Combinatorial chemistry & high throughput screening最新文献

Objective: In search of efficient anticancer agents, we aimed at the design and synthesis of a library of tetrasubstituted alkenes. These are structural analogues of tamoxifen, one of the widely used anticancer therapeutics.

Methods: Our small organic compound library was prepared via a chemical synthesis in the solution using the Larock three-component coupling reaction, which is known to tolerate diverse functional groups. Further, we have integrated this synthetic approach to four- and fivecomponent alkene assembly by using Sonogashira coupling, A3 and AHA reactions. The final products were isolated through preparative LC/MS station and characterized by NMR, MS, and X-ray crystallography. The biological activity of all novel compounds was tested by luciferase reporter assays against estrogen receptor (ER) and androgen receptor (AR).

Results and discussion: Our combinatorial synthetic approach was based on structurally diverse internal alkynes, arylboronic acids and aryl halides. After experiment optimization a "one-pot" single synthetic procedure was developed. This allowed us to prepare a small-sized screening library of novel tetrasubstituted alkenes quickly and efficiently without laborious intermediate isolation. In most cases, we isolated the final product as a single isomer, and in selected cases, we confirmed their chemical structure via X-ray crystallography. High throughput screening of the novel tetrasubstituted alkenes revealed a dozen hits with predominant agonistic ERα- and antagonistic AR-activity in the micromolar range.

Conclusion: The proposed combinatorial approach is applicable for the synthesis of diversified organic compound libraries and for the discovery of new tamoxifen analogues with an improved therapeutic profile.

Introduction: Ovarian Cancer (OC) was known for its high mortality rate among gynecological malignancies, often resulting in a poor prognosis. This study sought to identify prognostic necroptosis-related long non-coding RNAs (lncRNAs) (NRlncRNAs) with prognostic potential and to construct a reliable risk prediction model for OC patients.

Method: The transcriptome and clinic data were sourced from TCGA and GTEx databases. Initially, NRlncRNAs were discovered by assessing gene correlations and evaluating differences in gene expression. Subsequently, Cox regression and LASSO methods were employed to develop the NRlncRNAs risk model, which was further validated through survival analysis, ROC curves, Cox regression, and nomograms across both the test and entire datasets.

Results: Multivariate Cox analysis revealed that the risk score based on 14 NRlncRNAs can independently predict the prognosis of OC. The low-risk group demonstrated significantly higher immune cell infiltration scores and lower tumor immune dysfunction, exclusion, and TIDE scores, as well as an increased number of neoantigens and higher TMB. Notably, the low-risk group also exhibited an elevated HRD score.

Conclusion: The model's predictive accuracy was further substantiated through ROC analysis, showing superior performance compared to many existing models.Finally, the expression levels of 14 NRlncRNAs were confirmed using the qRT-PCR in two OC cell lines. These findings suggested that the NRlncRNAs risk model could serve as a more precise indicator for forecasting immune response and outcomes of targeted treatments in OC.

Background: The prevalence of depression in COVID-19 patients is notably high, disrupting daily life routines and compounding the burden of other chronic health conditions. In addition, to elucidate the connection between COVID-19 and depression, we conducted an analysis of commonly differentially expressed genes [co-DEGs], uncovering potential biomarkers and therapeutic avenues specific to COVID-19-related depression.

Methods: We obtained gene expression profiles from the Gene Expression Omnibus [GEO] database with strategic keyword searches ["COVID-19", "depression," and "SARS"]. We used functional enrichment analysis of the co-DEGs to decipher their likely biological roles. Then, we utilized protein-protein interaction [PPI] network analysis to identify hub genes among the co- DEGs. These findings were validated via an independent third-party dataset.

Results: Our analysis of blood samples from COVID-19 patients revealed 10,716 upregulated genes and 10,319 downregulated genes. In addition, by applying the same approach to depression samples, we identified 571 upregulated and 847 downregulated genes. Furthermore, by intersecting these datasets, we extracted 121 upregulated and 175 downregulated co-DEGs. Through PPI network construction and hub gene selection, we identified MPO, ARG1, CD163, FCGR1A, ELANE, LCN2, and CR1 as co-upregulated hub genes and MRPL13, RPS23, and MRPL1 as co-downregulated hub genes. The incorporation of third-party datasets revealed that these hub genes are specific targets of SARS-CoV-2, not generic viral response mechanisms.

Conclusion: The identification of potential biomarkers represents a groundbreaking strategy for assessing and treating depression in the context of COVID-19, with the potential to reduce its prevalence among these patients. However, to fully harness this potential, additional clinical research is paramount.

