Current Computer - Aided Drug Design - Volume 20, Issue 6, 2024

The rapidity and high-throughput nature of in silico technologies make them advantageous for predicting the properties of a large array of substances. In silico approaches can be used for compounds intended for synthesis at the beginning of drug development when there is either no or very little compound available. In silico approaches can be used for impurities or degradation products. Quantifying drugs and related substances (RS) with pharmaceutical drug analysis (PDA) can also improve drug discovery (DD) by providing additional avenues to pursue. Potential future applications of PDA include combining it with other methods to make insilico predictions about drugs and RS. One possible outcome of this is a determination of the drug potential of nontoxic RS. ADME estimation, QSAR research, molecular docking, bioactivity prediction, and toxicity testing all involve impurity profiling. Before committing to DD, RS with minimal toxicity can be utilised in silico. The efficacy of molecular docking in getting a medication to market is still debated despite its refinement and improvement. Biomedical labs and pharmaceutical companies were hesitant to adopt molecular docking algorithms for drug screening despite their decades of development and improvement. Despite the widespread use of "force fields" to represent the energy exerted within and between molecules, it has been impossible to reliably predict or compute the binding affinities between proteins and potential binding medications.

Aims: This study aims to evaluate the efficacy and safety of PARP inhibitor therapy in advanced ovarian cancer and identify the optimal treatment for the survival of patients. Background: The diversity of PARP inhibitors makes clinicians confused about the optimal strategy and the most effective BRCAm mutation-based regimen for the survival of patients with advanced ovarian cancer. Objectives: The objective of this study is to compare the effects of various PARP inhibitors alone or in combination with other agents in advanced ovarian cancer. Methods: PubMed, Embase, Cochrane Library, and Web of Science were searched for relevant studies on PARP inhibitors for ovarian cancer. Bayesian network meta-analysis was performed using Stata 15.0 and R 4.0.4. The primary outcome was the overall PFS, and the secondary outcomes included OS, AE3, DISAE, and TFST. Results: Fifteen studies involving 5,788 participants were included. The Bayesian network metaanalysis results showed that olaparibANDAI was the most beneficial in prolonging overall PFS and non-BRCAm PFS, followed by niraparibANDAI. However, for BRCAm patients, olaparibTR might be the most effective, followed by niraparibANDAI. Olaparib was the most effective for the OS of BRCAm patients. AI, olaparibANDAI, and veliparibTR were more likely to induce grade 3 or higher adverse events. AI and olaparibANDAI were more likely to cause DISAE. Conclusion: PARP inhibitors are beneficial to the survival of patients with advanced ovarian cancer. The olaparibTR is the most effective for BRCAm patients, whereas olaparibANDAI and niraparibANDAI are preferable for non-BRCAm patients. Other: More high-quality studies are desired to investigate the efficacy and safety of PARP inhibitors in patients with other genetic performances.

Introduction: Sepsis is a state of the systemic inflammatory response of the host induced by infection, frequently affecting numerous organs and producing varied degrees of damage. The most typical consequence of sepsis is sepsis-associated acute kidney injury(SA-AKI). Xuebijing is developed based on XueFuZhuYu Decoction. Five Chinese herbal extracts, including Carthami Flos, Radix Paeoniae Rubra, Chuanxiong Rhizoma, Radix Salviae, and Angelicae Sinensis Radix, make up the majority of the mixture. It has properties that are anti-inflammatory and anti-oxidative stress. Xuebijing is an effective medication for the treatment of SA-AKI, according to clinical research. But its pharmacological mechanism is still not completely understood. Methods: First, the composition and target information of Carthami Flos, Radix Paeoniae Rubra, Chuanxiong Rhizoma, Radix Salviae, and Angelicae Sinensis Radix were collected from the TCMSP database, while the therapeutic targets of SA-AKI were exported from the gene card database. To do a GO and KEGG enrichment analysis, we first screened the key targets using a Venn diagram and Cytoscape 3.9.1. To assess the binding activity between the active component and the target, we lastly used molecular docking. Results: For Xuebijing, a total of 59 active components and 267 corresponding targets were discovered, while for SA-AKI, a total of 1,276 targets were connected. There were 117 targets in all that was shared by goals for active ingredients and objectives for diseases. The TNF signaling pathway and the AGE-RAGE pathway were later found to be significant pathways for the therapeutic effects of Xuebijing by GO analysis and KEGG pathway analysis. Quercetin, luteolin, and kaempferol were shown to target and modulate CXCL8, CASP3, and TNF, respectively, according to molecular docking results. Conclusion: This study predicts the mechanism of action of the active ingredients of Xuebijing in the treatment of SA-AKI, which provides a basis for future applications of Xuebijing and studies targeting the mechanism.

