Virtual screening, ADME prediction, drug-likeness, and molecular docking analysis of Fagonia indica chemical constituents against antidiabetic targets.

Fagonia indica from Zygophyllaceae family is a medicinal specie with significant antidiabetic potential. The present study aimed to investigate the in vitro antidiabetic activity of Fagonia indica crude extract followed by an in silico screening of its phytoconstituents. For this purpose, crude extract of Fagonia indica was prepared and divided in three different parts, i.e., n-hexane, ethyl acetate, and methanolic fraction. Based on in vitro outcomes, the phytochemical substances of Fagonia indica were virtually screened through a literature survey and a screening library of compounds (1-13) was prepared. The clinical potential of these novel drug candidates was assessed by applying an ADME screening profile. Findings of SwissADME indicators (Absorption, Distribution, Metabolism, and Excretion) for the compounds (1-13) presented relatively optimal physicochemical characteristics, drug-likeness, and medicinal chemistry. The antidiabetic action of these leading drug candidates was optimized through molecular docking analysis against 3 different human pancreatic α-amylase macromolecular targets with (PDB ID 1B2Y), (PDB ID 3BAJ), and (PDB ID: 3OLI) by applying Virtual Docker (Molegro MVD). Metformin was taken as a reference standard for the sake of comparison. In vitro antidiabetic evaluation gave good results with promising α-amylase inhibitory action in the form of IC50 values, as for n-hexane extract = 206.3 µM, ethyl acetate = 41.64 µM, and methanolic extract = 9.61 µM. According to in silico outcomes, all 13 phytoconstituents possess the best binding affinity with successful MolDock scores ranging from - 97.2003 to - 65.6877 kcal/mol and show a great number of binding interactions than native drug metformin. Therefore, the current work concluded that the diabetic inhibition prospective of extract and the compounds of Fagonia indica may contribute to being investigated as a new class of antidiabetic drug or drug-like candidate for further studies.

Exploring Type II Diabetes Inhibitors from Genus Daphne Plant-species: An Integrated Computational Study.

BACKGROUND: Plant species of the genus Daphne clasps a historical background with a potential source of bioactive phytochemicals such as flavonoids and daphnodorins. These compounds manifest a significant chemotaxonomic value in drug discovery. Their flair comprehensive pharmacological, phytochemical, biological, catalytic, and clinical utilities make them exclusively unique. This study was conducted to investigate the optimization and structure-based virtual screening of these peculiar analogs. The majority of the active constituents of medicines are obtained from natural products. Previously, before the invention of virtual screening methods or techniques, almost 80% of drugs were obtained from natural resources. Comparing reported data to drug discovery from 1981 to 2007 signifies that half of the FDA-approved drugs are obtained from natural resources. It has been reported that structures of natural products that have particularities of structural diversity, biochemical specification, and molecular properties make them suitable products for drug discovery. These products basically have unique chiral centers which increase their structural complexity than the synthesized drugs. METHOD: This work aimed to probe the use of daphnodorins analogs for the first time as antidiabetic inhibitors based on significant features and to determine the potential of daphnodorin analogs as antidiabetic inhibitors through computational analysis and structure-based virtual screening. A dataset of 38 compounds was selected from different databases, including PubChem and ZINC, for computational analysis, and optimized compounds were docked against various co-crystallized structures of inhibitors, antagonists, and receptors which were downloaded from PDB by using AutoDock Vina (by employing Broyden-Fletcher-Goldfarb-Shanno method), Discovery studio visualizer 2020, PYMOL (Schrodinger). Docking results were further validated by Molecular dynamic simulation and MM-GBSA calculation. Quantitative structure-activity relationship (QSAR) was reported by using Gaussian 09W by intimating Density Functional Theory (DFT). Using this combination of multi-approach computational strategy, 14 compounds were selected as potential exclusive lead compounds, which were analyzed through ADMET studies to pin down their druglike properties and toxicity. RESULT: At significant phases of drug design approaches regular use of molecular docking has helped to promote the separation of important representatives from 38 pharmaceutically active compounds by setting a threshold docking score of -9.0 kcal/mol which was used for their exposition. Subsequently, by employing a threshold it was recognized that 14 compounds proclaimed this threshold for antidiabetic activity. Further, molecular dynamic simulation, MM-GBSA, ADMET, and DFT results screened out daphnegiralin B4 (36) as a potential lead compound for developing antidiabetic agents. CONCLUSION: Our analysis took us to the conclusion that daphnegiralin B4 (36) among all ligands comes out to be a lead compound having drug-like properties among 38 ligands being non-carcinogenic and non-cytotoxic which would benefit the medical community by providing significant drugs against diabetes. Pragmatic laboratory investigations identified a new precursor to open new doors for new drug discovery.

