Investigation on the mechanisms by which the herbal remedies induce anti-prostate cancer activity: uncovering the most practical natural compound.

Prostate cancer (PCa) is one of the most reported cancers among men worldwide. Targeting the essential proteins associated with PCa could be a promising method for cancer treatment. Traditional and herbal remedies (HRs) are the most practical approaches for PCa treatment. Here, the proteins and enzymes associated with PCa were determined based on the information obtained from the DisGeNET database. The proteins with a gene-disease association (GDA) score greater than 0.7 and the genes that have a disease specificity index (DSI) = 1 were selected as the target proteins. 28 HRs with anti-PCa activity as a traditional treatment for PCa were chosen as potential bioactive compounds. More than 500 compound-protein complexes were screened to find the top-ranked bioactives. The results were further evaluated using the molecular dynamics (MD) simulation and binding free energy calculations. The outcomes revealed that procyanidin B2 3,3'-di-O-gallate (B2G2), the most active ingredient of grape seed extract (GSE), can act as an agonist for PTEN. PTEN has a key role in suppressing PCa cells by applying phosphatase activity and inhibiting cell proliferation. B2G2 exhibited a considerable binding affinity to PTEN (11.643 kcal/mol). The MD results indicated that B2G2 could stabilize the key residues of the phosphatase domain of PTEN and increase its activity. Based on the obtained results, the active ingredient of GSE, B2G2, could play an agonist role and effectively increase the phosphatase activity of PTEN. The grape seed extract is a useful nutrition that can be used in men's diets to inhibit PCa in their bodies.Communicated by Ramaswamy H. Sarma.

Tracing the pathways and mechanisms involved in the anti-breast cancer activity of glycyrrhizin using bioinformatics tools and computational methods.

A complete investigation to understand the pathways that could be affected by glycyrrhizin (licorice), as anti-breast cancer (BC) agent, has not been performed to date. This study aims to investigate the pathways involved in the anti-cancer activity of glycyrrhizin against BC. For this purpose, the target genes of glycyrrhizin were obtained from the ChEMBL database. The BC-associated genes for three types of BC (breast carcinoma, malignant neoplasm of breast, and triple-negative breast neoplasms) were retrieved from DisGeNET. The target genes of glycyrrhizin and the BC-associated genes were compared, and the genes with disease specificity index (DSI) > 0.6 were selected for further evaluation using in silico methods. The protein-protein interaction (PPI) network was constructed, and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were analyzed. The potential complexes were further evaluated using molecular dynamics (MD) simulation. The results revealed that among 80 common genes, ten genes had DSI greater than 0.6, which included POLK, TACR2, MC3R, TBXAS1, HH1R, SLCO4A1, NPY2R, ADRA2C, ADRA1A, and SLCO2B1. The binding affinity of glycyrrhizin to the cognate proteins and binding characteristics were assessed using molecular docking and binding free energy calculations (MM/GBSA). POLK, TBXAS1, and ADRA1A showed the highest binding affinity with -8.9, -9.3, and -9.6 kcal/mol, respectively. The final targets had an association with BC at several stages of tumor growth. By affecting these targets, glycyrrhizin could influence and control BC efficiently. MD simulation suggested the pathways triggered by the complex glycyrrhizin-ADRA1A were more likely to happen.Communicated by Ramaswamy H. Sarma.

Adsorptive removal of phosphorus from aqueous solutions using natural and modified coal solid wastes.

An invaluable utilization approach for industrial wastes is to employ them as effective adsorbents for environmental pollutants. This study aimed to investigate the phosphorus (P) adsorption behavior of coal wastes and zeolite in three forms of pristine powder (CP and ZP), nanoparticles (CNP and ZNP), and Fe (III)-modified nanoparticles (MCNP and MZNP). The adsorbents were characterized using X-ray diffraction (XRD), Fourier transform infrared (FTIR), scanning electron microscopy (SEM), and energy-dispersive spectroscopy (EDS) analyses. The effects of pH, initial P concentration, and contact time were studied under batch mode. Results showed an optimum pH range of 2-6 for the P adsorption process. The pseudo-second-order kinetic model and the Langmuir isotherm described the P adsorption data well. The P adsorption capacity of the studied adsorbents was enhanced after modifications. However, the coal-based modified adsorbents represented higher P adsorption performances rather than the zeolite ones. The maximum P adsorption capacity (Qmax) values were obtained as 0.36, 3.23, and 30.48 mg g-1 for CP, CNP, and MCNP, and 0.80, 2.84, and 6.99 mg g-1 for ZP, ZNP, and MZNP, respectively. The surface complexation, ligand exchange, and electrostatic attraction processes were identified as the main P adsorption mechanisms by the studied adsorbents.

