Flubendazole suppresses VEGF-induced angiogenesis in HUVECs and exerts antitumor effects in PC-3 cells.

Flubendazole, an FDA-approved anthelmintic, has been predicted to show strong VEGFR2 inhibitory activity in silico screening combined with in vitro experimental validation, and it has shown anti-cancer effects on some human cancer cell lines, but little is known about the anti-angiogenesis effects and anti-prostate cancer effects. In this study, we analyzed the binding modes and kinetic analysis of flubendazole with VEGFR2 and first demonstrated that flubendazole suppressed VEGF-stimulated cell proliferation, wound-healing migration, cell invasion and tube formation of HUVEC cells, and decreased the phosphorylation of extracellular signal-regulated kinase and serine/threonine kinase Akt, which are the downstream proteins of VEGFR2 that are important for cell growth. What's more, our results showed that flubendazole decreased PC-3 cell viability and proliferation ability, and suppressed PC-3 cell wound healing migration and invasion across a Matrigel-coated Transwell membrane in a concentration-dependent manner. The antiproliferative effects of flubendazole were due to induction of G2-M phase cell cycle arrest in PC-3 cells with decreasing expression of the Cyclin D1 and induction of cell apoptosis with the number of apoptotic cells increased after flubendazole treatment. These results indicated that flubendazole could exert anti-angiogenic and anticancer effects by inhibiting cell cycle and inducing cell apoptosis.

Identification of multi-target anti-cancer agents from TCM formula by in silico prediction and in vitro validation.

Cancer is a complex disease associated with multiple gene mutations and malignant phenotypes, and multi-target drugs provide a promising therapy idea for the treatment of cancer. Natural products with abundant chemical structure types and rich pharmacological characteristics could be ideal sources for screening multi-target antineoplastic drugs. In this paper, 50 tumor-related targets were collected by searching the Therapeutic Target Database and Thomson Reuters Integrity database, and a multi-target anti-cancer prediction system based on mt-QSAR models was constructed by using naïve Bayesian and recursive partitioning algorithm for the first time. Through the multi-target anti-cancer prediction system, some dominant fragments that act on multiple tumor-related targets were analyzed, which could be helpful in designing multi-target anti-cancer drugs. Anti-cancer traditional Chinese medicine (TCM) and its natural products were collected to form a TCM formula-based natural products library, and the potential targets of the natural products in the library were predicted by multi-target anti-cancer prediction system. As a result, alkaloids, flavonoids and terpenoids were predicted to act on multiple tumor-related targets. The predicted targets of some representative compounds were verified according to literature review and most of the selected natural compounds were found to exert certain anti-cancer activity in vitro biological experiments. In conclusion, the multi-target anti-cancer prediction system is very effective and reliable, and it could be further used for elucidating the functional mechanism of anti-cancer TCM formula and screening for multi-target anti-cancer drugs. The anti-cancer natural compounds found in this paper will lay important information for further study.

Discovery of Multitarget-Directed Ligands Against Influenza A Virus From Compound Yizhihao Through a Predictive System for Compound-Protein Interactions.

Influenza A virus (IAV) is a threat to public health due to its high mutation rate and resistance to existing drugs. In this investigation, 15 targets selected from an influenza virus-host interaction network were successfully constructed as a multitarget virtual screening system for new drug discovery against IAV using Naïve Bayesian, recursive partitioning, and CDOCKER methods. The predictive accuracies of the models were evaluated using training sets and test sets. The system was then used to predict active constituents of Compound Yizhihao (CYZH), a Chinese medicinal compound used to treat influenza. Twenty-eight compounds with multitarget activities were selected for subsequent in vitro evaluation. Of the four compounds predicted to be active on neuraminidase (NA), chlorogenic acid, and orientin showed inhibitory activity in vitro. Linarin, sinensetin, cedar acid, isoliquiritigenin, sinigrin, luteolin, chlorogenic acid, orientin, epigoitrin, and rupestonic acid exhibited significant effects on TNF-α expression, which is almost consistent with predicted results. Results from a cytopathic effect (CPE) reduction assay revealed acacetin, indirubin, tryptanthrin, quercetin, luteolin, emodin, and apigenin had protective effects against wild-type strains of IAV. Quercetin, luteolin, and apigenin had good efficacy against resistant IAV strains in CPE reduction assays. Finally, with the aid of Gene Ontology biological process analysis, the potential mechanisms of CYZH action were revealed. In conclusion, a compound-protein interaction-prediction system was an efficient tool for the discovery of novel compounds against influenza, and the findings from CYZH provide important information for its usage and development.