A virtual insight into mushroom secondary metabolites: 3D-QSAR, docking, pharmacophore-based analysis and molecular modeling to analyze their anti-breast cancer potential.

Breast cancer is a major issue of investigation in drug discovery due to its rising frequency and global dominance. Plants are significant natural sources for the development of novel medications and therapies. Medicinal mushrooms have many biological response modifiers and are used for the treatment of many physical illnesses. In this research, a database of 89 macro-molecules with anti-breast cancer activity, which were previously isolated from the mushrooms in literature, has been selected for the three-dimensional quantitative structure-activity relationships (3D-QSAR) studies. The 3D-QSAR model was necessarily used in Pharmacopoeia virtual evaluation of the database to develop novel MCF-7 inhibitors. With the known potential targets of breast cancer, the docking studies were achieved. Using molecular dynamics simulations, the targets' stability with the best-chosen natural product molecule was found. Furthermore, the absorption, distribution, metabolism, excretion, and toxicity of three compounds, resulting after the docking study, were predicted. The compound C1 (Pseudonocardian A) showed the features of effective compounds because it has bioavailability from different coral species and is toxicity-free for the prevention of many dermatological illnesses. C1 is chemically active and possesses charge transfer inside the monomer, as seen by the band gaps of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) electrons. The reactivity descriptors ionization potential, electron affinity, chemical potential (µ), hardness (η), softness (S), electronegativity (χ), and electrophilicity index (ω) have been estimated using the energies of frontier molecular orbitals (HOMO-LUMO). Additionally, molecular electrostatic potential maps were created to show that the C1 is reactive.Communicated by Ramaswamy H. Sarma.

The selected compounds from the mushroom were evaluated as potential breast cancer MCF-7 cell line inhibitor.Ligand-based 3D-QSAR study to analyze the structurally diverse compounds with experimentally reported IC₅₀.Pharmacophore-based virtual screening of compounds.Molecular docking analysis pointed out the vital interaction of the hit with the protein's amino acids.Absorption, distribution, metabolism, and excretion (ADME) and toxicity properties of the lead compounds were examined.

A discovery of potent kaempferol derivatives as multi-target medicines against diabetes as well as bacterial infections: an in silico approach.

Flavonoids demonstrate beneficial effects on human health because flavonoids contain important biological properties. Kaempferol is a flavonol, type of flavonoid found in eatable plants and in plants usually employed in ancient drugs (Moringa oleifera, Tilia spp., fern genus spp. and gingko etc.). Some medicinal studies have shown that the use of foods full of kaempferol decreases the risk of many (cancer, vascular) diseases. All the data of 50 kaempferol derivatives were collected from PubChem database. Through Schrödinger software, 3D-QSAR study was performed for 50 compounds by using method of field base. Conformer of kaempferol derivatives was docked against anti-diabetic, anti-microbial co-crystal structures and protein. To monitor the best anti-diabetic and antibacterial agent, particular kaempferol derivatives were downloaded from PubChem database. Virtual screening by molecular docking provided four lead compounds with four different proteins. These hit compounds were found to be potent inhibitor for diabetic enzymes alpha-amylase and DPP IV and had the potential to suppress DNA gyrase and dihydrofolate reductase synthesis. Molecular dynamic simulation of docked complexes evaluates the value of root mean square fluctuation by iMOD server. Kaempferol 3-O-alpha-L-(2, 3-di-Z-p-coumaroyl) rhamnoside (42) compound used as anti-diabetic and kaempferol 3-O-gentiobioside (3) as antibacterial with good results can be used for drug discovery.Communicated by Ramaswamy H. Sarma.

