Current computer-aided drug design最新文献

Introduction: Skin cancer is the most common type of cancer caused by the uncontrolled growth of abnormal cells in the epidermis and the outermost skin layer.

Aim: This study aimed to study the anti-skin cancer potential of [6]-Gingerol and 21 related structural analogs using in vitro and in silico studies.

Methods: The ethanolic crude extract of the selected plant was subjected to phytochemical and GC-MS analysis to confirm the presence of the [6]-gingerol. The anticancer activity of the extract was evaluated by MTT (3-[4, 5-dimethylthiazol-2-y]-2, 5-diphenyl tetrazolium bromide) assay using the A431 human skin adenocarcinoma cell line.

Results: The GC-MS analysis confirmed the presence of [6]-Gingerol compound, and its promising cytotoxicity IC₅₀ was found at 81.46 ug/ml in the MTT assay. Furthermore, the in silico studies used [6]-Gingerol and 21 structural analogs collected from the PubChem database to investigate the anticancer potential and drug-likeliness properties. Skin cancer protein, DDX3X, was selected as a target that regulates all stages of RNA metabolism. It was docked with 22 compounds, including [6]-Gingerol and 21 structural analogs. The potent lead molecule was selected based on the lowest binding energy value.

Conclusion: Thus, the [6]-Gingerol and its structure analogs could be used as lead molecules against skin cancer and future drug development process.

Background: Rheumatoid Arthritis (RA) is a chronic autoimmune disease that can lead to joint pain and disability, and seriously impact patients' quality of life. Strychni Semen combined with Atractylodes Macrocephala koidz (SA) have pronounced curative effect on RA, and there is no poisoning of Strychni Semen (SS). However, its pharmacological mechanisms are still unclear.

Objective: In this study, we aimed to investigate the pharmacological mechanisms of Strychni Semen combined with Atractylodes Macrocephala Koidz (SA) for the treatment of RA.

Methods: We used network pharmacology to screen the active components of SA and predict the targets and pathways involved. Results originating from network pharmacology were then verified by animal experiments.

Results: Network pharmacology identified 81 active ingredients and 141 targets of SA; 2640 disease- related genes were also identified. The core targets of SA for the treatment of RA included ALB, IL-6, TNF and IL-1β. A total of 354 gene ontology terms were identified by Gene ontology (GO) enrichment analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis results showed that SA was closely associated with TNF signaling pathways in the treatment of RA. Furthermore, according to the predicted results of network pharmacology, we established a rat model of Adjuvant Arthritis (AA) for in vivo experiments. Analysis showed that each treatment group led to an improvement in paw swelling, immune organ coefficient and synovial tissue morphology in AA rats to different degrees, inhibit the expression levels of IL-1β, TNF-α and IL-6, upregulated the levels of Fas, Bax and Caspase 3, down-regulated the expression levels of Fas-L, Bcl-2 and p53.

Conclusion: SA has an anti-RA effect, the mechanism underlying the therapeutic action of SA in AA rats was related to the regulation of apoptosis signaling pathways.

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: Prostate cancer is one of the most prevalent cancers in men, leading to the second most common cause of death in men. Despite the availability of multiple treatments, the prevalence of prostate cancer remains high. Steroidal antagonists are associated with poor bioavailability and side effects, while non-steroidal antagonists show serious side effects, such as gynecomastia. Therefore, there is a need for a potential candidate for the treatment of prostate cancer with better bioavailability, good therapeutic effects, and minimal side effects.

Objective: This current research work focused on identifying a novel non-steroidal androgen receptor antagonist through computational tools, such as docking and in silico ADMET analysis.

Methods: Molecules were designed based on a literature survey, followed by molecular docking of all designed compounds and ADMET analysis of the hit compounds.

Results: A library of 600 non-steroidal derivatives (cis and trans) was designed, and molecular docking was performed in the active site of the androgen receptor (PDBID: 1Z95) using Auto- Dock Vina 1.5.6. Docking studies resulted in 15 potent hits, which were then subjected to ADME analysis using SwissADME. ADME analysis predicted three compounds (SK-79, SK-109, and SK-169) with the best ADME profile and better bioavailability. Toxicity studies using Protox-II were performed on the three best compounds (SK-79, SK-109, and SK-169), which predicted ideal toxicity for these lead compounds.

Conclusion: This research work will provide ample opportunities to explore medicinal and computational research areas. It will facilitate the development of novel androgen receptor antagonists in future experimental studies.

