Combinatorial chemistry & high throughput screening最新文献

Aims: Organic thiocyanates are important pharmaceutical intermediates. This study aimed to develop a selective and efficient approach for synthesizing organic thiocyanates.

Methods: Under mild reaction conditions, an array of alkenes, KSCN, and diaryliodonium salts are considered good substrates, providing various aryl-substituted alkylthiocyanates with modest to excellent yield. Radical trapping and nucleophilic trapping experiments were carried out to explore the mechanistic pathways. The experiments indicated the involvement of free-radical and carbenium ion intermediate processes. Diaryliodonium salts were used as the radical arylating reagent, and KSCN was the electrophilic cyanating reagent. Under irradiation, the excited photocatalyst reduced aryldiazonium salt to aryl radical. Then, the radical was added to olefin to generate a new radical. Finally, the generated radical was further oxidized and arrested by SCN anion.

Conclusion: This coupling reaction provides a straightforward and practical route to construct various aryl-substituted alkylthiocyanates.

Background: HJ11 (HJ11 decoction), which is based on the traditional prescription Si-Miao-Yong-An decoction, has exerted a remarkable effect on atherosclerosis (AS). Nevertheless, the main components and underlying mechanisms of HJ11 for treating AS remain unclear.

Aim of the study: This study was designed to elucidate the mechanism of HJ11 in the treatment of AS through network pharmacology and in vivo experimental validation.

Methods: Network pharmacology was employed to explore the primary bioactive components and targets of HJ11. AS-related genes were obtained from the GeneCards and DisGeNET databases and screened for intersections with HJ11. A herb-compound-target interaction network was constructed by Cytoscape 3.9.1, and molecular docking analyses were constructed on key targets. By using a mouse model, the mechanism of action of HJ11 was further confirmed.

Results: A total of 231 active components of HJ11, 1681 AS-related genes, and 156 common targets were identified. Through the establishment of numerous networks, it was discovered that the main association of the mechanism of HJ11 in AS therapy pertained to anti-inflammation. Important substances included quercetin, kaempferol, and luteolin, while TNF-α, AKT1, IL-6, and VEGFA were the main targets. Molecular docking demonstrated that there were favorable binding interactions between active drugs (quercetin, kaempferol, and luteolin) and targets (TNF-α, AKT1, IL-6, and VEGFA). In the in vivo study, HJ11 reduced the expression of TNF-α, AKT1, IL-6, and VEGFA at both the mRNA and protein levels, inhibited atherosclerotic lesions in AS mouse models, and retarded the development of retroarterioid sclerosis.

Conclusions: HJ11 can inhibit inflammation and the progression of AS, and the mechanism might involve downregulating the expression of TNF-α, AKT1, IL-6, and VEGFA.

Background: GBM is an aggressive brain tumor with limited treatment options. Prior research has indicated FOLR1 as a pivotal gene involved in cancer pathogenesis.

Aim: This study aimed to explore the involvement of folate receptor alpha (FOLR1) in glioblastoma (GBM) and evaluate its potential as a therapeutic target.

Objective: This study investigated the expression pattern of FOLR1 in GBM, its impact on patient prognosis, and its role in GBM cell growth and the SRC/ERK1/2 signaling axis.

Methods: Initially, we conducted an expression analysis of FOLR1 based on public databases and examined its expression pattern in GBM and its impact on patient prognosis. Subsequently, cell experiments were carried out to evaluate the regulation of GBM cells by differential FOLR1 expression. We then downloaded 100 FOLR1 co-expressed genes from the Linkedomics data repository and performed an enrichment analysis. Finally, the role of FOLR1 and SRC/ERK1/2 axis in GBM was analyzed again by cell experiments.

Results: FOLR1 was found to be substantially expressed in GBM patients and was linked to a poor prognosis. Cell experiments showed that overexpression of FOLR1 promoted GBM cell growth, while low expression of FOLR1 inhibited cell growth. Additionally, genes related to FOLR1 were enriched in the lysosome, toxoplasmosis, and other pathways. This study further indicated that FOLR1 facilitates the activation of the SRC/ERK1/2 signaling pathway in GBM cells, and the attenuation of these pathways can effectively impede the malignancy-promoting effects triggered by FOLR1 in GBM cells.

Conclusions: We revealed that FOLR1 orchestrates the malignant advancement of GBM by stimulating the SRC/ERK1/2 signaling axis, underscoring its pivotal role in the pathogenesis of GBM.

Aim: The goal of this research was to formulate mucoadhesive gels using hydroglyceric extracts of Cistus creticus L. and Inula viscosa (L.) Aiton, either separately or in combination, utilizes carboxymethyl cellulose and detects their physicochemical characteristics and safety for oromucosal cells and antimicrobial (antibacterial, antifungal, and antiviral) efficacy to assess their performance.

Methods: Using LC-HRMS, the extracts of C. creticus and I. viscosa were examined. Evaluations were conducted on the formulations' viscosity, cytotoxicity-cell proliferation controls, texture, antibacterial activity, pH, and organoleptic properties. The minimal inhibitory concentrations and microbroth dilution tests were used to assess the effectiveness of the formulations.

Results: The pH, organoleptic, and physical characteristics of each formulation have been determined to be appropriate. The research results demonstrated that I. viscosa contributed antiviral efficacy to the formulations linked to dose-dependent activities against all examined mouth pathogens, whereas C. creticus provided antibacterial and antifungal efficacy. The formulation containing C. creticus extract alone was the most cytotoxic, whereas the formulation including I. viscosa extract alone was the least cytotoxic against gingival fibroblast cells, according to the findings of tests on cell proliferation and cytotoxicity.