Combined Experimental and Theoretical Insights: Spectroscopic and Molecular Investigation of Polyphenols from Fagonia indica via DFT, UV-vis, and FT-IR Approaches.

This review deals with computational study of polyphenolic compounds of medicinal importance and interest for drug development. Herein, four polyphenolic compounds comprising catechol (1), caffeic acid (II), gallic acid (III), and pyrogallol (IV) have been isolated from a medicinal specie, Fagonia indica, by applying silica gel column chromatography. These compounds were identified by using gas chromatography-mass spectrometry (GC-MS) analysis and confirmed by geometric computational analysis. According to computational results, caffeic acid has shown the highest biological activation due to higher chemical softness, electronegativity (χ (eV) = -648.644), and electrostatic potential value (-8.424 × 10-2 to +8.424 × 10-2), while smaller values of chemical potential (-0.269), ELUMO (-0.080), and energy gap (ΔE = 0.149). The Mulliken atomic charges were calculated by using DFT/B3LYP with basis set 6-311G for the determination of active sites. The oxygen atom of catechol showed highest nucleophilic characteristic with a more negative charge (08 = -0.695), and pyrogallol indicated a strong electrophilic center at C14 = 0.415 with a higher positive charge. Moreover, UV-visible absorption spectra and a detailed study of vibrational frequencies for all phenolic compounds by employing the DFT approach with 3-21G, 6-31G, and 6-311G basis sets at the ground-state level showed the great agreement with experimental results. ANOVA has been applied to validate the theoretical data. Results suggest that compounds I-IV are suitable in diverse fields.

Exploring Citrus sinensis Phytochemicals as Potential Inhibitors for Breast Cancer Genes BRCA1 and BRCA2 Using Pharmacophore Modeling, Molecular Docking, MD Simulations, and DFT Analysis.

BACKGROUND: Structure-activity relationship (SAR) is considered to be an effective in silico approach when discovering potential antagonists for breast cancer due to gene mutation. Major challenges are faced by conventional SAR in predicting novel antagonists due to the discovery of diverse antagonistic compounds. Methodologyand Results: In predicting breast cancer antagonists, a multistep screening of phytochemicals isolated from the seeds of the Citrus sinensis plant was applied using feasible complementary methodologies. A three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed through the Flare project, in which conformational analysis, pharmacophore generation, and compound alignment were done. Ten hit compounds were obtained through the development of the 3D-QSAR model. For exploring the mechanism of action of active compounds against cocrystal inhibitors, molecular docking analysis was done through Molegro software (MVD) to identify lead compounds. Three new proteins, namely, 1T15, 3EU7, and 1T29, displayed the best Moldock scores. The quality of the docking study was assessed by a molecular dynamics simulation. Based on binding affinities to the receptor in the docking studies, three lead compounds (stigmasterol P8, epoxybergamottin P28, and nobiletin P29) were obtained, and they passed through absorption, distribution, metabolism, and excretion (ADME) studies via the SwissADME online service, which proved that P28 and P29 were the most active allosteric inhibitors with the lowest toxicity level against breast cancer. Then, density functional theory (DFT) studies were performed to measure the active compound's reactivity, hardness, and softness with the help of Gaussian 09 software. CONCLUSIONS: This multistep screening of phytochemicals revealed high-reliability antagonists of breast cancer by 3D-QSAR using flare, docking analysis, and DFT studies. The present study helps in providing a proper guideline for the development of novel inhibitors of BRCA1 and BRCA2.

Integration of virtual screening of phytoecdysteroids as androgen receptor inhibitors by 3D-QSAR Model, CoMFA, molecular docking and ADMET analysis: An extensive and interactive machine learning.

