In-silico Results Research Articles

Molecular docking, QSAR, pharmacophore modeling, and dynamics studies of some chromone derivatives for the discovery of anti-breast cancer agents against hormone-dependent breast cancer

In search of new anti-breast cancer agents, the present study envisaged the design and synthesis of a series of benzopyran-chalcones. All the synthesized compounds were assayed for their in-vitro anticancer activity against ER + MCF-7 and triple-negative MDA-MB-231 breast cancer cell lines using SRB assay. The synthesized compounds were found active against ER + MCF-7 cell lines. Based on the in-vitro data, in-silico analysis was performed using hormone-dependent breast cancer targets such as hER-α and aromatase because the compounds showed activity against MCF-7 cells and none was active against MDA-MB-231. The in-silico results supported the in-vitro anticancer activity suggesting the affinity of compounds toward hormone-dependant breast cancer. Compounds 4A1 to 4A3 were found to be most cytotoxic to MCF-7 cells with IC50 values of 31.87, 22.95, and 20.34 μg/ml, respectively (Doxorubicin IC50: <10 μg/ml). In addition, they showed the interactions with the amino acid residues of a binding cavity of an hER-α. Furthermore, quantitative structure-activity relationship (QSAR) studies were performed to reveal the vital structural features required for anticancer activity against breast cancer. Molecular dynamic simulation studies of hER-α and 4A3 in comparison with the raloxifene complex ensure the appropriate refinement of compounds in the dynamic system. Additionally, a generated pharmacophore model explored the essential pharmacophoric features of the synthesized scaffolds with respect to clinically used drug molecules for optimal hormone-dependant anti-breast cancer activity. Communicated by Ramaswamy H. Sarma

Considerations about 3-glycidoxypropyltrimethoxysilane reactivity in function of the complexity of aqueous and plasma gel media

Organosilanes are versatile reactants for the chemical modification of silicas and organic polymers. The objective of this work is to evaluate the organic–inorganic reactivity of 3-glycidoxypropyltrimethoxysilane on aqueous media in the presence of a glutamic-like nucleophile residue and on protein gels. This is made by the combination of computational, spectroscopic, and thermal methods. Correlations are found between the in-silico results, calculated with Density Functional Theory, and sol-gel chemistry of 3-glycidoxypropyltrimethoxysilane in water. They explain the prevalence of esterification reaction with glutamic-like residue over hydrolysis of methoxy groups and epoxy ring of this molecule. This esterification contributes to the obtention of crosslinked gels when this organosilane is added to the blood plasma at concentrations higher than 75 mM at neutral pH. On the other hand, the prehydrolyzed molecule under acidic conditions is less reactive toward nucleophiles, and the silanol and diol groups act as a plasticizer on the gels. This in conjunction with non-extensive condensation in the protein media, shown by 29Si NMR spectroscopy, allows the identification of oligomers and silsesquioxanes on these samples. In summary, this kind of reactivity study contributes to overcoming the challenges of rationalizing organosilane chemical reactions, and effects in the gelation of proteins from complex sources.

Design and construction of multi epitope- peptide vaccine candidate for rabies virus.

Rabies virus is a zoonotic pathogen that causes lethal encephalitis with a case fatality rate of almost 100% in unvaccinated individuals. The currently available vaccines against rabies are composed of inactivated viral particles that only confer a short-term immune response. It is well-known that the entry of rabies virus into host cells is mediated by a trimeric glycoprotein presents on the surface of viral envelope. As the sole viral surface protein, this trimeric glycoprotein represents a promising molecular target to design new vaccines and neutralizing antibodies against rabies virus. Epitope mapping studies had identified several antigenic sites on the surface of trimeric pre-fusion glycoprotein of rabies virus. Therefore, it is of interest to screen the rabies virus glycoprotein by different web-based immuno-informatics tools to identify potential B-cells and T-cells linear epitopes. Here, we present a construct of peptide vaccine that consists of these predicted linear epitopes of rabies virus glycoprotein together with appropriate linkers and adjuvant. Various online prediction tools, molecular docking and dynamics simulation assume that the vaccine construct may be stable, safe and effective. However, validation of these in-silico results is necessary both in vitro and in vivo setting.

