Computational pharmacology profiling of borapetoside C against melanoma

Melanoma,also known as a 'black tumor', begins in the melanocytes when cells (that produce pigment) grows out of control. Immunological dysregulation, which raises the risk for multiple illnesses, including melanoma, may be influenced by stress tiggered through viral infection, long term effects of ultraviolet radiation, environmental pollutants etc. Borapetoside C is one of the phytoconstituents from Tinospora crispa, and its biological source has been reported for its antistress property. Network pharmacology and KEGG pathway analysis of borapetoside C-regulated proteins were conducted to identify the hub genes involved in melanoma development. Further, a molecular docking was performed between borapetoside C and targets involved in melanoma. Further, the top 3 complexes were selected based on the binding energy to conduct molecular dynamics simulations to evaluate the stability of ligand-protein complex followed by principal component analysis and dynamic cross-correlation matrix. In addition, borapetoside C was also screened for its pharmacokinetics and toxicity profile. Network Pharmacology studies and KEGG pathway analysis revealed 8 targets involved in melanoma. Molecular docking between borapetoside C and targets involved in melanoma identified 3 complexes with minimum binding i.e. borapetoside C- MAP2K1, MMP9, and EGFR. Further, molecular dynamics simulations showed a stable complex of borapetoside C with MMP9 and EGFR. The present study suggested that borapetoside C may target MMP9 and EGFR to possess an anti-melanoma property. This finding can be useful in developing a novel therapeutic agent against melanoma from a natural source.Communicated by Ramaswamy H. Sarma.

Guideline directed medical therapy induced nephrotoxicity in HFrEF patients; an insight to its mechanism

Guideline Directed Medical Therapy (GDMT) has been the standard pharmacotherapy for the treatment of Heart Failure patients with reduced Ejection Fraction (HFrEF) recommended by the European Society of Cardiology (ESC). However, patients on GDMT are likely to possess nephrotoxicity as an adverse effect. We utilized multiple system biology tools like ADVER-Pred, gene enrichment analysis, molecular docking, molecular dynamic simulations, and MMPBSA analysis to predict a possible molecular mechanism of how selected combinations of GDMT may cause nephrotoxicity. As per the ACC/AHA/ESC guidelines, we categorized the drugs as category 1 including β-blockers (BB), angiotensin receptor blockers (ARB), and sodium-glucose cotransporter-2 inhibitors (SGLT2I), category 2 includes BB's, SGLT2I, and angiotensin receptor-neprilysin inhibitors (ARNI), and category 3 includes BB's, SGLT2I, and angiotensin-converting enzyme (ACE) inhibitors. Enrichment analysis predicted category 2 drugs to possess the highest number of proteins to be involved in the development of nephrotoxicity i.e. 79.41%. The targets HBA1, CBR1, ATG5, and SLC6A3 were the top hub genes with an edge count of 7 followed by GPX1 with an edge count of 6. Molecular docking studies revealed candesartan-SLC6A3 to possess the highest binding affinity of -10.2 kcal/mol. In addition, simulation studies displayed empagliflozin-CBR1 to possess the highest stability followed by candesartan-ATG5. A combination of β-blockers, ARBs, and SGLT2I are predicted to likely possess nephrotoxicity which may be due to the modulation of HBA1, CBR1, ATG5, and GPX1. In conclusion, candesartan and empagliflozin are most likely to cause nephrotoxicity via the modulation of HBA1, CBR1, ATG5, and GPX1.Communicated by Ramaswamy H. Sarma.