Ecdysteroids, a class of naturally isolated polyhydroxylated sterols, stands at a very good place in the pharmaceutical industry from their medicinal point of views like anti-inflammatory, neuroprotective, anti-microbial, anti-diabetic, antioxidant, and anti-tumor effects. Due to their excellent antioxidant and anti-microbial potential, ecdysteroids have extensive use in skin products, especially derma creams. To monitor the best anti-acne phytoecdysteroids, here made use of different computational approaches, by using the rapid, easy, cost-effective and high throughput method to screen and identify ecdysteroids as androgen receptor inhibitors. 3D-QSAR study was carried out on a dataset of ecdysteroids by using comparative molecular field analysis (CoMFA) to determine the factors responsible for the activity of compounds. Statistically a cross-validated (q2) 0.1457 and regression coefficient (r2) 0.9713 indicated the best model. Contour map results showed the influence of steric effect to enhance activity. A molecular docking analysis was done to further find out the binding sites and their anti-acne potential against three crystal structured macromolecules (PDB ID: 2REQ, 2BAC, 4EM0). Docking results were further evaluated by prime MM-GBSA analysis and findings confirmed the accuracy. Toxicity by ADMET assessment was carried out and M2 was found as lead druglike with best anti-acne activity against Propionium acnes GehA lipase bacteria after passing all filters. This research study is novel because it is representing first effort to explore ecdysteroids class for their high therapeutic output as androgen receptor inhibitor by using computational tools and expectedly led to novel scaffold for androgen receptor inhibitor. This is a novel and new approach to investigate the ecdysteroids for first time for their practical applications.

Exploration of phenolic acid derivatives as inhibitors of SARS-CoV-2 main protease and receptor binding domain: potential candidates for anti-SARS-CoV-2 therapy.

Severe acute respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) is the etiological virus of Coronavirus Disease 2019 (COVID-19) which has been a public health concern due to its high morbidity and high mortality. Hence, the search for drugs that incapacitate the virus via inhibition of vital proteins in its life cycle is ongoing due to the paucity of drugs in clinical use against the virus. Consequently, this study was aimed at evaluating the potentials of natural phenolics against the Main protease (Mpro) and the receptor binding domain (RBD) using molecular modeling techniques including molecular docking, molecular dynamics (MD) simulation, and density functional theory (DFT) calculations. To this end, thirty-five naturally occurring phenolics were identified and subjected to molecular docking simulation against the proteins. The results showed the compounds including rosmarinic acid, cynarine, and chlorogenic acid among many others possessed high binding affinities for both proteins as evident from their docking scores, with some possessing lower docking scores compared to the standard compound (Remdesivir). Further subjection of the hit compounds to drug-likeness, pharmacokinetics, and toxicity profiling revealed chlorogenic acid, rosmarinic acid, and chicoric acid as the compounds with desirable profiles and toxicity properties, while the study of their electronic properties via density functional theory calculations revealed rosmarinic acid as the most reactive and least stable among the sets of lead compounds that were identified in the study. Molecular dynamics simulation of the complexes formed after docking revealed the stability of the complexes. Ultimately, further experimental procedures are needed to validate the findings of this study.

Developmental landscape of computational techniques to explore the potential phytochemicals from Punica granatum peels for their antioxidant activity in Alzheimer's disease.

Alzheimer's disease (AD) is more commonly found in women than in men as the risk increases with age. Phytochemicals are screened in silico from Punica granatum peels for their antioxidant activity to be utilized for Alzheimer's disease. Alzheimer's disease is inhibited by the hormone estrogen, which protects the brain from the bad effects of amyloid beta and acetylcholine (ACh), and is important for memory processing. For the purpose, a library of about 1,000 compounds from P. granatum were prepared and studied by applying integrated computational calculations like 3D-QSAR, molecular docking, MD simulation, ADMET, and density functional theory (DFT). The 3D-QSAR model screened the active compounds B25, B29, B35, B40, B45, B46, B48, B61, and B66 by the field points and activity atlas model from the prepared library. At the molecular level, docking was performed on active compounds for leading hit compounds such as B25 and B35 that displayed a high MolDock score, efficacy, and compatibility with drug delivery against the antioxidant activity. Optimization of the structure and chemical reactivity parameter of the hit compound was calculated by DFT. Moreover, ADMET prediction was evaluated to check the bioavailability and toxicity of the hit compound. Hesperidin (B25) is found to be a hit compound after the whole study and can be synthesized for potent drug discovery in the future.