Exploring the repurposing potential of telmisartan drug in breast cancer: an in-silico and in-vitro approach.

Anticancer drug resistance is one of the biggest hurdles in the treatment of breast cancer. Drug repurposing is a viable option fordeveloping novel medical treatment strategies since this method is more cost-efficient and rapid. Antihypertensive medicines have recently been found to have pharmacological features that could be used to treat cancer, making them effective candidates for therapeutic repurposing. The goal of our research is to find a potent antihypertensive drug that can be repurposed as adjuvant therapy for breast cancer. In this study, virtual screening was performed using a set of Food and Drug Administration (FDA)-approved antihypertensive drugs as ligands with selected receptor proteins (EGFR, KRAS, P53, AGTR1, AGTR2, and ACE) assuming these proteins are regarded to have a significant role in hypertension as well as breast cancer. Further, our in-silico results were further confirmed by an in-vitro experiment (cytotoxicity assay). All the compounds (enalapril, atenolol, acebutolol, propranolol, amlodipine, verapamil, doxazosin, prazosin, hydralazine, irbesartan, telmisartan, candesartan, and aliskiren) showed remarkable affinity towards the target receptor proteins. However, maximum affinity was displayed by telmisartan. Cell-based cytotoxicity study of telmisartan in MCF7 (breast cancer cell line) confirmed the anticancer effect of telmisartan. IC50 of the drug was calculated to be 7.75 µM and at this concentration, remarkable morphological alterations were observed in the MCF7 cells confirming its cytotoxicity in breast cancer cells. Based on both in-silico and in-vitro studies, we can conclude that telmisartan appears to be a promising drug repurposing candidate for the therapeutic treatment of breast cancer.

Systematizing Microbial Bioplastic Production for Developing Sustainable Bioeconomy: Metabolic Nexus Modeling, Economic and Environmental Technologies Assessment.

The excessive usage of non-renewable resources to produce plastic commodities has incongruously influenced the environment's health. Especially in the times of COVID-19, the need for plastic-based health products has increased predominantly. Given the rise in global warming and greenhouse gas emissions, the lifecycle of plastic has been established to contribute to it significantly. Bioplastics such as polyhydroxy alkanoates, polylactic acid, etc. derived from renewable energy origin have been a magnificent alternative to conventional plastics and reconnoitered exclusively for combating the environmental footprint of petrochemical plastic. However, the economically reasonable and environmentally friendly procedure of microbial bioplastic production has been a hard nut to crack due to less scouted and inefficient process optimization and downstream processing methodologies. Thereby, meticulous employment of computational tools such as genome-scale metabolic modeling and flux balance analysis has been practiced in recent times to understand the effect of genomic and environmental perturbations on the phenotype of the microorganism. In-silico results not only aid us in determining the biorefinery abilities of the model microorganism but also curb our reliance on equipment, raw materials, and capital investment for optimizing the best conditions. Additionally, to accomplish sustainable large-scale production of microbial bioplastic in a circular bioeconomy, extraction, and refinement of bioplastic needs to be investigated extensively by practicing techno-economic analysis and life cycle assessment. This review put forth state-of-the-art know-how on the proficiency of these computational techniques in laying the foundation of an efficient bioplastic manufacturing blueprint, chiefly focusing on microbial polyhydroxy alkanoates (PHA) production and its efficacy in outplacing fossil based plastic products.

Using Reinforcement Learning to Simplify Mealtime Insulin Dosing for People with Type 1 Diabetes: In-Silico Experiments