System biology mediated assessment of molecular mechanism for sinapic acid against breast cancer: via network pharmacology and molecular dynamic simulation

Sinapic acid is a hydroxycinnamic acid widespread in the plant kingdom, known to be a potent anti-oxidant used for the treatment of cancer, infections, oxidative stress, and inflammation. However, the mode of action for its chemotherapeutic properties has yet not been unleashed. Hence, we aimed to identify potential targets to propose a possible molecular mechanism for sinapic acid against breast cancer. We utilized multiple system biology tools and databases like DisGeNET, DIGEP-Pred, Cytoscape, STRING, AutoDock 4.2, AutoDock vina, Schrodinger, and gromacs to predict a probable molecular mechanism for sinapic acid against breast cancer. Targets for the disease breast cancer, were identified via DisGeNET database which were further matched with proteins predicted to be modulated by sinapic acid. In addition, KEGG pathway analysis was used to identify pathways; a protein-pathway network was constructed via Cytoscape. Molecular docking was performed using three different algorithms followed by molecular dynamic simulations and MMPBSA analysis. Moreover, cluster analysis and gene ontology (GO) analysis were performed. A total of 6776 targets were identified for breast cancer; 95.38% of genes predicted to be modulated by sinapic acid were common with genes of breast cancer. The 'Pathways in cancer' was predicted to be modulated by most umber of proteins. Further, PRKCA, CASP8, and CTNNB1 were predicted to be the top 3 hub genes. In addition, molecular docking studies revealed CYP3A4, CYP1A1, and SIRT1 to be the lead proteins identified from AutoDock 4.2, AutoDock Vina, and Schrodinger suite Glide respectively. Molecular dynamic simulation and MMPBSA were performed for the complex of sinapic acid with above mentioned proteins which revealed a stable complex throughout simulation. The predictions revealed that the mechanism of sinapic acid in breast cancer may be due to regulation of multiple proteins like CTNNB1, PRKCA, CASP8, SIRT1, and cytochrome enzymes (CYP1A1 & CYP3A4); the majorly regulated pathway was predicted to be 'Pathways in cancer'. This indicates the rationale for sinapic acid to be used in the treatment of breast cancer. However, these are predictions and need to be validated and looked upon in-depth to confirm the exact mechanism of sinapic acid in the treatment of breast cancer; this is future scope as well as a drawback of the current study.

Virtual screening, pharmacokinetics & MD simulation study of active phytoconstituents of Ficus Carica Linn. against PPAR-γ in diabetes mellitus

F. carica is a small tree and commonly used as a traditional medicine against several disorders. Diabetes is currently treated with insulin and oral hypoglycemic medicines such as sulphonyl urea derivatives, bigunides, thiazolidinediones and alpha-glucosidase inhibitors. Peroxisome proliferator-activated receptor gamma (PPAR-γ) agonists were found to be very much beneficial in the management of diabetes by inhibiting hepatic gluconeogenesis. The aim of this study is to evaluate the bioactive phytoconstituents from Ficus carica Linn. against the target PPAR-γ agonist by in silico docking approach. We investigated 68 phytoconstituents as potential inhibitors of PPAR-γ agonists and the top 24 phytoconstituents were further selected for molecular docking studies. Drug ability, side effects, and ADMET analysis were determined by using MolSoft, toxtree freeware, and ADMET SAR web server, respectively. The phytoconstituents were docked with the target PPAR-γ (PDB ID: 4Y29, 1.98 Å) receptor. Quercetin-3-o-rutinoside possessed the highest G score -14.22 kcal/mol, followed by Angelicin with a G score of -13.56 kcal/mol. All the other phytoconstituents displayed good pharmacokinetic and toxicological parameters with values within the permissible limits. The ligand-protein interaction was calculated by molecular dynamic (MD) simulation study. Subsequently, the binding free energy of the Quercetin-3-o-rutinosideand Pioglitazone complex was calculated using MMPBSA analysis. The results indicated that some of the phytoconstituents from Ficus carica have potency as an anti-diabetic agents. So, these bioactive phytoconstituents like Quercetin-3-o-glucoside, 5-O-caffeoylquinic acids may act as a good agonist for PPAR-γ.Communicated by Ramaswamy H. Sarma.