One-pot multicomponent synthesis of novel pyridine derivatives for antidiabetic and antiproliferative activities.

Background: Due to the close relationship of diabetes with hypertension reported in various research, a set of pyridine derivatives with US FDA-approved drug cores were designed and integrated by artificial intelligence. Methods: Novel pyridines were designed and synthesized. Compounds MNS-1-MNS-4 were evaluated for their structure and were screened for their in vitro antidiabetic (α-amylase) activity and anticancer (HepG2) activity by methyl thiazolyl tetrazolium assay. Comparative 3D quantitative structure-activity relationship analysis and pharmacophore generation were carried out. Results: The study revealed MNS-1 and MNS-4 as good alternatives to acarbose as antidiabetic agents, and MNS-2 as a more viable, better alternative to doxorubicin in the methyl thiazolyl tetrazolium assay. Conclusion: This combination of studies identifies new and more active analogs of existing FDA-approved drugs for the treatment of diabetes.

Exploring the potential of FDA approved anti-diabetic drugs for repurposing against COVID-19: a core combination of multiple computational strategies and integrated artificial intelligence.

The latest variant of coronavirus is omicron. The World Health Organization (WHO) designated variation 'B.1.1.529' named omicron as a variant of concern (VOC) on 26 November 2021. By September 2020, it will have infected over 16 million patients and killed over 600,000 people over the world. This very infectious viral illness still poses a danger to world health; it has also become the greatest problem the world is facing and become the main area of research. The development of vaccines is insufficient to stop their spread and serious effects. Despite several reputable pharmaceutical firms claiming to have developed a cure for COVID-19. For that purpose, the field-based 3D-QSAR model has been used to analyze a series of anti-diabetic drugs to repurpose them against COVID-19. The LOO verified partial least square (PLS) model generates satisfactory q2 (0.4) and r2 (0.5) values. By using this model 10 compounds were screened out of 55 FDA approved anti-diabetic drugs (built-up library). Additionally, these substances were examined using molecular docking screening and ADMET. Finally, the drugs L8, and L23 were discovered to be the lead drugs. Density functional theory at the B3LYP/6-311G* technique was used to examine structural geometries, electronic characteristics, and molecular electrostatic potential (MEP). This work will greatly assist in the detection and development of leads for early drug development to control COVID-19.Communicated by Ramaswamy H. Sarma.

The combination of multi-approach studies to explore the potential therapeutic mechanisms of imidazole derivatives as an MCF-7 inhibitor in therapeutic strategies.

Breast cancer covers a large area of research because of its prevalence and high frequency all over the world. This study is based on drug discovery against breast cancer from a series of imidazole derivatives. A 3D-QSAR and activity atlas model was developed by exploring the dataset computationally, using the machine learning process of Flare. The dataset of compounds was divided into active and inactive compounds according to their biological and structural similarity with the reference drug. The obtained PLS regression model provided an acceptable r 2 = 0.81 and q2 = 0.51. Protein-ligand interactions of active molecules were shown by molecular docking against six potential targets, namely, TTK, HER2, GR, NUDT5, MTHFS, and NQO2. Then, toxicity risk parameters were evaluated for hit compounds. Finally, after all these screening processes, compound C10 was recognized as the best-hit compound. This study identified a new inhibitor C10 against cancer and provided evidence-based knowledge to discover more analogs.

The impact of the cytoplasmic ubiquitin ligase TNFAIP3 gene variation on transcription factor NF-κB activation in acute kidney injury.

