermore, the stability of these four ligands was maintained throughout the 50 ns simulation, maintaining stable hydrogen bond formation with the active site residues of DBP.

CONCLUSION

The present study suggests that flavonoids might be good candidates and novel agents against DBP-mediated RBC invasion of P. vivax and can be further analyzed in in vitro studies.

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.

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.

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.

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

Ewing sarcoma (ES) is a highly malignant carcinoma prevalent in children and most frequent in the second decade of life. It mostly occurs due to t(11;22) (q24;q12) translocation. This translocation encodes the oncogenic fusion protein EWS/FLI (Friend leukemia integration 1 transcription factor), which acts as an aberrant transcription factor to deregulate target genes essential for cancer. Traditionally, flavonoids from plants have been investigated against viral and cancerous diseases and have shown some promising results to combat these disorders. In the current study, representative flavonoid compounds from various subclasses are selected and used to disrupt the RNA-binding motif of EWS, which is required for EWS/FLI fusion. By blocking the RNA-binding motif of EWS, it might be possible to combat ES. Therefore, molecular docking experiments validated the binding interaction patterns and structural behaviors of screened flavonoid compounds within the active region of the Ewing sarcoma protein (EWS). Furthermore, pharmacogenomics analysis was used to investigate potential drug interactions with Ewing sarcoma-associated genes. Finally, molecular dynamics simulations were used to investigate the stability of the best selected docked complexes. Taken together, daidzein, kaempferol, and genistein exhibited a result comparable to ifosfamide in the proposed in silico study and can be further analyzed as possible candidate compounds in biological in vitro studies against ES.