The cytotoxic potential of sinapic acid on luminal A breast cancer; a computational and experimental pharmacology approach

Breast cancer is a highly concerning and prevalent disease that impacts a significant proportion of women worldwide, whose repeated exposure to therapies leads to resistance for drugs; making it alarming to identify novel chemotherapeutic agents. Sinapic acid is a phenolic acid that occurs naturally and is known to exhibit cytotoxic action in a variety of cancer cell types. In the present study, we utilized cell cytotoxicity assays to assess the cytotoxic potential of sinapic acid on various breast cancer subtypes. In addition, we assessed the cell migration rate, cell apoptosis, and cell cycle phases. Moreover, we utilized multiple system biology tools to predict the potential targets, and molecular docking was performed on the hub targets followed by molecular dynamic (MD) simulations. Cytotoxicity assay was performed on cell lines MCF7, T47D, MDA-MB-468, and SKBR3 at different time exposures of 24, 48, and 96 h. Our results revealed sinapic acid to be potent on MCF7 and T47D cell lines. The cell cycle analysis and cell apoptotic assays revealed sinapic acid to cause cell death by apoptosis majorly in the G0/G1 phase. Computational biology revealed KIF18B and VKORC1 to possess the highest binding affinity of -6.5 and -7.5 kcal/mol; displayed stable trajectories on MD run. The cytotoxicity of sinapic acid on luminal A cell lines may be due to the modulation of VKORC1 and KIF18B with major cell death in the G0/G1 phase. However, the mechanism has been proposed via in silico tools, which need further validation using wet lab protocols.Communicated by Ramaswamy H. Sarma.

Systems and in vitro pharmacology profiling of diosgenin against breast cancer

Aim: The purpose of this study was to establish a mode of action for diosgenin against breast cancer employing a range of system biology tools and to corroborate its results with experimental facts. Methodology: The diosgenin-regulated domains implicated in breast cancer were enriched in the Kyoto Encyclopedia of Genes and Genomes database to establish diosgenin-protein(s)-pathway(s) associations. Later, molecular docking and the lead complexes were considered for molecular dynamics simulations, MMPBSA, principal component, and dynamics cross-correlation matrix analysis using GROMACS v2021. Furthermore, survival analysis was carried out for the diosgenin-regulated proteins that were anticipated to be involved in breast cancer. For gene expression analyses, the top three targets with the highest binding affinity for diosgenin and tumor expression were examined. Furthermore, the effect of diosgenin on cell proliferation, cytotoxicity, and the partial Warburg effect was tested to validate the computational findings using functional outputs of the lead targets. Results: The protein-protein interaction had 57 edges, an average node degree of 5.43, and a p-value of 3.83e-14. Furthermore, enrichment analysis showed 36 KEGG pathways, 12 cellular components, 27 molecular functions, and 307 biological processes. In network analysis, three hub proteins were notably modulated: IGF1R, MDM2, and SRC, diosgenin with the highest binding affinity with IGF1R (binding energy -8.6 kcal/mol). Furthermore, during the 150 ns molecular dynamics (MD) projection run, diosgenin exhibited robust intermolecular interactions and had the least free binding energy with IGF1R (-35.143 kcal/mol) compared to MDM2 (-34.619 kcal/mol), and SRC (-17.944 kcal/mol). Diosgenin exhibited the highest cytotoxicity against MCF7 cell lines (IC₅₀ 12.05 ± 1.33) µg/ml. Furthermore, in H₂O₂-induced oxidative stress, the inhibitory constant (IC₅₀ 7.68 ± 0.51) µg/ml of diosgenin was lowest in MCF7 cell lines. However, the reversal of the Warburg effect by diosgenin seemed to be maximum in non-cancer Vero cell lines (EC₅₀ 15.27 ± 0.95) µg/ml compared to the rest. Furthermore, diosgenin inhibited cell proliferation in SKBR3 cell lines more though. Conclusion: The current study demonstrated that diosgenin impacts a series of signaling pathways, involved in the advancement of breast cancer, including FoxO, PI3K-Akt, p53, Ras, and MAPK signaling. Additionally, diosgenin established a persistent diosgenin-protein complex and had a significant binding affinity towards IGF1R, MDM2, and SRC. It is possible that this slowed down cell growth, countered the Warburg phenomenon, and showed the cytotoxicity towards breast cancer cells.