Nuclear factor κB (NF-κB) activation is a deleterious molecular mechanism that drives acute kidney injury (AKI) and manifests in transplanted kidneys as delayed graft function. The TNFAIP3 gene encodes A20, a cytoplasmic ubiquitin ligase and a master negative regulator of the NF- κB signaling pathway. Common population-specific TNFAIP3 coding variants that reduce A20's enzyme function and increase NF- κB activation have been linked to heightened protective immunity and autoimmune disease, but have not been investigated in AKI. Here, we functionally identified a series of unique human TNFAIP3 coding variants linked to the autoimmune genome-wide association studies single nucleotide polymorphisms of F127C; namely F127C;R22Q, F127C;G281E, F127C;W448C and F127C;N449K that reduce A20's anti-inflammatory function in an NF- κB reporter assay. To investigate the impact of TNFAIP3 hypomorphic coding variants in AKI we tested a mouse Tnfaip3 hypomorph in a model of ischemia reperfusion injury (IRI). The mouse Tnfaip3 coding variant I325N increases NF- κB activation without overt inflammatory disease, providing an immune boost as I325N mice exhibit enhanced innate immunity to a bacterial challenge. Surprisingly, despite exhibiting increased intra-kidney NF- κB activation with inflammation in IRI, the kidney of I325N mice was protected. The I325N variant influenced the outcome of IRI by changing the dynamic expression of multiple cytoprotective mechanisms, particularly by increasing NF- κB-dependent anti-apoptotic factors BCL-2, BCL-XL, c-FLIP and A20, altering the active redox state of the kidney with a reduction of superoxide levels and the enzyme super oxide dismutase-1, and enhancing cellular protective mechanisms including increased Foxp3+ T cells. Thus, TNFAIP3 gene variants represent a kidney and population-specific molecular factor that can dictate the course of IRI.

Characterization of stenocephol from Seriphidium stenocephalum as potent HepG2 cell growth and glycogen phosphorylase inhibitor.

Plant-derived compounds represent an important source for developing innovative drugs. One of the widely distributed plants, especially in Afghanistan and Pakistan, Seriphidium stenocephalum, was investigated in this study to identify bioactive compounds. The plant extract was subjected to silica gel column chromatography, four phenolic acid derivatives were isolated, while stenocephol was obtained by ethyl acetate fraction. Stenocephol was subjected to experimental screening for anti-diabetic and anti-cancer activities, measuring its inhibitory potency against glycogen phosphorylase, and its cytotoxicity against HepG2 cells. Further insights into the mechanism of action of stenocephol were obtained from a computational investigation. Stenocephol showed a dose-dependent manner of inhibition against glycogen phosphorylase and HepG2 cells in the low micromolar range. Notably, coupling in vitro and computational investigation, we identified the natural product stenocephol as a possible anti-diabetic and anti-cancer agent, representing a possible starting point for developing novel therapeutics, enriching the armamentarium against the mentioned diseases.

Exploring the anti-SARS-CoV-2 main protease potential of FDA approved marine drugs using integrated machine learning templates as predictive tools.

Since the inception of COVID-19 pandemic in December 2019, socio-economic crisis begins to rise globally and SARS-CoV-2 was responsible for this outbreak. With this outbreak, currently, world is in need of effective and safe eradication of COVID-19. Hence, in this study anti-SAR-Co-2 potential of FDA approved marine drugs (Biological macromolecules) data set is explored computationally using machine learning algorithm of Flare by Cresset Group, Field template, 3D-QSAR and activity Atlas model was generated against FDA approved M-pro SARS-CoV-2 repurposed drugs including Nafamostat, Hydroxyprogesterone caporate, and Camostat mesylate. Data sets were categorized into active and inactive molecules on the basis of their structural and biological resemblance with repurposed COVID-19 drugs. Then these active compounds were docked against the five different M-pro proteins co-crystal structures. Highest LF VS score of Holichondrin B against all main protease co-crystal structures ranked it as lead drug. Finally, this new technique of drug repurposing remained efficient to explore the anti-SARS-CoV-2 potential of FDA approved marine drugs.

Myths, misconceptions, and hesitancy in people residing in Qatar toward mRNA COVID-19 vaccines: An experience exchange from Qatar University health center.