The discovery of novel counteractive pharmaceuticals, which have recently generated much interest, has played a significant role in the development of drugs derived from herbal medicines or botanical sources. Paederia foetida (P. foetida) is one such example of a role in both traditional and traditional medicine. Owing to its popularity in folk medicine, it has been the subject of intense pharmacological and chemical studies for the last 30 years. They are locally known as "Gandhavadulia" or "GandhaPrasarini" (English name "skunkvine") and belong to the family Rubiaceae. This plant has a broad range of applications in the treatment of several illnesses, including anti-inflammatory, antinociceptive, antitussive, thrombolytic, antidiabetic, antihyperlipidaemic, antioxidant, nephroprotective, anti-inflammatory, hepatoprotective, anthelmintic, and antidiarrheal effects. Additionally, it contains a variety of phytochemicals, such as asperuloside, paederosidic acid, sitosterols, campesterol, lignans, alkaloids, volatile oils, iridoids, methylindooxy substances, stigmasterol, tannins, triterpenoids, ellagic acid, ursolic acid, epifriedelinol, and phenolic compounds. Recently, mounting data on numerous active ingredients have indicated that they may be beneficial for spermatogenesis, wound healing, inflammatory illnesses, and cancer. This review describes the phytochemistry and mechanisms of action of these pharmacological effects and provides insights into potential pharmacological targets. These results highlight the need for additional studies on this medicinal plant and the investigation of new counteractive medications to determine their mode of action before use in healthcare.

Background: Stomach adenocarcinoma (STAD) is the fifth most common tumor worldwide, imposing a significant disease burden on populations, particularly in Asia. Oxidative stress is well-known to play an essential role in the occurrence and progression of malignancies. Our study aimed to construct a prediction model by exploring the correlation between oxidative stress-related genes and the prognosis of patients with STAD.

Method: STAD data from TCGA were used to identify the differentially expressed oxidative stress-related genes (OSGs), with data from GEO serving as the validation cohort. Univariate Cox and LASSO regression analyses were performed to select prognosis-related genes for the risk model, which was then integrated with clinical features into a nomogram. The physiological functions and pathways of these identified genes were explored using GO and KEGG analyses. After evaluating the prediction value of the prognostic model in the GEO cohort, drug sensitivity and immune infiltration were comprehensively analyzed using R. Expression levels of the prognostic genes were verified by quantitative real-time PCR in gastric cancer and paired normal tissues.

Results: Cox regression and LASSO regression analysis identified SERPINE1, VHL, CD36, NOS3, ANXA5, ADCYAP1, POLRMT and GPX3 as the signature genes from 160 differentially expressed OSGs. Both Kaplan-Meier survival analysis and ROC curve at 5 years in the TCGA and the GEO cohort exhibited great predictive ability of the prognostic model, with the AUC >0.7 in TCGA. Validated as an independent risk factor, the model was integrated with clinicopathological variables (including age, stage, and gender) to build a nomogram for more accurate risk stratification. Moreover, therapy sensitivity analysis between the low- and high-risk categories showed that those who scored higher would benefit more from BEZ235, Dasatinib, Pazopanib, and Saracatinib. Meanwhile, differences in the tumor environment, immune infiltration and response to immunotherapy between the two groups were noted. Finally, qRT-PCR validated the differential expression of these genes in STAD and paired normal tissues.

Conclusion: Our study has effectively established an oxidative stress-related prognostic model, providing a promising tool for personalized clinical strategies and improved STAD patient outcomes.

Objective: This study aimed to explore the active components and potential mechanism of Tanre Qing Injection (TRQI) in the treatment of Acute Respiratory Distress Syndrome (ARDS) using network pharmacology, molecular docking, and animal experiments.

Methods: The targets of active ingredients were identified using the TCMSP and Swiss Target Prediction databases. The targets associated with ARDS were obtained from the GeneCards database, Mala card database, and Open Targets Platform. A Protein-protein Interaction network (PPI) was constructed, and the core targets were subjected to Gene Ontology (GO) function annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Finally, molecular docking technology and a mouse model of lipopolysaccharide-induced acute lung injury validated the experimental results.