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.

Objective: To explore the mechanism of Maiwei Dihuang decoction in the treatment of non-small cell lung cancer (NSCLC) by using network pharmacology and LC-MS technology.

Methods: The effective components in Maiwei Dihuang decoction were detected by liquid chromatography- mass spectrometry (LC-MS). Use the SuperPred database to collect the relevant targets of the active ingredients of Mai Wei Di Tang, and then collect the relevant targets of nonsmall cell lung cancer from GeneCards, DisgenNET and OMIM databases. On this basis, PPI network construction, GO enrichment analysis and KEGG pathway annotation analysis were carried out for target sites. Finally, AutoDock Vina is used for molecular docking.

Results: We further screened 16 effective Chinese herbal compounds through LC-MS combined with ADME level. On this basis, we obtained 77 core targets through protein interaction network analysis. Through GO, KEGG analysis and molecular docking results, we finally screened out the potential targets of Maiwei Dihuang Decoction for NSCLC: TP53, STAT3, MAPK3.

Conclusion: Maiwei Dihuang decoction may play a role in the treatment of NSCLC by coregulating TP53/STAT3/MAPK3 signal pathway.

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: Studies have indicated that Sinomenii Caulis (SC) has several physiological activities, such as anti-inflammatory, anti-cancer, immunosuppression, and so on. SC is currently widely used in the treatment of rheumatoid arthritis, skin disease, and other diseases. However, the mechanism of SC in the treatment of ulcerative colitis (UC) remains unclear.

Aims: To predict the active components of SC and determine the mechanism of SC on UC.

Methods: Active components and targets of SC were screened and obtained by TCMSP, PharmMapper, and CTD databases. The target genes of UC were searched from GEO (GSE9452), and DisGeNET databases. Based on the String database, Cytoscape 3.7.2 software, and David 6.7 database, we analyzed the relationship between SC active components and UC potential targets or pathways. Finally, identification of SC targets in anti-UC by molecular docking. GROMACS software was used to perform molecular dynamics simulations of protein and compound complexes and to perform free energy calculations.

Results: Six main active components, 61 potential anti-UC gene targets, and the top 5 targets with degree value are IL6, TNF, IL1β, CASP3, and SRC. According to GO enrichment analysis, the vascular endothelial growth factor receptor and vascular endothelial growth factor stimulus may be relevant biological processes implicated in the treatment of UC by SC. The KEGG pathway analysis result was mainly associated with the IL-17, AGE-RAGE, and TNF signaling pathways. Based on molecular docking results, beta-sitosterol, 16-epi-Isositsirikine, Sinomenine, and Stepholidine are strongly bound to the main targets. Molecular dynamics simulation results showed that IL1B/beta-sitosterol and TNF/16-epi-Isositsirikine binding was more stable.

Conclusion: SC can play a therapeutic role in UC through multiple components, targets, and pathways. The specific mechanism of action needs to be further explored.

Objective: This study aimed to analyze the potential targets and mechanism of the Tiannanxing-shengjiang drug pair in pain treatment using network pharmacology and molecular docking technology.

Methods: The active components and target proteins of Tiannanxing-Shengjiang were obtained from the TCMSP database. The pain-related genes were acquired from the DisGeNET database. The common target genes between Tiannanxing-Shengjiang and pain were identified and subjected to the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analyses on the DAVID website. AutoDockTools and molecular dynamics simulation analysis were used to assess the binding of the components with the target proteins.

Results: Ten active components were screened out, such as stigmasterol, β-sitosterol, and dihydrocapsaicin. A total of 63 common targets between the drug and pain were identified. GO analysis showed the targets to be mainly associated with biological processes, such as inflammatory response and forward regulation of the EKR1 and EKR2 cascade. KEGG analysis revealed 53 enriched pathways, including pain-related calcium signaling, cholinergic synaptic signaling, and serotonergic pathway. Five compounds and 7 target proteins showed good binding affinities. These data suggest that Tiannanxing-shengjiang may alleviate pain through specific targets and signaling pathways.

Conclusion: The active ingredients in Tiannanxing-shengjiang might alleviate pain by regulating genes, such as CNR1, ESR1, MAPK3, CYP3A4, JUN, and HDAC1 through the signaling pathways, including intracellular calcium ion conduction, cholinergic prominent signaling, and cancer signaling pathway.