Modeling and affinity maturation of an anti-CD20 nanobody: a comprehensive in-silico investigation.

B-cell Non-Hodgkin lymphomas are the malignancies of lymphocytes. CD20 is a membrane protein, which is highly expressed on the cell surface of the B-cells in NHL. Treatments using monoclonal antibodies (mAbs) have resulted in failure in some cases. Nanobodies (NBs), single-domain antibodies with low molecular weights and a high specificity in antigen recognition, could be practical alternatives for traditional mAbs with superior characteristics. To design an optimized NB as a candidate CD20 inhibitor with raised binding affinity to CD20, the structure of anti-CD20 NB was optimized to selectively target CD20. The 3D structure of the NB was constructed based on the optimal templates (6C5W and 5JQH), and the key residues were determined by applying a molecular docking study. After identifying the key residues, some mutations were introduced using a rational protocol to improve the binding affinity of the NB to CD20. The rational mutations were conducted using the experimental design (Taguchi method). Six residues (Ser27, Thr28, Phe29, Ile31, Asp99, and Asn100) were selected as the key residues, and five residues were targeted for rational mutation (Trp, Phe, His, Asp, and Tyr). Based on the mutations suggested by the experimental design, two optimized NB structures were constructed. NB2 showed a remarkable binding affinity to CD20 in docking studies with a binding energy of - 853 kcal/mol. The optimized NB was further evaluated using molecular dynamics simulation. The results revealed that CDR1 (complementarity determining regions1) and CDR3 are essential loops for recognizing the antigen. NB2 could be considered as a potential inhibitor of CD20, though experimental evaluations are needed to confirm it.

Supercritical carbon dioxide utilization in drug delivery: Experimental study and modeling of paracetamol solubility.

In the present study, the solubility of paracetamol in supercritical CO2 is measured at temperatures between 311 and 358 K and pressures between 95 and 265 bar. It was shown that the solubility of paracetamol through a static solubility measurement method was between 0.3055 × 10-6 to 16.3582 × 10-6 based on mole fraction. The obtained experimental solubility data revealed the direct effect of pressure on the paracetamol experimental data, while the temperature has a dual effect of both increasing and decreasing effect considering the shifting point known as crossover pressure which was measured to be around 110 bar for paracetamol. Besides, two theoretical approaches were applied to predict the paracetamol experimental results. The experimental data were modeled with two various equations of state (EoSs) i.e. Peng-Robinson EoS and the simplified perturbed chain statistical associating fluid theory (sPC-SAFT) EoS, which their binary interaction parameters were optimized by using genetic algorithm. The obtained results demonstrated the sPC-SAFT EoS predicted the solubility of paracetamol with more accuracy (the average percent deviation was equal to 2.5215), especially at higher pressures. Furthermore, four well-known semi-empirical density-based correlations namely Mendez-Santiago and Teja (MST), Kumar-Johnston (KJ), Bartle et al., and Garlapati and Madras models were applied for modeling the solubility of paracetamol experimental data. According to self-consistency test, KJ model presented more accurate correlative capability with average percent deviation about 4.09%. As a final point, the rapid expansion of supercritical solution process was performed to reduce particle size of paracetamol and mean particle size of paracetamol was calculated based on EoSs and mathematical modeling.

The Impact of D614G Mutation of SARS-COV-2 on the Efficacy of Anti-viral Drugs: A Comparative Molecular Docking and Molecular Dynamics Study.

D614G is one of the most reported mutations in the spike protein of SARS-COV-2 that has altered some crucial characteristics of coronaviruses, such as rate of infection and binding affinities. The binding affinity of different antiviral drugs was evaluated using rigid molecular docking. The reliability of the docking results was evaluated with the induced-fit docking method, and a better understanding of the drug-protein interactions was performed using molecular dynamics simulation. The results show that the D614G variant could change the binding affinity of antiviral drugs and spike protein remarkably. Although Cytarabine showed an appropriate interaction with the wild spike protein, Ribavirin and PMEG diphosphate exhibited a significant binding affinity to the mutated spike protein. The parameters of the ADME/T analysis showed that these drugs are suitable for further in-vitro and in-vivo investigation. D614G alteration affected the binding affinity of the RBD and its receptor on the cell surface.