People with type 1 diabetes (T1D) struggle to calculate the optimal insulin dose at mealtime, especially when under multiple daily injections (MDI) therapy. Effectively, they will not always perform rigorous and precise calculations; but occasionally, they might rely on intuition and previous experience. Reinforcement learning (RL) has shown outstanding results in outperforming humans on tasks requiring intuition and learning from experience. In this work, we propose an RL agent that recommends the optimal meal-accompanying insulin dose corresponding to a qualitative meal (QM) strategy that does not require precise carbohydrate counting (CC) (e.g., a usual meal at noon.). The agent is trained using the soft actor-critic approach and comprises long short-term memory (LSTM) neurons. For training, eighty virtual subjects (VS) of the FDA-accepted UVA/Padova T1D adult population were simulated using MDI therapy and QM strategy. For validation, the remaining twenty VS were examined in 26-week scenarios, including intra- and inter-day variabilities in glucose. In-silico results showed that the proposed RL approach outperforms a baseline run-to-run approach and can replace the standard CC approach. Specifically, after 26 weeks, the time-in-range (70 - 180mg/dL) and time-in-hypoglycemia (< 70mg/dL) were 73.1 ± 11.6% and 2.0 ± 1.8% using the RL-optimized QM strategy compared to 70.6± 14.8% and 1.5± 1.5% using CC. Such an approach can simplify diabetes treatment resulting in improved quality of life and glycemic outcomes.

Computational investigation of quinazoline derivatives as Keap1 inhibitors for Alzheimer's disease

The Keap1-Nrf2-ARE pathway is crucial in the antioxidant defense mechanism. The Keap1-Nrf2-ARE pathway has become an important target for the development of potent therapeutic agents for numerous diseases. Keap1-Nrf2 protein inhibitors are electrophilic species that covalently bind with the sulfhydryl group of a cysteine residue of Keap1, which results in modification in cysteine residue. In this in-silico research, we investigated the Keap1 inhibitory potential of 99 quinazoline derivatives by molecular docking investigation. The ligand molecules were extracted from the ZINC15 database in sdf format. The pdb format Keap1 protein with PDBID: 4ZY3 was downloaded from RCSB (https://www.rcsb.org/). Resveratrol was chosen as a standard drug molecule for molecular docking studies to compare the results of test ligands. The in-silico docking study was performed by using AutoDock Vina 1.5.7 software. Out of 99 quinazoline derivatives, 12 derivatives showed the best binding energy towards Keap1. Thus, the top 12 quinazoline derivatives were further screened for their ADME profiling, and bioactivity assessment. The molecular docking study revealed that trifluoromethyl substituted quinazoline derivatives S91 and S44 had the best binding energy of −9.1 kcal/mol and −9.0 kcal/mol respectively (better than reference drug Resveratrol, ΔG = −8.1 kcal/mol) at the KELCH binding pocket of Keap1. Compound S91 formed a conventional hydrogen bond with Val (418, 464,512), Ala (366, 466, 607), and Gly (419,511). All 12 derivatives were found to possess drug likeliness properties. For this result, it can be concluded that quinazoline derivatives can be developed as new potent Keap1-Nrf2 inhibitors. The in-vivo/in-vitro study is necessary to validate the in-silico results.

Hsa-miR-662 as a New Prognostic Biomarker in Patients with Breast Cancer; In-silico and Experimental Study

Background &amp; Objective: Breast cancer (BC) is a complex genetic disease that has an average annual incidence of one million people and is the second leading cause of death among women in the world. Therefore, a better understanding of tumor biology and the determination of biomarkers for early diagnosis of disease is essential. MicroRNAs and long non-coding RNAs are novel gene regulators that play key roles in tumor initiation and progression. The current study was performed to assess the biomarker potential. This study performed a combination of in-silico and experimental investigations of altered miRNAs in BC to assess the use of miRNAs as novel biomarkers for early detection and prognosis prediction of patients with BC.&#x0D; Materials &amp; Methods: We searched the miRNA expression patterns of BC from three expression arrays (GSE58606, GSE38867, and GSE40525) from the Gene Expression Omnibus (GEO) database to recognize differentially expressed miRNAs (DEMs) between BC tissues and normal adjacent samples. Using “Limma” package’s Quantile Normalization function and INMEX bioinformatic tool, hub DEMs were identified. MiRNAs targeted genes were found and visualized via the miRWalk and miRTargetLink databases and their Enrichment analysis was performed for identified genes. Due to more validation of DEGs, GSE70951, an independent expression array dataset, was analyzed. By merging DEMs and DEGs, miRNA-mRNA network was constructed. After elucidation of hub miRNAs, the capacity of detected miRNAs to differentiate BC from adjacent controls was estimated by Kaplan-Meier analysis. Furthermore, RT-qPCR on 100 BC samples and 100 adjacent non-tumor tissues was performed to validate the in-silico results.&#x0D; Results: According to our study, in BC samples, miR-662 was differentially downregulated in comparison with normal adjacent tissues.&#x0D; Conclusion: Altogether, miR-662 can be considered as a viable target for BC diagnostics and treatment.