Conclusion: The formulation contained a 32% 1:1 mixture of I. viscosa and C. creticus hydroglyceric extracts was detected as safe with acceptable cytotoxicity along with antibacterial and antiviral effectiveness, were encouraging for future investigations.

Background: Postmenopausal Osteoporosis (PMOP) is characterized by decreased bone mass and deterioration of bone microarchitecture, leading to increased fracture risk. Current treatments often have adverse effects, necessitating safer alternatives. Kaempferol, a flavonoid identified as a key active component of the traditional Chinese medicine Yishen Gushu formula, has shown promise in improving bone health, but its mechanisms in PMOP treatment remain unclear.

Objective: The aim of this study was to investigate the therapeutic effects and underlying mechanisms of kaempferol in the treatment of PMOP.

Methods: A bilateral ovariectomized (OVX) rat model was established to simulate PMOP. Sixty female Sprague-Dawley rats were divided into six groups: Sham operation, OVX control, OVX with alendronate (ALN), and OVX with kaempferol at doses of 10, 20, and 40 mg/kg. Treatments were administered orally once daily for 12 weeks. Assessments included Bone Mineral Density (BMD), trabecular microarchitecture via histopathology, organ morphology, organ indices, and serum levels of bone metabolism markers (TRACP-5b, BALP, ALP, Ca, P, and Fe) as well as liver and kidney function indicators (ALT, AST, CREA, and urea). Tandem Mass Tag (TMT) quantitative proteomics and bioinformatics analyses were conducted on femur samples to identify differentially expressed proteins (DEPs). Key DEPs were validated using parallel reaction monitoring (PRM), immunohistochemistry, and molecular docking.

Results: Kaempferol significantly improved BMD and enhanced trabecular microarchitecture in OVX rats in a dose-dependent manner, comparable to ALN, without causing hepatic, renal, or estrogen- like side effects. Serum bone metabolism markers were normalized with kaempferol treatment. Proteomic analysis identified 902 DEPs associated with kaempferol treatment, involved in processes such as bone remodeling, skeletal system development, and osteoclast function. Key signaling pathways affected included NF-κB, PI3K-AKT, and HIF-1. Notably, kaempferol downregulated five key DEPs-Rac2, Ddb1, Cdc42, Rpl19, and Hist2h4-in the femur, which are crucial for osteoclast resorptive activity, migration, adhesion, and cell cycle progression.

Conclusion: Kaempferol exerts therapeutic effects on PMOP by inhibiting key proteins involved in osteoclast function, thereby reducing bone resorption and promoting bone health. These findings suggested that kaempferol is a potential safer alternative for PMOP treatment. Further research is recommended to explore its clinical applications and elucidate detailed mechanisms.

Background: Thermotoga maritima is an anaerobic hyperthermophilic eubacterium isolated from geothermally heated maritime surfaces. It can grow at temperatures up to 80 degrees Celsius.

Methods: A 2.3-L bioreactor was specifically designed to cultivate hyperthermophilic bacteria under carefully regulated pH, redox potential, temperature, and dissolved O2.

Results: Using this bioreactor, which was adjusted at 80°C and pH 7.0, it was found that Thermotoga maritima demonstrated continued growth even after being exposed to oxygen for an extended period. Transcription studies revealed that following prolonged oxygen exposure, the genes encoding ROS-scavenging systems, alkyl hydroperoxide reductase (ahp), thioredoxindependent thiol peroxidase (bcp 2), and, to a lesser extent, neelaredoxin (nlr), were upregulated/ overexpressed. When oxygen was available, the metabolism of glucose was diverted to make lactate rather than acetate.

Conclusion: Based on the O/R ratio of 1.0 in anaerobiosis and 1.67 in the presence of O2, we may conclude that Thermotoga maritima is capable of semi-oxidative metabolism.

Computer-Aided Drug Design (CADD) entails designing molecules that could potentially interact with a specific biomolecular target and promising their potential binding. The stereo- arrangement and stereo-selectivity of small molecules (SMs)--based chemotherapeutic agents significantly influence their therapeutic potential and enhance their therapeutic advantages. CADD has been a well-established field for decades, but recent years have observed a significant shift toward acceptance of computational approaches in both academia and the pharmaceutical industry. Recently, artificial intelligence (AI), bioinformatics, and data science have played a significant role in drug discovery to accelerate the development of effective treatments, reduce expenses, and eliminate the need for animal testing. This shift can be attributed to the availability of extensive data on molecular properties, binding to therapeutic targets, and their 3D structures. Increasing interest from legislators, pharmaceutical companies, and academic and industrial scientists is evidence that AI is reshaping the drug discovery industry. To achieve success in drug discovery, it is necessary to optimize pharmacodynamic, pharmacokinetic, and clinical outcome-related properties. Moreover, the advent of on-demand virtual libraries containing billions of drug-like SMs, coupled with abundant computing capacities, has further facilitated this transition. To fully capitalize on these resources, rapid computational methods are needed for effective ligand screening. This includes structure-based virtual screening (SBVS) of vast chemical spaces, aided by fast iterative screening approaches. At the same time, advances in deep learning (DL) predictions of ligand properties and target activities have become very helpful, as they no longer need information about the structure of the receptor. This study examines recent progress in the drug discovery and development (DDD) approach, their potential to reshape the entire DDD process, and the challenges they face. This review examines the role of artificial intelligence as a fundamental component in drug discovery, particularly focusing on small molecules. It also discusses how AI-driven approaches can expedite the identification of diverse, potent, target-specific, and drug-like ligands for protein targets. This advancement has the potential to make drug discovery more efficient and cost-effective, ultimately facilitating the development of safer and more effective therapeutics.