The hesitancy in taking COVID-19 vaccines is a complex process influenced by several factors, including individual, social, and cultural. Health literacy and community awareness around mRNA COVID-19 vaccines are critical for successfully combating the pandemic. Healthcare professionals, including family physicians and nurses, can help increase community awareness and mitigate some misconceptions and hesitancy regarding mRNA COVID-19 vaccines in people's attitudes. Therefore, in this study, we aimed to explore how the interaction between an individual's social identities such as gender, ethnicity, culture, knowledge, and belief impact their hesitancy and attitudes toward mRNA COVID-19 vaccines. We aimed to describe our experience in dealing with people residing in Qatar from the perspective of healthcare practitioners from the Qatar University Health Center during the period when mRNA COVID-19 vaccines was introduced in a time frame of 6 months (April to October, 2021). We identified several factors associated with the reluctance to receive mRNA COVID-19 vaccines once vaccination services were available, affordable, and accessible to everyone in Qatar (Table 1). Most individuals were hesitant and refused to take mRNA COVID-19 vaccines owing to the unjustified myths and fear about potential side effects of vaccines in general and unknown long-term effects of vaccination, especially among women who were uneducated. We believe we have been able to put forth a fair, unbiased, and balanced argument between an individual's right to take or refuse the vaccine and the overall benefits to the public and community health in terms of the overall community immunity when the vast majority of the population will be vaccinated. Our experience could assist in developing culturally sensitive and tailored community outreach programs to increase community awareness as it is the cornerstone on which public health can fight the irrational myths, fear, misconceptions, vaccine hesitancy, and improve vaccination coverages. Moreover, our shared experiences might be able to better prepare future launching of pandemic vaccination campaigns in order to minimize vaccine hesitancy.

Repurposing of metformin and colchicine reveals differential modulation of acute and chronic kidney injury.

Acute kidney injury (AKI) is a major health problem affecting millions of patients globally. There is no effective treatment for AKI and new therapies are urgently needed. Novel drug development, testing and progression to clinical trials is overwhelmingly expensive. Drug repurposing is a more cost-effective measure. We identified 2 commonly used drugs (colchicine and metformin) that alter inflammatory cell function and signalling pathways characteristic of AKI, and tested them in models of acute and chronic kidney injury to assess therapeutic benefit. We assessed the renoprotective effects of colchicine or metformin in C57BL/6 mice challenged with renal ischemia reperfusion injury (IRI), treated before or after injury. All animals underwent analysis of renal function and biomolecular phenotyping at 24 h, 48 h and 4 weeks after injury. Murine renal tubular epithelial cells were studied in response to in vitro mimics of IRI. Pre-emptive treatment with colchicine or metformin protected against AKI, with lower serum creatinine, improved histological changes and decreased TUNEL staining. Pro-inflammatory cytokine profile and multiple markers of oxidative stress were not substantially different between groups. Metformin augmented expression of multiple autophagic proteins which was reversed by the addition of hydroxychloroquine. Colchicine led to an increase in inflammatory cells within the renal parenchyma. Chronic exposure after acute injury to either therapeutic agent in the context of reduced renal mass did not mitigate the development of fibrosis, with colchicine significantly worsening an ischemic phenotype. These data indicate that colchicine and metformin affect acute and chronic kidney injury differently. This has significant implications for potential drug repurposing, as baseline renal disease must be considered when selecting medication.

CD47 limits autophagy to promote acute kidney injury.

Acute kidney injury (AKI) initiates a complex pathophysiological cascade leading to epithelial cell death. Recent studies identify autophagy, a key intracellular process that degrades cytoplasmic constituents, as protective against AKI. We have previously reported that the protein thrombospondin-1 and its receptor CD47 are induced in AKI; however, the mechanism underlying their regulation of injury is unknown. Here, we investigated whether CD47 signaling affects autophagy to regulate AKI. Wild-type (WT) and CD47-/- mice were challenged with renal ischemia-reperfusion injury. All animals underwent analysis of renal function and biomolecular phenotyping. CD47-/- mice were resistant to AKI, with decreased serum creatinine and ameliorated histologic changes compared with WT animals. These mice also displayed increased abundance of key autophagy genes, including autophagy-related gene (Atg)5, Atg7, beclin-1, and microtubule-associated proteins 1A/1B light chain 3 (LC3) at baseline and post-AKI, which were significantly reduced in WT mice. Changes in protein expression correlated with increased autophagosome and autolysosome formation in renal tubular epithelial cells (RTECs). In mouse kidney transplantation, treatment with a CD47-blocking antibody that improved function was associated with increased autophagy compared with control mice. Primary isolated RTECs from CD47-/- mice demonstrated increased basal expression of several autophagy components that was preserved under hypoxic stress. These data suggest that activated CD47 promotes AKI through inhibition of autophagy and point to CD47 as a target to preserve renal function following injury.-El-Rashid, M., Ghimire, K., Sanganeria, B., Lu, B., Rogers, N. M. CD47 limits autophagy to promote acute kidney injury.