Effect of Sauropus androgynus L. Merr. on dengue virus-2: An in vitro and in silico study

Ethnopharmacological relevanceSauropus androgynus L. Merr. (Euphorbiaceae) commonly known as “multigreen” and “multivitamin” is consumed as a vegetable and used in traditional medicine to relieve fever. Aim of the studyThis in vitro study is aimed to explore the activities of the lipophilic fraction of the leaves of S. androgynus (LFSA) against dengue (DENV), chikungunya (CHIKV) viruses and malaria (P. falciparum strain 3D7) parasite. Materials and methodsThe LFSA was analyzed by using GC-FID and GC-MS. The antiviral activity of LFSA was studied using the Vero CCL-81 cell line. The cytotoxicity assay was performed using 3-(4,5-dimethythiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT). Focus forming unit (FFU), cell-based immunofluorescence (IFA) assays, and quantitative RT-PCR, were used to determine and confirm antiviral activity against DENV and CHIKV. The antiparasitic activity of LFSA was carried out against P. falciparum strain 3D7 grown in fresh O+ human erythrocytes culture. ResultsTwelve compounds were identified in LFSA using GC/MS. The most abundant compound was squalene (36.9%), followed by vitamin E (12.5%) and linolenic acid (10.2%). Significant reduction in DENV titre was observed under pre- and post-infection treatment conditions at a concentration of 31.25 μg/ml, but no anti-malarial and anti-CHIKV activity was observed. The Autodock-Vina-based in-silico docking study revealed that β-sitosterol could form a strong interaction with the DENV E glycoprotein. ConclusionOur findings suggest that LFSA can inhibit DENV infection and might act as a potent prophylactic/therapeutic agent against DENV-2. In-silico results suggested that β-sitosterol may block the viral entry by inhibiting the fusion process.

Deep learning-based motion quantification from k-space for fast model-based magnetic resonance imaging motion correction.

Intra-scan rigid-body motion is a costly and ubiquitous problem in clinical magnetic resonance imaging (MRI) of thehead. State-of-the-art methods for retrospective motion correction in MRI are often computationally expensive or in the case of image-to-image deep learning (DL) based methods can be prone to undesired alterations of the image (hallucinations'). In this work we introduce a novel rigid-body motion correction method which combines the advantages of classical model-driven and data-consistency (DC) preserving approaches with a novel DL algorithm, to provide fast and robust retrospective motion correction. The proposed Motion Parameter Estimating Densenet (MoPED) retrospectively estimates subject head motion during MRI acquisitions using a DL network with DenseBlocks and multitask learning. It quantifies the 2D rigid in-plane motion parameters slice-wise for each echo train (ET) of a Cartesian T2-weighted 2D Turbo-Spin-Echo sequence. The network receives a center patch of the motion corrupted k-space as well as an additional motion-free low-resolution reference scan to provide the ground truth orientation. The supervised training utilizes motion simulations based on 28 acquisitions with subject-wise training, validation, and test data splits of 70%, 23%, and 7%. During inference, MoPED is embedded in an iterative DC-driven motion correction algorithm which alternatingly updates estimates of the motion parameters and motion-corrected low-resolution k-space data. The estimated motion parameters are then used to reconstruct the final motion correctedimage. The mean absolute/squared error and the Pearson correlation coefficient were used to analyze the motion parameter estimation quality on in-silico data in a quantitative evaluation. Structural similarity (SSIM), DC error and root mean squared error (RMSE) were used as metrics of image quality improvement. Furthermore, the generalization capability of the network was analyzed on two in-vivo motion volumes with 28 slices each and on one simulated T1-weightedvolume. The motion estimation achieves a Pearson correlation of 0.968 to the simulated ground-truth of the 2433 test data slices used. In-silico results indicate that MoPED decreases the time for the optimization by a factor of around 27 compared to a conventional method and is able to reduce the RMSE of the reconstructions and average DC error by more than a factor of two compared to uncorrected images. In-vivo experiments show a decrease in computation time by a factor of around 20, a RMSE decrease from 0.055 to 0.033 and an SSIM increase from 0.795 to 0.862. Furthermore, contrast independence is demonstrated as MoPED is also able to correct T1-weighted images in simulations without retraining. Due to the model-based correction, no hallucinations wereobserved. Incorporating DL in a model-based motion correction algorithm shows great benefit on the optimization and computation time. The k-space-based estimation also allows a data consistent correction and therefore avoids the risk of hallucinations of image-to-imageapproaches.