Introduction: JAK3 kinase inhibitor has become an effective means to treat tumors and autoimmune diseases. Methods: In this study, molecular docking and molecular dynamics simulation were used to study the theoretical interaction mechanism between 1-phenylimidazolidine-2-one molecules and JAK3 protein. Results: The results of molecular docking showed that the six 1-phenylimidazolidine-2-one derivatives obtained by virtual screening were bound to the ATP pocket of JAK3 kinase, which were competitive inhibitors of ATP, and were mainly bound to the pocket through hydrogen bonding and hydrophobic interaction. Further, MM/GBSA based on molecular dynamics simulation sampling was used to calculate the binding energy between six molecules and the JAK3 kinase protein. Subsequently, the binding energy was decomposed into the contribution of each amino acid residue, of which Leu905, Lys855, Asp967, Leu956, Tyr904, and Val836 were the main energycontributing residues. Among them, the molecule numbered LCM01415405 can interact with the specific amino acid Arg911 of JAK3 kinase, suggesting that the molecule may be a selective JAK3 kinase inhibitor. The root-mean-square fluctuation (RMSF) of JAK3 kinase pocket residues during molecular dynamics simulation showed that the combination of six new potential small molecule inhibitors with JAK3 kinase could reduce the flexibility of JAK3 kinase pocket residues. Conclusion: These findings reveal the mechanism of 1-phenylimidazolidine-2-one derivatives on JAK3 protein and provide a relatively solid theoretical basis for the development and structural optimization of JAK3 protein inhibitors.

Aims and Objectives: Metabolic syndrome (MetS) is a group of metabolic disorders that includes obesity in combination with at least any two of the following conditions, i.e., insulin resistance, high blood pressure, low HDL cholesterol, and high triglycerides level. Treatment of this syndrome is challenging because of the multiple interlinked factors that lead to increased risks of type-2 diabetes and cardiovascular diseases. This study aims to conduct extensive in silico analysis to (i) find central genes that play a pivotal role in MetS and (ii) propose suitable drugs for therapy. Our objective is to first create a drug-disease network and then identify novel genes in the drug-disease network with strong associations to drug targets, which can help in increasing the therapeutical effects of different drugs. In the future, these novel genes can be used to calculate drug synergy and propose new drugs for the effective treatment of MetS. Methods: For this purpose, we (i) investigated associated drugs and pathways for MetS, (ii) employed eight different similarity measures to construct eight gene regulatory networks, (iii) chose an optimal network, where a maximum number of drug targets were central, (iv) determined central genes exhibiting strong associations with these drug targets and associated disease-causing pathways, and lastly (v) employed these candidate genes to propose suitable drugs. Results: Our results indicated (i) a novel drug-disease network complex, with (ii) novel genes associated with MetS. Conclusion: Our developed drug-disease network complex closely represents MetS with associated novel findings and markers for an improved understanding of the disease and suggested therapy.

Background: The novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a worldwide public health crisis. At present, the development of effective drugs and/or related therapeutics is still the most urgent and important task for combating the virus. The viral entry and associated infectivity mainly rely on its envelope spike protein to recognize and bind to the host cell receptor angiotensin-converting enzyme 2 (ACE2) through a conformational switch of the spike receptor binding domain (RBD) from inactive to active state. Thus, it is of great significance to design an allosteric inhibitor targeting spike to lock it in the inactive and ACE2-inaccessible state. Objective: This study aims to discover the potential broad-spectrum allosteric inhibitors capable of binding and stabilizing the diverse spike variants, including the wild type, Delta, and Omicron, in the inactive RBD down state. Methods: In this work, we first detected a potential allosteric pocket within the SARS-CoV-2 spike protein. Then, we performed large-scale structure-based virtual screening by targeting the putative allosteric pocket to identify allosteric inhibitors that could stabilize the spike inactive state. Molecular dynamics simulations were further carried out to evaluate the effects of compound binding on the stability of spike RBD. Results: Finally, we identified three potential allosteric inhibitors, CPD3, CPD5, and CPD6, against diverse SARS-CoV-2 variants, including Wild-type, Delta, and Omicron variants. Our simulation results showed that the three compounds could stably bind the predicted allosteric site and effectively stabilize the spike in the inactive state. Conclusion: The three compounds provide novel chemical structures for rational drug design targeting spike protein, which is expected to greatly assist in the development of new drugs against SARS-CoV-2.