Identification of Estrogen Receptor α Antagonists from Natural Products via In Vitro and In Silico Approaches.

Estrogen receptor α (ERα) is a successful target for ER-positive breast cancer and also reported to be relevant in many other diseases. Selective estrogen receptor modulators (SERMs) make a good therapeutic effect in clinic. Because of the drug resistance and side effects of current SERMs, the discovery of new SERMs is given more and more attention. Virtual screening is a validated method to high effectively to identify novel bioactive small molecules. Ligand-based machine learning methods and structure-based molecular docking were first performed for identification of ERα antagonist from in-house natural product library. Naive Bayesian and recursive partitioning models with two kinds of descriptors were built and validated based on training set, test set, and external test set and then were utilized for distinction of active and inactive compounds. Totally, 162 compounds were predicted as ER antagonists and were further evaluated by molecular docking. According to docking score, we selected 8 representative compounds for both ERα competitor assay and luciferase reporter gene assay. Genistein, daidzein, phloretin, ellagic acid, ursolic acid, (-)-epigallocatechin-3-gallate, kaempferol, and naringenin exhibited different levels for antagonistic activity against ERα. These studies validated the feasibility of machine learning methods for predicting bioactivities of ligands and provided better insight into the natural products acting as estrogen receptor modulator, which are important lead compounds for future new drug design.

Discovery of VEGFR2 inhibitors by integrating naïve Bayesian classification, molecular docking and drug screening approaches.

The high morbidity and mortality of cancer make it one of the leading causes of global death, thus it is an urgent need to develop effective drugs for cancer therapy. Vascular endothelial growth factor receptor-2 (VEGFR2) acts as a central modulator of angiogenesis, and is therefore an important pharmaceutical target for developing anti-angiogenic agents. In this study, ligand-based naïve Bayesian (NB) models and structure-based molecular docking were combined to develop a virtual screening (VS) pipeline for identifying potential VEGFR2 inhibitors from FDA-approved drugs. The best validated naïve Bayesian model (NB-c) gave Matthews correlation coefficients of 0.966 and 0.951 for the test set and external validation set, respectively. 1841 FDA-approved drugs were sequentially screened by the optimal model NB-c and molecular docking module LibDock. By analyzing the results of VS, 9 top ranked drugs with EstPGood value ≥ 0.6 and LibDock Score ≥ 120 were chosen for biological validation. VEGFR2 kinase assay results demonstrated that flubendazole, rilpivirine and papaverine showed VEGFR2 inhibitory activities with IC50 values ranging from 0.47 to 6.29 µM. Binding mode analysis with CDOCKER revealed the action mechanism of the 3 hit drugs binding to VEGFR2. In summary, we not only proposed an integrated VS pipeline for potential VEGFR2 inhibitors screening, but also identified 3 FDA-approved drugs as novel VEGFR2 inhibitors, which could be used to design and develop new antiangiogenic agents.

The Bioinformatic Analysis of the Dysregulated Genes and MicroRNAs in Entorhinal Cortex, Hippocampus, and Blood for Alzheimer's Disease.

AIM: The incidence of Alzheimer's disease (AD) has been increasing in recent years, but there exists no cure and the pathological mechanisms are not fully understood. This study aimed to find out the pathogenesis of learning and memory impairment, new biomarkers, potential therapeutic targets, and drugs for AD. METHODS: We downloaded the microarray data of entorhinal cortex (EC) and hippocampus (HIP) of AD and controls from Gene Expression Omnibus (GEO) database, and then the differentially expressed genes (DEGs) in EC and HIP regions were analyzed for functional and pathway enrichment. Furthermore, we utilized the DEGs to construct coexpression networks to identify hub genes and discover the small molecules which were capable of reversing the gene expression profile of AD. Finally, we also analyzed microarray and RNA-seq dataset of blood samples to find the biomarkers related to gene expression in brain. RESULTS: We found some functional hub genes, such as ErbB2, ErbB4, OCT3, MIF, CDK13, and GPI. According to GO and KEGG pathway enrichment, several pathways were significantly dysregulated in EC and HIP. CTSD and VCAM1 were dysregulated significantly in blood, EC, and HIP, which were potential biomarkers for AD. Target genes of four microRNAs had similar GO_terms distribution with DEGs in EC and HIP. In addtion, small molecules were screened out for AD treatment. CONCLUSION: These biological pathways and DEGs or hub genes will be useful to elucidate AD pathogenesis and identify novel biomarkers or drug targets for developing improved diagnostics and therapeutics against AD.