In silico guided designing of optimized benzochalcones derivatives as potent CYP1B1 inhibitors: An integrated in vitro and ONIOM study

Cytochrome P4501B1 (CYP1B1) is reported to be overexpressed in various malignancies including ovarian, lung, lymph, and breast cancers. The overexpression of this enzyme is accountable for the biotransformation-based inactivation of some anti-cancer drugs i.e. Docetaxel, Paclitaxel, and Cisplatin. To circumvent solutions to this issue, the current study reports some optimized derivatives of benzochalcone as selective CYP1B1 inhibitors. The optimized derivatives were screened using some structure-based drug-designing approaches including molecular docking and molecular dynamics. The implemented approaches revealed that all the designed molecules demonstrated not only essential interactions with key amino acid residues but also maintained stability within the active site of CYP1B1. Furthermore, to validate the in-silico results and develop a SAR, the designed molecules were subsequently synthesized and tested for their ability to selectively inhibit CYP1B1 over CYP1A1 using well established EROD assay. This assay results suggested that compounds 1(c), 1(d), and 1(e) are eightfold more selective CYP1B1 inhibitors over CYP1A1 with IC50 values ranging from 0.06 to 0.09 μM respectively. Among these, compound 1(d) manifested potent inhibitory activity i.e. IC50 of 0.06 μM with 24 folds selectivity over 1A1. To have a better insight into the binding pattern of 1(d) within CYP1B1 and precisely compute binding affinity for 1(d)-CYP1B1 complex, one of the advanced QM/MM approaches i.e. ONIOM has been implemented. Where 1(d)-CYP1B1 complex conferred comparable binding affinity in terms of ΔG (kcal/mol) with that of ANF-CYP1B1 complex. This research could provide a suitable starting point for the development of more potent multi-functional compounds with CYP1B1 inhibitory activity.

Position-based dynamics simulator of vessel deformations for path planning in robotic endovascular catheterization.

A major challenge during autonomous navigation in endovascular interventions is the complexity of operating in a deformable but constrained workspace with an instrument. Simulation of deformations for it can provide a cost-effective training platform for path planning. Aim of this study is to develop a realistic, auto-adaptive, and visually plausible simulator to predict vessels' global deformation induced by the robotic catheter's contact and cyclic heartbeat motion. Based on a Position-based Dynamics (PBD) approach for vessel modeling, Particle Swarm Optimization (PSO) algorithm is employed for an auto-adaptive calibration of PBD deformation parameters and of the vessels movement due to a heartbeat. In-vitro experiments were conducted and compared with in-silico results. The end-user evaluation results were reported through quantitative performance metrics and a 5-Point Likert Scale questionnaire. Compared with literature, this simulator has an error of 0.23±0.13% for deformation and 0.30±0.85mm for the aortic root displacement. In-vitro experiments show an error of 1.35±1.38mm for deformation prediction. The end-user evaluation results show that novices are more accustomed to using joystick controllers, and cardiologists are more satisfied with the visual authenticity. The real-time and accurate performance of the simulator make this framework suitable for creating a dynamic environment for autonomous navigation of robotic catheters.