Background: Anthrapyrazoles are a new class of antitumor agents and successors to anthracyclines possessing a broad range of antitumor activity in various model tumors. Objectives: The present study introduces novel QSAR models for the prediction of antitumor activity of anthrapyrazole analogues. Methods: The predictive performance of four machine learning algorithms, namely artificial neural networks, boosted trees, multivariate adaptive regression splines, and random forest, was studied in terms of variation of the observed and predicted data, internal validation, predictability, precision, and accuracy. Results: ANN and boosted trees algorithms met the validation criteria. It means that these procedures may be able to forecast the anticancer effects of the anthrapyrazoles studied. Evaluation of validation metrics, calculated for each approach, indicated the artificial neural network (ANN) procedure as the algorithm of choice, especially with regard to the obtained predictability as well as the lowest value of mean absolute error. The designed multilayer perceptron (MLP)-15-7-1 network displayed a high correlation between the predicted and the experimental pIC50 value for the training, test, and validation set. A conducted sensitivity analysis enabled an indication of the most important structural features of the studied activity. Conclusion: The ANN strategy combines topographical and topological information and can be used for the design and development of novel anthrapyrazole analogues as anticancer molecules.

Background: Leptin is predominant in regulating body weight by stimulating energy expenditure through its neuronal action in the brain. Moreover, it is projected to adipose tissue and induces adipocyte browning by activating the β3-adrenergic receptor (β3AR). However, the expression of leptin receptor (Lep-R) and β3AR in people with obesity is downregulated. Aim: We hypothesized that Hibiscus sabdariffa Linn. extract (HSE) would increase hypothalamus arcuate nucleus (ARC) Lep-R and white adipose tissue (WAT) β3AR mRNA expression in DIO rats. This study also analyzed the potency of H. sabdariffa bioactive compounds as activators of Lep-R and β3AR by an in-silico experiment. Methods: Twenty-four male Sprague-Dawley rats were divided into four groups: Control (standard food), DIO (high-fat diet), DIO-Hib200 (HFD+HSE 200 mg/kg BW), and DIO-Hib400 (HFD+HSE400 mg/kg BW). HSE was administered orally for five weeks, once a day. Results: HSE administration significantly (p <0,05) increased the ARC Lep-R expression. The Lee index significantly decreased to the normal range (≤ 310) with p <0,001 for DIO-Hib200 and p <0,01 for DIO-Hib400. Among 39 bioactive compounds, 5-O-caffeoyl shikimic acid exhibited high free binding scores (-8,63) for Lep-R, and myricetin_3_arabinogalactoside had high free binding scores (-9,39) for β3AR. These binding predictions could activate Lep-R and β3AR. Conclusion: This study highlights that HSE could be a potential therapeutic target for obesity by increasing LepR mRNA and leptin sensitivity, enhancing energy expenditure, and reducing obesity.

Background: Epilepsy is a chronic neurological disorder caused by irregular electrical activity in the brain. To manage this disorder effectively, it is imperative to identify potential pharmacological targets and to understand the pathophysiology of epilepsy in depth. Objective: This research aimed to identify promising leads from a library of 1,2,4-triazine-6Hindolo[ 2,3-b]quinoline derivatives and optimize them using in silico and dynamic processes. Methods: We used computational studies to examine 1,2,4-Triazine-6H-indolo[2,3-b]quinoline derivatives. Some methods were used to strengthen the stability of binding sites, including Docking, ADMET, IFD, MMGBSA, Density Functional Theory (DFT), and Molecular Dynamics. Results: HRSN24 and HRSN34 exhibited promising pharmacokinetic and pharmacodynamic characteristics compared to standard drugs (Carbamazepine and Phenytoin) and a co-crystal ligand (Diazepam). Both HRSN24 and HRSN34 presented notable Glide Xp docking scores (-4.528 and -4.633 Kcal/mol), IFD scores (-702.22 and -700.3 Kcal/mol), and MMGBSA scores (-45.71 and -14.46 Kcal/mol). HRSN24 was selected for molecular dynamics and DFT analysis. During MD, HRSN24 identified LYS21, GLY22, ASP24, ARG26, VAL53, MET55, and SER308 as the most important amino acid residues for hydrophobic interactions. A DFT computation was performed to determine the physicochemical properties of HRSN24, revealing a total energy of -1362.28 atomic units, a HOMO value of -0.20186, and a LUMO value of -0.01915. Conclusion: Based on computational modelling techniques, an array of 1,2,4-triazine-6H-indolo [2,3-b]quinoline derivatives were evaluated for their anti-convulsant properties. A stable compound within the GABAA receptor was identified by HRSN24, suggesting its affinity as an anti-convulsant.