Association of genetic polymorphism of PC-1 gene (rs1044498 Lys121Gln) with insulin-resistant type 2 diabetes mellitus in Punjabi Population of Pakistan.

BACKGROUND: Insulin resistance (IR), known to reduce the response to insulin action, develops with obesity leading to type 2 diabetes mellitus (T2DM). The PC-1 gene has been associated with dyslipidemia, polycystic ovarian disease and T2DM in different regions of the world. The objective of the present study was to investigate the genetic association of PC-1 rs1044498 polymorphism with insulin resistance in type 2 diabetes in the Punjabi population of Pakistan. METHODS: This study was carried out on 161 healthy controls and 161 patients of T2DM with insulin resistance. Whole blood was collected for DNA extraction and molecular studies. PCR-RFLP with AvaII was performed to determine the genotype in cases and controls. Chi-square and Hardy Weinberg analyses were carried out. Statistical analysis was performed by SPSS software. RESULTS: The demographic data of cases and controls showed significant differences for different parameters like glucose, insulin, Homeostatic model assessment-insulin resistance (HOMA-IR) and lipid profiles (p < 0.000). Different statistical models revealed that all the dominant models were found associated in between alleles for disease risk (p < 0.001) while no association of PC-1 rs1044498 (K121Q) polymorphism was found with insulin-resistant parameters in T2DM cases. CONCLUSION: Overall, the results indicate that the K121Q polymorphism was not found associated with insulin resistance in type 2 diabetes in a Pakistani Punjabi population. This is the first-ever report about the genotype of PC-1 gene in this population.

Variability in the therapeutic response of Metformin treatment in patients with type 2 diabetes mellitus.

OBJECTIVE: To assess the glycemic response of metformin in patients with Type-2 Diabetes Mellitus (T2DM) as well as to see its association with reductions in BMI and GIT intolerance. METHODS: This Quasi, Experimental study was conducted at Jinnah-Allama Iqbal Institute of Diabetes and Endocrinology (JAIDE) Jinnah Hospital, Lahore from 1st March 2016 to 30th September 2016. Newly diagnosed T2DM patients were given metformin for duration of three months and later on they were categorized into Responders and Non-Responders on the basis of HbA1c (A1C) reductions, which were estimated by Hemoglobin (A1C) analyzer (TD4611A TAIDoc Tech. Taiwan) through photometry. Similarly, baseline BMI and BMI after three months therapy with metformin was also recorded. RESULTS: Among total of 200 patients, 40.5% of the patients were classified as Non-Responders whereas; 59.5% of the patients as Responders. The baseline BMI (26.09 kg/m2) was also decreased significantly after metformin therapy (25.40 kg/m2). It was found that metformin reduced the A1C in all the patients. However, the glycemic control was much better in patients with higher baseline A1C (1.13% ± 0.08) as compared to lower baseline levels (0.61% ± 0.07). Regarding GIT intolerance, 140 patients lacked the symptoms, out of which 60.7% were responders and 39.3% were non-responders. CONCLUSIONS: Metformin lead to improvement in glycemic control in 59.5% of newly diagnosed T2DM patients after taking metformin for three months but in 40.5% it did not which may be because of combined effects of various gene polymorphisms and their interaction with non-genetic factors. Metformin reduced the BMI in all the patients; however, BMI lowering activity of metformin was same regardless of its effect on HbA1C. Moreover, the signs and symptoms of GIT intolerance did not differ between the two groups.