Insight into the conformational analysis of 3-phenyl-N-(3-(trimethoxysilyl) propyl)prop-2-en-1-imine (PTP) as a biocidal candidate: In-silico and quantum computational approach

A newly synthesized compound, 3-phenyl-N-(3-(trimethoxysilyl) propyl)prop-2-en-1-imine (PTP) is analyzed in detail for structural clarification by mass spectroscopy & FT-IR along with theoretical quantum chemical computations. The conformers of the structure were optimized through DFT to understand the stable conformer (1Z2E) of the structure and their interaction with the protein. The docking studies of the synthesized compound were carried out to determine the interaction with the receptor protein Dihydropteroate synthase protein (PDB ID: 3TZF) which showed very good interactions with various amino acid residues like HIS203, LEU194, LEU40, and so on. Molecular dynamic simulation of the title compound was made to understand the stability of the docked complex. The MM-GBSA calculations verify that the stable conformer (1Z2E) efficiently binds to selected protein to form a stable protein–ligand complex. The in-silico results revealed that in the future PTP can be used for further development, to treat the antibacterial infection.

Therapeutic potential of reserpine in metabolic syndrome: An evidence based study

Reserpine is as old as the scientific diagnosis of hypertension. For many years’ clinicians have used it for the treatment of high blood pressure, but with the passage of time and introduction of new anti-hypertensive drugs, the usage of reserpine has gone down drastically most probably due to poorly understood mechanism of action and multiple misleading adverse effects precisely due to high dosing of reserpine. With an aim to elucidate the specific mechanism of action, we screened reserpine against various targets associated with regulation of blood pressure. Surprisingly reserpine showed remarkable inhibitory potential for soluble epoxide hydrolase an enzyme responsible for pathophysiology of not only hypertension but also hyperlipidemia, diabetes and inflammation collectively known as metabolic syndrome. The in-silico, in-vitro and in-vivo results showed that reserpine has the ability to treat metabolic syndrome effectively by inhibiting soluble epoxide hydrolase.

Shape matters: Lagrangian tracking of complex nonspherical microparticles in superellipsoidal approximation

Humans are constantly exposed to airborne pollutants such as pollen, exhaust residues, microplastics, fabrics, aerosols, or, as recently, ash particles from volcanic eruptions, which are rarely perfectly spherical. In order to reduce the impact of harmful particles or, on the contrary, to improve the targeted delivery of drugs, understanding the motion of complex shaped particles in fluid flows is of key interest. Common models mainly use shape factors to account for deviations from spherical shape, but these often fail to accurately predict particle motion. We advocate a more accurate modeling of complex particles by a superellipsoidal shape approximation, which allows covering a wide range of particle geometries. Superellipsoidal particle shapes allow for a novel approximation of translational and rotational resistance tensors, derived based on data from dedicated DNS computations. Our surrogate approach for convex bodies (ϵ1,ϵ2∈(0,2)), implemented in OpenFOAM® based on Lagrangian particle tracking, is first validated based on experimental and in-silico results from the literature, followed by a comparison of the effects of non-sphericity for some well known fluid flow cases, such as a lid-driven cavity (validated for Re=470, St=0.0023), pipe flow (validated for Re=137, St=0.01) and a simplified bifurcation (validated for Re=500, St=0.5×10−2−0.5). We show that using shape factor assisted spherical or ellipsoidal approximations of the particles leads to insufficient accuracy of computation of the particle’s trajectory, whereas the newly derived superellipsoidal drag and torque models proved to provide a superior accuracy of the Lagrangian particle tracking over simplified non-spherical particle approximations.