Background: Due to the biological importance of the benzoxazole derivatives, some 1- (benzo[d]oxazol-2-yl)-3,5-diphenyl-formazans 4a-f were synthesized and screened for in-silico studies and in-vitro antibacterial activity. Methods: The benzo[d]oxazole-2-thiol (1) was prepared by reacting with 2-aminophenol and carbon disulfide in the presence of alcoholic potassium hydroxide. Then 2-hydrazinylbenzo[d] oxazole (2) was synthesized from the reaction of compound 1 with hydrazine hydrate in the presence of alcohol. Compound 2 was reacted with aromatic aldehydes to give Schiff base, 2-(2- benzylidene-hydrazinyl)benzo[d]oxazole derivatives 3a-f. The title compounds, formazan derivatives 4a-f, were prepared by a reaction of benzene diazonium chloride. All compounds were confirmed by their physical data, FTIR, ¹H-NMR, and ¹³CNMR spectral data. All the prepared title compounds were screened for in-silico studies and in-vitro antibacterial activity on various microbial strains. Results: Molecular docking against the 4URO receptor demonstrated that molecule 4c showed a maximum dock score of (-) 8.0 kcal/mol. MD simulation data reflected the stable ligand-receptor interaction. As per MM/PBSA analysis, the maximum free binding energy of (-) 58.831 kJ/mol was exhibited by 4c. DFT calculation data confirmed that most of the molecules were soft molecules with electrophilic nature. Conclusion: The synthesized molecules were validated using molecular docking, MD simulation, MMPBSA analysis, and DFT calculation. Among all the molecules, 4c showed maximum activity. The activity profile of the synthesized molecules against tested micro-organisms was found to be 4c>4b>4a>4e>4f>4d.

Background: Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin's lymphoma worldwide. Novel treatment strategies are still needed for this disease. Objective: The present study aimed to systematically explore the potential targets and molecular mechanisms of matrine in the treatment of DLBCL. Methods: Potential matrine targets were collected from multiple platforms. Microarray data and clinical characteristics of DLBCL were downloaded from publicly available database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were applied to identify the hub genes of DLBCL using R software. Then, the shared target genes between matrine and DLBCL were identified as the potential targets of matrine against DLBCL. The least absolute shrinkage and selection operator (LASSO) algorithm was used to determine the final core target genes, which were further verified by molecular docking simulation and receiver operating characteristic (ROC) curve analysis. Functional analysis was also performed to elucidate the potential mechanisms. Results: A total of 222 matrine target genes and 1269 DLBCL hub genes were obtained through multiple databases and machine learning algorithms. From the nine shared target genes of matrine and DLBCL, five final core target genes, including CTSL, NR1H2, PDPK1, MDM2, and JAK3, were identified. Molecular docking showed that the binding of matrine to the core genes was stable. ROC curves also suggested close associations between the core genes and DLBCL. Additionally, functional analysis showed that the therapeutic effect of matrine against DLBCL may be related to the PI3K-Akt signaling pathway. Conclusion: Matrine may target five genes and the PI3K-Akt signaling pathway in DLBCL treatment.

Background: The Fule Cream (FLC) is an herbal formula widely used for the treatment of pediatric atopic dermatitis (AD), however, the main active components and functional mechanisms of FLC remain unclear. This study performed an initial exploration of the potential acting mechanisms of FLC in childhood AD treatment through analyses of an AD mouse model using network pharmacology, molecular docking technology, and RNA-seq analysis. Materials and Methods: The main bioactive ingredients and potential targets of FLC were collected from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) and SwissTargetPrediction databases. An herb-compound-target network was built using Cytoscape 3.7.2. The disease targets of pediatric AD were searched in the DisGeNET, Therapeutic Target Database (TTD), OMIM, DrugBank and GeneCards databases. The overlapping targets between the active compounds and the disease were imported into the STRING database for the construction of the protein-protein interaction (PPI) network. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the intersection targets were performed, and molecular docking verification of the core compounds and targets was then performed using AutoDock Vina 1.1.2. The AD mouse model for experimental verification was induced by MC903. Results: The herb-compound-target network included 415 nodes and 1990 edges. Quercetin, luteolin, beta-sitosterol, wogonin, ursolic acid, apigenin, stigmasterol, kaempferol, sitogluside and myricetin were key nodes. The targets with higher degree values were IL-4, IL-10, IL-1α, IL-1β, TNFα, CXCL8, CCL2, CXCL10, CSF2, and IL-6. GO enrichment and KEGG analyses illustrated that important biological functions involved response to extracellular stimulus, regulation of cell adhesion and migration, inflammatory response, cellular response to cytokine stimulus, and cytokine receptor binding. The signaling pathways in the FLC treatment of pediatric AD mainly involve the PI3K-Akt signaling pathway, cytokine128;’cytokine receptor interaction, chemokine signaling pathway, TNF signaling pathway, and NF-ΚB signaling pathway. The binding energy scores of the compounds and targets indicate a good binding activity. Luteolin, quercetin, and kaempferol showed a strong binding activity with TNFα and IL-4. Conclusion: This study illustrates the main bioactive components and potential mechanisms of FLC in the treatment of childhood AD, and provides a basis and reference for subsequent exploration.