In silico SELEX screening and statistical analysis of newly designed 5mer peptide-aptamers as Bcl-xl inhibitors using the Taguchi method.

Drug development for cancer treatment is a complex process that requires special efforts. Targeting crucial proteins is the most essential purpose of drug design in cancers. Bcl-xl is an anti-apoptotic protein that binds to pro-apoptotic proteins and interrupts their signals. Pro-apoptotic Bcl-xl effectors are short BH3 sequences that form an alpha helix and bind to anti-apoptotic proteins to inhibit their activity. Computational systematic evolution of ligands by exponential enrichment (SELEX) is an exclusive approach for developing peptide aptamers as potential effectors. Here, the amino acids with a high tendency for constructing an alpha-helical structure were selected. Due to the enormous number of pentapeptides, Taguchi method was used to study a selected number of peptides. The binding affinity of the peptides to Bcl-xl was assessed using molecular docking, and after analysis of the obtained results, a final set of optimized peptides was arranged and constructed. For a better comparison, three chemical compounds with approved anti-Bcl-xl activity were selected for comparison with the top-ranked 5mer peptides. The optimized peptides showed considerable binding affinity to Bcl-xl. The molecular dynamics (MD) simulation indicated that the designed peptide (PO5) could create considerable interactions with the BH3 domain of Bcl-xl. The MM/GBSA calculations revealed that these interactions were even stronger than those created by chemical compounds. In silico SELEX is a novel approach to design and evaluate peptide-aptamers. The experimental design improves the SELEX process considerably. Finally, PO5 could be considered a potential inhibitor of Bcl-xl and a potential candidate for cancer treatment.

Recent advances in Bio-conjugated nanocarriers for crossing the Blood-Brain Barrier in (pre-)clinical studies with an emphasis on vesicles.

The brain is one of the most challenging organs for drug delivery. It is preserved by the blood-brain barrier (BBB), which controls the transport of components into and out of the brain. Although various approaches have been utilized to cross the BBB, the drug delivery into the brain is still less successful than the other human body parts. Comprehensive knowledge about the mechanisms and functionalities of brain delivery for different components is required to overcome this complex barrier. In this review, we have discussed the BBB structure, its characteristics, and the BBB's mechanisms for transporting components between the brain and blood. Furthermore, the barriers in front of drug delivery systems, the strategies which should be employed for overcoming these barriers, and different pathways for brain targeting are discussed. Then, the drug delivery systems utilized for crossing the BBB, especially nanocarriers and novel approaches, are considered. Finally, the bioconjugated vesicles as versatile nanocarriers that combine the bioconjugation approach and vesicles are reviewed. This review focuses on the biomolecules used in nano vesicular systems for overcoming the BBB. Various biomolecules such as amino acids, peptides, proteins, antibodies, and carbohydrates could be used for bioconjugation. Bioconjugated vesicles like liposomes and niosomes utilize the related receptor or transporter to cross the BBB. These drug delivery systems have shown enhanced drug loading into the brain, which is discussed.

3D-QSAR, molecular docking, molecular dynamics, and ADME/T analysis of marketed and newly designed flavonoids as inhibitors of Bcl-2 family proteins for targeting U-87 glioblastoma.

Glioblastoma is the most common and destructive brain tumor with increasing complexity. Flavonoids are versatile natural compounds with the approved anticancer activity, which could be considered as a potential treatment for glioblastoma. A quantitative structure-activity relationship (QSAR) can provide adequate data for understanding the role of flavonoids structure against glioblastoma. The IC50 of various flavonoids for the U-87 cell line was used to prepare an adequate three-dimensional QSAR (3D-QSAR) model. The validation of the model was carried out using some statistical parameters such as R2 and Q2 . Based on the QSAR model, the activities of other marketed and newly designed flavonoids were predicted. Molecular docking study and molecular dynamics (MD) simulation were conducted for better recognition of the interactions between the most active compounds and Bcl-2 family proteins. Moreover, an AMDE/T analysis was performed for the most active flavonoids. A reliable 3D-QSAR was performed with R2 and Q2 of 0.91 and 0.82. The molecular docking study revealed that BCL-XL has a higher binding affinity with the most active compounds, and the MD simulation showed that some residues of the BH3 domain, such as Phe97, Tyr101, Arg102, and Phe105 create remarkable hydrophobic interactions with the ligands. ADME/T analysis also showed the potential of the active compounds for further investigation. 3D-QSAR study is a beneficial method to evaluate and design anticancer compounds. Considering the results of the molecular docking study, MD simulation, and ADME/T analysis, the designed compound 54 could be considered as a potential treatment for glioblastoma.