In Silico Design, Synthesis and Evaluation of Hydroxyxanthone Derivatives as Potential Anti-diabetic Agents Targeting α-glucosidase

Background: Glucosidase is a catalytic enzyme that catalyzes, specifically hydrolyses, the carbohydrates to free glucose units in blood in the last step of carbohydrate metabolism. So far, many compounds with α-glucosidase inhibitory activity for example, acarbose, voglibose etc., have been accounted and commercialized for diabetes therapy. However, Xanthones are recognized as efficient glucosidase inhibitors because of their planar structure and thereby opens the door for the researchers to utilize the same for designing and developing potent and novel hybrid xanthones for anti-diabetic therapy. Objectives: The current study aimed to determine and evaluate the anti-diabetic potential of different synthetic hydroxylxanthone derivatives using Nicotinamide and Streptozotocin(60mg/kg i.p.) induced diabetic rats. Methods: The partially purified synthetic hydroxyxanthone derivatives namely A1,A2, A3, A4, and A5 were administered to diabetic rats with a dose of 150mg/kg, per oral(p.o.) and the effect of the fraction on blood glucose level was studied upto 21 days. Further, the synthetic compounds were subjected to spectral analysis for their characterization. Results: The in-silico molecular docking results indicated that the compound A3 has shown the best binding energy score. Also, the in-vivo anti-diabetic potential of the synthetic hydroxyxanthone derivatives have revealed that the compounds A3 and A2 were significantly effective in controlling the blood glucose level when compared to the standard drug miglitol. In addition, compounds A3 and A2 were found to be effective in restoring the enzymes of liver and lipid profile in Streptozotocin-induced Wistar rat models. Conclusion: With an objective to investigate the compounds for predicting biological activity, it was found that the hydroxyxanthonepossesses a safety margin for toxicity and acts as a lead towards the development of potential α-glucosidase inhibitors. These compounds show excellent correlation between docking results, synthetic data and in-vivo anti-diabetic activity. However, further modifications can be done to enhance the potency, binding affinity profile and minimize toxicity.

An integrative bioinformatics investigation and experimental validation of critically involved genes in high-grade gliomas

BackgroundLack of knowledge around underlying mechanisms of gliomas mandates intense research efforts to improve the disease outcomes. Identification of high-grade gliomas pathogenesis which is known for poor prognosis and low survival is of particular importance. Distinguishing the differentially expressed genes is one of the core approaches to clarify the causative factors.MethodsMicroarray datasets of the treatment-naïve gliomas were provided from the Gene Expression Omnibus considering the similar platform and batch effect removal. Interacting recovery of the top differentially expressed genes was performed on the STRING and Cytoscape platforms. Kaplan–Meier analysis was piloted using RNA sequencing data and the survival rate of glioma patients was checked considering selected genes. To validate the bioinformatics results, the gene expression was elucidated by real-time RT-qPCR in a series of low and high-grade fresh tumor samples.ResultsWe identified 323 up-regulated and 253 down-regulated genes. The top 20 network analysis indicated that PTX3, TIMP1, CHI3L1, LTF and IGFBP3 comprise a crucial role in gliomas progression. The survival was inversely linked to the levels of all selected genes. Further analysis of RNA sequencing data indicated a significant increase in all five genes in high-grade tumors. Among them, PTX3, TIMP1 and LTF did not show any change in low-grade versus controls. Real-time RT-qPCR confirmed the in-silico results and revealed significantly higher expression of selected genes in high-grade samples compared to low-grade.ConclusionsOur results highlighted the role of PTX3 and TIMP1 which were previously considered in glioma tumorigenesis as well as LTF as a new potential biomarker.

Xanthine oxidase inhibitory study of eight structurally diverse phenolic compounds.

This project was designed to explore the xanthine oxidase (XO) inhibitory mechanism of eight structurally diverse phenolic compounds [quercetin: C1, quercetin-3-rhamnoside: C2, 4, 5-O-dicaffeoylquinic acid: C3, 3, 5-O-dicaffeoylquinic acid: C4, 3, 4-O-di-caffeoylquinic acid: C5, 4-O-caffeoylquinic acid (C6), 3-O-caffeoylquinic acid: C7, and caffeic acid: C8]. For this purpose, in-vitro and different computational methods were applied to determine the xanthine oxidase (XO) inhibitory potential of eight structurally diverse phenolic compounds. The results revealed that phenolic compounds (C1–C8) possess strong to weak XO inhibitory activity. These results were further confirmed by atomic force microscopy (AFM) and 1H NMR analysis. Furthermore, computational study results revealed that phenolic compounds (C1–C8) bind with the surrounding amino acids of XO at the molybdenum (MO) site. These in-vitro and in-silico results divulge that phenolic compounds have a strong potential to lower uric acid levels via interacting with the XO enzyme and can be used to combat hyperuricemia.