Aims: The aim of this study is to develop a novel antiviral strategy capable of efficiently targeting a broad set of SARS-CoV-2 variants. Background: Since the first emergence of SARS-CoV-2, it has rapidly transformed into a global pandemic, posing an unprecedented threat to public health. SARS-CoV-2 is prone to mutation and continues to evolve, leading to the emergence of new variants capable of escaping immune protection achieved due to previous SARS-CoV-2 infections or by vaccination. Objective: RNA interference (RNAi) is a remarkable biological mechanism that can induce gene silencing by targeting complementary mRNA and inhibiting its translation. Methods: In this study, using the computational approach, we predicted the most efficient siRNA capable of inhibiting SARS-CoV-2 variants of concern (VoCs). Results: The presented siRNA was characterized and evaluated for its thermodynamic properties, offsite-target hits, and in silico validation by molecular docking and molecular dynamics simulations (MD) with Human AGO2 protein. Conclusion: The study contributes to the possibility of designing and developing an effective response strategy against existing variants of concerns and preventing further.

Aims: To decipher the underlying mechanisms of Sanleng-Ezhu for the treatment of idiopathic pulmonary fibrosis based on network pharmacology and single-cell RNA sequencing data. Background: Idiopathic Pulmonary Fibrosis (IPF) is the most common type of interstitial lung disease. Although the combination of herbs Sanleng (SL) and Ezhu (EZ) has shown reliable efficacy in the management of IPF, its underlying mechanisms remain unknown. Methods: Based on LC-MS/MS analysis and the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database, we identified the bioactive components of SL-EZ. After obtaining the IPF-related dataset GSE53845 from the Gene Expression Omnibus (GEO) database, we performed the differential expression analysis and the weighted gene co-expression network analysis (WGCNA), respectively. We obtained lowly and highly expressed IPF subtype gene sets by comparing Differentially Expressed Genes (DEGs) with the most significantly negatively and positively related IPF modules in WGCNA. Subsequently, we performed Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on IPF subtype gene sets. The low- and highexpression MCODE subgroup feature genes were identified by the MCODE plug-in and were adopted for Disease Ontology (DO), GO, and KEGG enrichment analyses. Next, we performed the immune cell infiltration analysis of the MCODE subgroup feature genes. Single-cell RNA sequencing analysis demonstrated the cell types which expressed different MCODE subgroup feature genes. Molecular docking and animal experiments validated the effectiveness of SL-EZ in delaying the progression of pulmonary fibrosis. Results: We obtained 5 bioactive components of SL-EZ as well as their corresponding 66 candidate targets. After normalizing the samples of the GSE53845 dataset from the GEO database source, we obtained 1907 DEGs of IPF. Next, we performed a WGCNA analysis on the dataset and got 11 modules. Notably, we obtained 2 IPF subgroups by contrasting the most significantly up- and down-regulated modular genes in IPF with DEGs, respectively. The different IPF subgroups were compared with drugcandidate targets to obtain direct targets of action. After constructing the protein interaction networks between IPF subgroup genes and drug candidate targets, we applied the MCODE plug-in to filter the highest-scoring MCODE components. DO, GO, and KEGG enrichment analyses were applied to drug targets, IPF subgroup genes, and MCODE component signature genes. In addition, we downloaded the single-cell dataset GSE157376 from the GEO database. By performing quality control and dimensionality reduction, we clustered the scattered primary sample cells into 11 clusters and annotated them into 2 cell subtypes. Drug sensitivity analysis suggested that SL-EZ acts on different cell subtypes in IPF subgroups. Molecular docking revealed the mode of interaction between targets and their corresponding components. Animal experiments confirmed the efficacy of SL-EZ. Conclusion: We found SL-EZ acted on epithelial cells mainly through the calcium signaling pathway in the lowly-expressed IPF subtype, while in the highly-expressed IPF subtype, SL-EZ acted on smooth muscle cells mainly through the viral infection, apoptosis, and p53 signaling pathway.