Evaluation of potential anti-RNA-dependent RNA polymerase (RdRP) drugs against the newly emerged model of COVID-19 RdRP using computational methods.

INTRODUCTION: Despite all the efforts to treat COVID-19, no particular cure has been found for this virus. Since developing antiviral drugs is a time-consuming process, the most effective approach is to evaluate the approved and under investigation drugs using in silico methods. Among the different targets within the virus structure, as a vital component in the life cycle of coronaviruses, RNA-dependent RNA polymerase (RdRP) can be a critical target for antiviral drugs. The impact of the existence of RNA in the enzyme structure on the binding affinity of anti-RdRP drugs has not been investigated so far. METHODS: In this study, the potential anti-RdRP effects of a variety of drugs from two databases (Zinc database and DrugBank) were evaluated using molecular docking. For this purpose, the newly emerged model of COVID-19 (RdRP) post-translocated catalytic complex (PDB ID: 7BZF) that consists of RNA was chosen as the target. RESULTS: The results indicated that idarubicin (IDR), a member of the anthracycline antibiotic family, and fenoterol (FNT), a known beta-2 adrenergic agonist drug, tightly bind to the target enzyme and could be used as potential anti-RdRP inhibitors of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). These outcomes revealed that due to the ligand-protein interactions, the presence of RNA in this structure could remarkably affect the binding affinity of inhibitor compounds. CONCLUSION: In silico approaches, such as molecular docking, could effectively address the problem of finding appropriate treatment for COVID-19. Our results showed that IDR and FNT have a significant affinity to the RdRP of SARS-CoV-2; therefore, these drugs are remarkable inhibitors of coronaviruses.

Mathematical modeling and experimental study of VOC adsorption by Pistachio shell-based activated carbon.

The adsorption of benzene vapor, as a volatile organic component, from inert gas (N2) by activated carbon was studied experimentally in the isothermal fixed bed reactor at various operating conditions. The activated carbon used in this study had pistachio shell base with high surface area. To improve the adsorption capacity of VOC vapor, the activated carbon was chemically treated with H2SO4, HNO3, NaOH, and NH3 solutions. The saturated adsorption capacities of benzene on initial activated carbon and treated samples were measured and compared. The results showed that the activated carbon treated with nitric acid had higher adsorption capacity than others samples, 640 mg/g. In addition, a mathematical model for adsorption in a fixed bed reactor was proposed in this study. The model results had good agreement with experimental data. In order to demonstrate the effects of operating conditions on adsorption and breakthrough curve, the experimental tests and simulation runs were carried out at various gas flow rate, temperature, and benzene concentration. The results showed that with increase VOC concentration from 700 to 1000 ppm, the total time of adsorption was decreased from 25 to 21 h and breakthrough point appeared earlier.

Study of kinetic and fixed bed operation of removal of sulfate anions from an industrial wastewater by an anion exchange resin.

Sulfate anions represent very important wastewater pollutants, which appear in the effluents discharged from copper mines. In this study, for the first time, an attempt has been made on the removal of sulfate anions by an ion exchange resin. This work is focused on the removal of sulfate anions from the Sarcheshmeh copper complex (Kerman province, Southeast of Iran) wastewater by an anion exchange resin. Batch experiments of sulfate anions adsorption on Lewatit K6362 resin were carried out to determine the adsorption equilibrium data and the relation of adsorption isotherms. Isothermal data can be fitted with Freundlich adsorption isotherms better than Langmuir equation. The results show that maximum removal of sulfate anions take places in the resin dosage of 1000 mg/100ml and the adsorption of sulfate anions on the resin follows reversible first-order kinetics. The overall adsorption rate constants were compared for different initial concentrations. Finally, the effects of parameters such as the flow rate, bed height and inlet adsorbate concentration on the breakthrough curve in a fixed bed column were studied in detail.