Integrating in silico and in vivo approach for investigating the role of polyherbal oil in prevention and treatment of COVID-19 infection

Currently, there are no FDA approved antiviral drugs available to treat COVID-19 patients. Also, due to emergence of new SARS-CoV-2 variants, the protective efficacy of vaccines could be reduced, hence it is urgent to have alternative treatments for combating the SARS-CoV-2 infection. Since, there is a long-standing history of herbal medicine in the treatment of respiratory diseases. In the present study, we investigated two polyherbal oil blend viz. Sudarshan AV and Elixir AV (SAV and EAV) in inhibiting SARS-COV-2. From GC-MS analysis of polyherbal oils (SAV and EAV) a total of 11 active compounds were selected, on the basis of their abundance and activity. Further, from the molecular docking studies, we found an inhibitory effect of these compounds on viral envelope and membrane, spike proteins whilst an agonistic effect with human host receptor angiotensin-converting enzyme 2 (ACE2) implicating the crucial role of the individual compound in resistance of SARS-CoV-2.Since, the in-silico results suggest that polyherbal oil (SAV and EAV) contributes in preventing the entry of SARS-CoV-2 into the human body, we further investigated the efficacy of polyherbal formulated essential oil (FEO; SAV & EAV) in prevention and treatment of COVID-19 in hamster model. The male golden Syrian hamsters (n = 23) were divided into 5 groups i.e., Group 1: Control (n = 3); Group 2: Infected (n = 5); Group 3: Infected + Remdesivir (n = 5); Group 4: Infected + FEO (n = 5) and Group 5: Prophylactic FEO + Infected (n = 5). In both treatment and prophylactic groups, the FEO's significantly reduced the lung injury investigated histo-pathologically and viral load expression measured by real time PCR in comparison to infected hamsters. Furthermore, cytokines expression analysis clearly highlighted the efficacy of FEO's due to its anti-inflammatory activity and overall protection in treatment groups. In conclusion, the FEO (SAV & EAV) seem to be potent in both prevention and treatment of COVID-19 and related lung injury.

Pyrazolo-imidazolidinones: Synthesis, antimicrobial assessment and molecular modelling studies by molecular mechanic and quantum mechanic approach

Antimicrobial resistance (AMR) and multidrug resistance (MDR) have become global therapeutic barriers and due to this, there is increasing global mortality. To overcome antimicrobial resistance, we developed a novel set of fifteen pyrazole-imidazolidinone hybrids. The synthesized compounds were characterized, and structures confirmed by spectral techniques like IR, NMR spectra and Mass data. Compounds (4a-o) were evaluated for their in vitro antimicrobial activity against Gram-positive, Gram-negative bacterial strains, as well as fungal strains by using the Mueller Hinton Broth dilution method. The results demonstrated that the compounds 4f with MIC (Minimum Inhibitory Concentration) 12.5 μg/mL against A. clavatus and 4l with MIC 12.5 μg/mL against P. aeruginosa were the most active compared to the standard drugs. The biological activity revealed that the electron-withdrawing group (4l) is responsible for the antibacterial activity whereas the electron-donating group (4b, 4d, 4e, 4f, and 4g) is accountable for the antifungal activity. The in-silico results prove the affinity of 4f, and 4l for bacterial and fungal targets. Compounds, 4a, 4b, and 4c have residues in their cavities that are comparable and have excellent affinity for such bacterial target. Further, 100 ns molecular dynamics simulations were used to evaluate the promising compound 4f interactions and stability on the fungal target 14α–demethylase enzyme. Optimization parameters, MESP (Molecular Electrostatic Potential) analysis, HUMO (Highest Occupied Molecular Orbital), and LUMO (Lowest Unoccupied Molecular Orbital) quantum parameters of the most active compound 4f were also calculated by using DFT/B3LYP theory and the 6–31 G ** base set, and the results were correlated with the anti-microbial activity.