Background: Type 2 diabetes-associated cognitive dysfunction (DCD) is a chronic complication of diabetes that has gained international attention. The medicinal compound Banxia Xiexin Decoction (BXXXD) from traditional Chinese medicine (TCM) has shown potential in improving insulin resistance, regulating endoplasmic reticulum stress (ERS), and inhibiting cell apoptosis through various pathways. However, the specific mechanism of action and medical value of BXXXD remain unclear. Methods: We utilized TCMSP databases to screen the chemical constituents of BXXXD and identified DCD disease targets through relevant databases. By using Stitch and String databases, we imported the data into Cytoscape 3.8.0 software to construct a protein-protein interaction (PPI) network and subsequently identified core targets through network topology analysis. The core targets were subjected to Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The results were further validated through in vitro experiments. Results: Network pharmacology analysis revealed the screening of 1490 DCD-related targets and 190 agents present in BXXXD. The topological analysis and enrichment analysis conducted using Cytoscape software identified 34 core targets. Additionally, GO and KEGG pathway analyses yielded 104 biological targets and 97 pathways, respectively. BXXXD exhibited its potential in treating DCD by controlling synaptic plasticity and conduction, suppressing apoptosis, reducing inflammation, and acting as an antioxidant. In a high glucose (HG) environment, the expression of JNK, Foxo3a, SIRT1, ATG7, Lamp2, and LC3 was downregulated. BXXXD intervention on HT22 cells potentially involved inhibiting excessive oxidative stress, promoting neuronal autophagy, and increasing the expression levels of JNK, SIRT1, Foxo3a, ATG7, Lamp2, and LC3. Furthermore, the neuroprotective effect of BXXXD was partially blocked by SP600125, while quercetin enhanced the favorable role of BXXXD in the HG environment. Conclusion: BXXXD exerts its effects on DCD through multiple components, targets, levels, and pathways. It modulates the JNK/SIRT1/Foxo3a signaling pathway to mitigate autophagy inhibition and apoptotic damage in HT22 cells induced by HG. These findings provide valuable perspectives and concepts for future clinical trials and fundamental research.

Background: People with osteoarthritis place a huge burden on society. Early diagnosis is essential to prevent disease progression and to select the best treatment strategy more effectively. In this study, the aim was to examine the diagnostic features and clinical value of peripheral blood biomarkers for osteoarthritis. Objective: The goal of this project was to investigate the diagnostic features of peripheral blood and immune cell infiltration in osteoarthritis (OA). Methods: Two eligible datasets (GSE63359 and GSE48556) were obtained from the GEO database to discern differentially expressed genes (DEGs). The machine learning strategy was employed to filtrate diagnostic biomarkers for OA. Additional verification was implemented by collecting clinical samples of OA. The CIBERSORT website estimated relative subsets of RNA transcripts to evaluate the immune-inflammatory states of OA. The link between specific DEGs and clinical immune-inflammatory markers was found by correlation analysis. Results: Overall, 67 robust DEGs were identified. The nuclear receptor subfamily 2 group C member 2 (NR2C2), transcription factor 4 (TCF4), stromal antigen 1 (STAG1), and interleukin 18 receptor accessory protein (IL18RAP) were identified as effective diagnostic markers of OA in peripheral blood. All four diagnostic markers showed significant increases in expression in OA. Analysis of immune cell infiltration revealed that macrophages are involved in the occurrence of OA. Candidate diagnostic markers were correlated with clinical immune-inflammatory indicators of OA patients. Conclusion: We highlight that DEGs associated with immune inflammation (NR2C2, TCF4, STAG1, and IL18RAP) may be potential biomarkers for peripheral blood in OA, which are also associated with clinical immune-inflammatory indicators.