System biology-based investigation of Silymarin to trace hepatoprotective effect

Silymarin is used as a hepatoprotective agent since ancient times which could be via its potent anti-oxidant effect. However, the mode of silymarin for the hepatoprotective effect has not been established with the targets involved in hepatic cirrhosis. The present study investigated the multiple interactions of the flavonolignans from Silybum marianum with targets involved in hepatic cirrhosis using a series of system biology approaches. Chemo-informative tools and databases i.e. DIGEP-Pred and DisGeNET were used to predict the targets of flavonolignans and proteins involved in liver cirrhosis respectively. Further, STRING was used to enrich the protein-protein interaction for the flavonolignans-modulated targets. Similarly, molecular docking was performed using AutoDock Vina. Additionally, molecular dynamics simulation and MM-PBSA calculations were carried out for the lead-hit complexes by GROMACS. Thirteen flavonolignans were identified from S. marianum, in which silymonin exhibited the highest drug-likeness score i.e. 1.09. Similarly, CTNNB1 was found to be regulated by the 12 different flavonolignans and was majorly expressed within the compound(s)-protein(s)-pathway(s) network. Further, silymonin had the highest binding affinity; binding energy -9.2 kcal/mol with the CTNNB1 and formed very stable hydrogen bond interactions with Arg332, Ser336, Lys371, and Arg475 throughout 100 ns molecular dynamic production run. The binding free energy of CTNNB1-silymonin complex was found to be -15.83 ± 2.71 kcal/mol. The hepatoprotective property of S. marianum may be due to the presence of silymonin and silychristin; this could majorly modulate CTNNB1, HMOX1, and CASP8 in combination with other flavonolignans. Our findings further suggest designing the in-vitro and in-vivo studies to validate the interaction of flavonolignans with identified targets to strengthen present findings of S. marianum as a hepatoprotective..

Exploring the therapeutic mechanisms of Cassia glauca in diabetes mellitus through network pharmacology, molecular docking and molecular dynamics

Cassia glauca is reported as anti-diabetic medicinal plant and is also used as an ethnomedicine. However, its mode of action as an anti-diabetic agent has not been clearly elucidated. Hence, the present study investigated the probable mechanism of action of C. glauca to manage diabetes mellitus via network pharmacology and molecular docking and simulations studies. The reported bioactives from C. glauca were retrieved from an open-source database, i.e. ChEBI, and their targets were predicted using SwissTargetPrediction. The proteins involved in the pathogenesis of diabetes were identified from the therapeutic target database. The targets involved in diabetes were enriched in STRING, and the pathways involved in diabetes were identified concerning the KEGG. Cytoscape was used to construct the network among bioactives, proteins, and probably regulated pathways, which were analyzed based on edge count. Similarly, molecular docking was performed using the Glide module of the Schrodinger suite against majorly targeted proteins with their respective ligands. Additionally, the drug-likeness score and ADMET profile of the individual bioactives were predicted using MolSoft and admetSAR2.0 respectively. The stability of these complexes were further studied via molecular dynamics simulations and binding energy calculations. Twenty-three bio-actives were retrieved from the ChEBI database in which cassiarin B was predicted to modulate the highest number of proteins involved in diabetes mellitus. Similarly, GO analysis identified the PI3K-Akt signaling pathway to be primarily regulated by modulating the highest number of gene. Likewise, aldose reductase (AKR1B1) was majorly targeted via the bioactives of C. glauca. Similarly, docking study revealed methyl-3,5-di-O-caffeoylquinate (docking score −9.209) to possess the highest binding affinity with AKR1B1. Additionally, drug-likeness prediction identified cassiaoccidentalin B to possess the highest drug-likeness score, i.e. 0.84. The molecular dynamics simulations and the MMGBSA indicate high stability and greater binding energy for the methyl-3,5-di-O-caffeoylquinate (ΔG_bind = −40.33 ± 6.69 kcal mol⁻¹) with AKR1B1, thus complementing results from other experiments. The study identified cassiarin B, cassiaoccidentalin B, and cinnamtannin A2 as lead hits for the anti-diabetic activity of C. glauca. Further, the PI3K-Akt and AKR1B1 were traced as majorly modulated pathway and target, respectively.

Cassia glauca is reported for anti-diabetic action and is also used as an ethnomedicine.

Gene ontology enrichment analysis of PPAR-γ modulators from Cassia glauca in diabetes mellitus

BACKGROUND

PPAR-γ has an integrative role in the management of insulin resistance; ligands of this receptor have emerged as potent insulin sensitizers and may modulate proteins involved in the pathogenesis of diabetes mellitus. Hence the present study is aimed to identify PPAR-γ modulators from the plant Cassia glauca and predict the ontology enrichment analysis utilizing various in-silico tools.

METHODS

ChEBI database was used to mine the phytoconstituents present in the plant C. glauca, SwissTargetPrediction database was used to identify the targets, and scrutinizing of phytoconstituents modulating PPAR-γ was performed. Autodock4.0 was used to dock phytoconstituent ligands with the target PPAR-γ. Multiple open-source databases and in-silico tools were utilized to predict the drug-likeness characters and predict side effects of the phytoconstituents modulating PPAR-γ and STRING database was used to construct a network between the modulated genes.

RESULTS

Twenty-four phytoconstituents were identified from the plant Cassia glauca from which four were found to modulate PPAR-γ, sennoside was predicted to have the greatest drug-likeness score and a significantly less side effect whereas diphenyl sulfone was predicted to show hepatotoxicity with the greatest pharmacological activity of 0.815. [epicatechin-(4beta- > 8)]5-epicatechin showed the lowest binding affinity with target PPAR-γ i.e. -8.6 kcal/mol and possessing a positive drug-likeness score with no side effect data.

CONCLUSION

Bioctives were found free from probable side effects leaving out diphenyl sulfone having a prediction of hepatotoxicity, the anti-diabetic property of the plant may be due to the presence of [epicatechin-(4beta- > 8)]5-epicatechin which needs further validation by in-vitro and in-vivo protocols.

Identification of PTP1B regulators from Cymbopogon citratus and its enrichment analysis for diabetes mellitus

PTP1B is identified as the insulin signaling pathway downregulator; involved in pancreatic β-cell apoptosis. Further, it associates in regulating multiple pathways in diabetes mellitus; kindled us to identify the binding affinity of bioactives from Cymbopogon citratus by targeting PTP1B and identify the probably associated with it; further identifying the probable pathways involved in diabetes mellitus. In this regard, ChEBI database was used to retrieve bio-actives from C. citrates and 3D structures for the same were obtained from the PubChem database. The energy of bioactives was minimized and converted into ligand and the docking was carried using autodock 4.0 against PTP1B. Further, multiple characters of bio-actives like drug-likeness score, ADMET profile, probable adverse effects, and boiled egg model for bioavailability were also studied. Swertiajaponin was predicted for the highest drug-likeness score i.e. 0.26. However, swertiajaponin was predicted with the highest probable side effect of nephrotoxicity with pharmacological activity of 0.478. Similarly, swertiajaponin was predicted for the highest binding affinity with PTP1B with the binding energy of - 8.3 kcal/mol. Likewise, KEGG identified 80 pathways associated with PTP1B modulation in which 7 pathways were involved in diabetes mellitus in which FoxO signaling pathway was predicted to have the least false discovery rate by modulating 7 genes. Swertiajaponin could act as the potent inhibitor of PTP1B; scored highest druglikeness score but possessed minimum GIT absorptivity; further, PTP1B was identified to be linked with multiple pathways that are concerned with diabetes mellitus.