Background: Resveratrol is a polyphenol that is found in plants and has been proposed to have a potential therapeutic effect through the activation of SIRT1, which is a crucial member of the mammalian NAD+ -dependent deacetylases. However, how its activity is enhanced toward specific substrates by resveratrol derivatives has not been studied. This study aimed to evaluate the types of interaction of resveratrol and its derivatives with SIRT1 as the target protein, as well as to find out the best ligand with the strangest interaction with SIRT1. Materials and Methods: In this study, we employed the extensive molecular docking analysis using AutoDock Vina to comparatively evaluate the interactions of resveratrol derivatives (22 molecules from the ZINC database) as ligands with SIRT1 (PDB ID: 5BTR) as a receptor. The ChemDraw and Chem3D tools were used to prepare 3D structures of all ligands and energetically minimize them by the MM2 force field. Results: The molecular docking and visualizations showed that conformational change in resveratrol derivatives significantly influenced the parameter for docking results. Several types of interactions, including conventional hydrogen bonds, carbon-hydrogen bonds, Pi-donor hydrogen bonds, and Pi-Alkyl, were found via docking analysis of resveratrol derivatives and SIRT1 receptors. The possible activation effect of resveratrol 4'-(6-galloylglucoside) with ZINC ID: ZINC230079516 with higher binding energy score (-46.8608 kJ/mol) to the catalytic domain (CD) of SIRT1 was achieved at the maximum value for SIRT1, as compared to resveratrol and its other derivatives. Conclusion: Finally, resveratrol 4'-(6-galloylglucoside), as a derivative for resveratrol, has stably interacted with the CD of SIRT1 and might be a potential effective activator for SIRT1.

Background: Pancreatic ductal adenocarcinoma (PDAC) has a 5-year relative survival rate of less than 10% making it one of the most fatal cancers. A lack of early measures of prognosis, challenges in molecular targeted therapy, ineffective adjuvant chemotherapy, and strong resistance to chemotherapy cumulatively make pancreatic cancer challenging to manage. Objective: The present study aims to enhance understanding of the disease mechanism and its progression by identifying prognostic biomarkers, potential drug targets, and candidate drugs that can be used for therapy in pancreatic cancer. Methods: Gene expression profiles from the GEO database were analyzed to identify reliable prognostic markers and potential drug targets. The disease's molecular mechanism and biological pathways were studied by investigating gene ontologies, KEGG pathways, and survival analysis to understand the strong prognostic power of key DEGs. FDA-approved anti-cancer drugs were screened through cell line databases, and docking studies were performed to identify drugs with high affinity for ARNTL2 and PIK3C2A. Molecular dynamic simulations of drug targets ARNTL2 and PIK3C2A in their native state and complex with nilotinib were carried out for 100 ns to validate their therapeutic potential in PDAC. Results: Differentially expressed genes that are crucial regulators, including SUN1, PSMG3, PIK3C2A, SCRN1, and TRIAP1, were identified. Nilotinib as a candidate drug was screened using sensitivity analysis on CCLE and GDSC pancreatic cancer cell lines. Molecular dynamics simulations revealed the underlying mechanism of the binding of nilotinib with ARNTL2 and PIK3C2A and the dynamic perturbations. It validated nilotinib as a promising drug for pancreatic cancer. Conclusion: This study accounts for prognostic markers, drug targets, and repurposed anti-cancer drugs to highlight their usefulness for translational research on developing novel therapies. Our results revealed potential and prospective clinical applications in drug targets ARNTL2, EGFR, and PI3KC2A for pancreatic cancer therapy.

Introduction: The conventional processes of drug discovery are too expensive, timeconsuming and the success rate is limited. Searching for alternatives that have evident safety and potential efficacy could save money, time and improve the current therapeutic regimen outcomes. Methods: Clinical phytotherapy implies the use of extracts of natural origin for prophylaxis, treatment, or management of human disorders. In this work, the potential role of common Fig (Ficus carica) in the management of COVID-19 infections has been explored. The antiviral effects of Cyanidin 3-rhamnoglucoside which is abundant in common Figs have been illustrated on COVID-19 targets. The immunomodulatory effect and the ability to ameliorate the cytokine storm associated with coronavirus infections have also been highlighted. This work involves various computational studies to investigate the potential roles of common figs in the management of COVID-19 viral infections. Results: Two molecular docking studies of all active ingredients in common Figs were conducted starting with MOE to provide initial insights, followed by Autodock Vina for further confirmation of the results of the top five compounds with the best docking score. Conclusion: Finally, Molecular dynamic simulation alongside MMPBSA calculations were conducted using GROMACS to endorse and validate the entire work.

Background: Hyperlipidemia, a cardiovascular disease risk factor, is characterized by a rise in low-density lipoprotein (LDL), triglycerides and total cholesterol, and a decrease in high-density lipoprotein (HDL). Cholesteryl ester transfer protein (CETP) enables the transfer of cholesteryl ester from HDL to LDL and very low-density lipoprotein. Objectives: CETP inhibition is a promising approach to prevent and treat cardiovascular diseases. By inhibiting lipid transport activity, it increases HDL levels and decreases LDL levels. Materials and Method: Herein, diaryl sulfonamides 6a-6g and 7a-7g were prepared, and the structure of these compounds was fully determined using different spectroscopic techniques. Results: These compounds underwent biological evaluation in vitro and showed different inhibitory activities against CETP; 100% inhibitory activity was observed for compounds 7a-7g, while activities of compounds 6a-6g ranged up to 42.6% at 10 μM concentration. Pharmacophore mapping agreed with the bioassay results where the four aromatic ring compounds 7a-7g possessed higher fit values against Hypo4/8 and the shape-complemented Hypo4/8 in comparison to compounds 6a-6g. Conclusion: Docking of the synthesized compounds using libdock and ligandfit engines revealed that compounds 7a-7g formed п-п stacking and hydrophobic interactions with the binding pocket, while compounds 6a-6g missed these hydrophobic interactions with amino acids Leu206, Phe265, and Phe263.

Background: Alzheimer's disease (AD) is one of the most concerned neurodegenerative disorders across the world characterized by amyloid-beta (Aβ) plaques and neurofibrillary tangles (NFTs), leading to cognitive decline and memory loss. Targeting key pathways involved in AD like Aβ and NFT pathways, are crucial for the development of effective therapeutic strategies. In this study, we aimed to identify and establish promising dual inhibitors targeting BACE1 and GSK-3β, two proteins implicated in Aβ and NFT formation respectively. Methods: We have used molecular docking, ADME property analysis, and MMGBSA calculations for the identification of hit molecules and further evaluation of binding affinity, drug-like properties, and stability against BACE1 and GSK-3β. Results: Our results demonstrated strong binding affinities of ZINC000034853956 towards the active sites of both proteins, with favorable interactions involving key residues crucial for inhibitory activity. Additionally, ZINC000034853956 exhibited favorable drug-like properties. MD simulations revealed the stable binding of ZINC000034853956 to both BACE1 and GSK-3β over a 50 ns period, with consistent ligand-protein interactions, such as hydrogen bonding and hydrophobic contacts. These findings highlight the potential of ZINC000034853956 as a promising candidate for AD treatment, acting as a dual inhibitor targeting both BACE1 and GSK-3β. Overall, our study provides valuable insights into the potential of ZINC000034853956 as a dual inhibitor for AD. The strong binding affinity, favorable drug-like properties, and stability observed in MD simulations support its suitability for further optimization and preclinical studies. Conclusion: Further investigations are warranted to elucidate the precise molecular mechanisms and therapeutic benefits of ZINC000034853956. Our findings offer hope for the development of novel therapeutic interventions targeting crucial pathways involved in AD neurodegeneration.

Background: In this study, we aimed to develop a new end-to-end learning model called Graph-Drug-Target Interaction (DTI), which integrates various types of information in the heterogeneous network data, and to explore automatic learning of the topology-maintaining representations of drugs and targets, thereby effectively contributing to the prediction of DTI. Precise predictions of DTI can guide drug discovery and development. Most machine learning algorithms integrate multiple data sources and combine them with common embedding methods. However, the relationship between the drugs and target proteins is not well reported. Although some existing studies have used heterogeneous network graphs for DTI prediction, there are many limitations in the neighborhood information between the nodes in the heterogeneous network graphs. We studied the drug-drug interaction (DDI) and DTI from DrugBank Version 3.0, protein–protein interaction (PPI) from the human protein reference database Release 9, drug structure similarity from Morgan fingerprints of radius 2 and calculated by RDKit, and protein sequence similarity from Smith-Waterman score. Methods: Our study consists of three major components. First, various drugs and target proteins were integrated, and a heterogeneous network was established based on a series of data sets. Second, the graph neural networks-inspired graph auto-encoding method was used to extract high-order structural information from the heterogeneous networks, thereby revealing the description of nodes (drugs and proteins) and their topological neighbors. Finally, potential DTI prediction was made, and the obtained samples were sent to the classifier for secondary classification. Results: The performance of Graph-DTI and all baseline methods was evaluated using the sums of the area under the precision-recall curve (AUPR) and the area under the receiver operating characteristic curve (AUC). The results indicated that Graph-DTI outperformed the baseline methods in both performance results. Conclusion: Compared with other baseline DTI prediction methods, the results showed that Graph-DTI had better prediction performance. Additionally, in this study, we effectively classified drugs corresponding to different targets and vice versa. The above findings showed that Graph-DTI provided a powerful tool for drug research, development, and repositioning. Graph- DTI can serve as a drug development and repositioning tool more effectively than previous studies that did not use heterogeneous network graph embedding.