Results: The results of network pharmacology showed the active components of TRQI in the treatment of ARDS to be baicalin, chenodeoxycholic acid, oroxylin-A, and ursodeoxycholic acid, and the core targets to be TP53, ESR1, AKT1, JUN, and SRC. KEGG analysis showed 181 signaling pathways, primarily including the IL-17 signaling pathway, endocrine resistance, lipid metabolism, and atherosclerosis. Molecular docking results demonstrated that baicalin, chenodeoxycholic acid, oroxylin-A, and ursodeoxycholic acid in TRQI exhibited the strongest affinity for TP53, ESR1, and SRC. Furthermore, the results of animal experiments have indicated TRQI to have a significant inhibitory effect on inflammatory factors TNF-α, IL-1β, and IL-6, and effectively alleviate the pathological damage of ARDS to lung tissue.

Conclusion: TRQI may exert its therapeutic effects on ARDS through multiple targets and pathways, providing a research basis for its clinical application and further development.

Background and aim: As a classical formula to invigorate blood circulation, Huoxue Tongluo Qiwei Decoction (HTQD) can effectively treat hypertensive erectile dysfunction (ED), but its exact mechanism of action is not yet clear. The goal of this research was to explore the potential mechanism of HTQD in improving hypertensive erectile dysfunction in rats through transcriptomics, network pharmacology, and associated animal experimentations.

Methods: The HTQD chemical constituents were screened using high-performance liquid chromatography- tandem mass spectrometry (HPLC-MS/MS). Furthermore, transcriptomics analysis was performed via mRNA sequencing to identify significantly differentially expressed proteins. Moreover, the key target proteins of HTQD in the treatment of hypertensive ED were screened by network pharmacology and transcriptomics. In addition, the endothelial cells of the corpus cavernosum were assessed using hematoxylin-eosin staining. The transcript and protein expressions were evaluated via western blotting and Real-time reverse transcription-quantitative polymerase chain reaction (RT-qPCR).

Results: The network pharmacology and transcriptome mRNA sequencing revealed that KCNE1 may be the target protein of HTQD in improving hypertensive ED. After HTQD treatment, the systolic and diastolic blood pressure (BP) of hypertensive rats decreased, the number of erections increased, and the pathological structure of the penis was improved. Moreover, HTQD downregulated the protein and mRNA expression of AngII, AT1R, DAG, and PKCε, whereas it upregulated the transcript and protein expression of KCNE1.

Conclusion: HTQD may activate the PKCε pathway through AngII, inhibit the expression of KCNE1 protein, relax vascular smooth muscles, and improve erectile function.

Introduction: Hedychium coronarium J. König, from the Zingiberaceae family, is a rhizomatous herb used in Ayurvedic medicine for its febrifuge, anti-rheumatic, and anthelmintic properties.

Method: This study characterizes the chemical diversity and biological activities of H. coronarium oleoresins collected from four locations in Uttarakhand: Pantnagar (HCPNOR), Bageshwar (HCBOR), Nainital (HCNOR) and Pithoragarh (HCPOR). GC-MS analysis identified key constituents, including n-Hexadecanoic acid (8.3-9.8%), photocitral B (4.8- 27.6%), (Z)-9- eicosenoic acid (0.7-6.5%), α-pinene (2.5-3.5%) and trans-13-octadecenoic acid (7.5%). Heat map clustering, Venn diagrams, and PCA revealed compositional variations.

Result: The oleoresins showed potent nematicidal, insecticidal, herbicidal, and antifungal activities against Meloidogyne incognita, Spodoptera litura, Raphanus raphanistrum subsp. sativus, Fusarium oxysporum, and Curvularia lunata.

Conclusion: These findings highlight the potential of H. coronarium oleoresins for pest and pathogen management, with chemical variation driven by environmental factors.

Objective: The objective of this study is to analyze and identify the main chemical components and blood-absorbed components of Xuantu Granules and predict their pharmacological substance basis and mechanism in the treatment of DKD.

Methods: A DKD rat model was established by feeding SD rats a high-fat and high-sugar diet and administering intraperitoneal injections of streptozotocin (STZ). The therapeutic effect of Xuantu granules was evaluated. Drug-containing serum was prepared after gavage, and the major chemical components of Xuantu Granules and the drug-containing serum were detected using UHPLC-Q-Exactive-HRMS. Blood-absorbed components were identified based on retention time, mass-to-charge ratio, and MS/MS spectrum. Blood-absorbed components' target proteins were searched using the CTD, SwissTarget, BindingDB, and TargetNet databases. DKD disease target genes were screened from the GEO database using WGCNA. A "bioactive blood-absorbed component-target-disease" PPI network was constructed using Cytoscape software, and the key clustering subnetworks were identified by MCODE plugin. GO functional analysis and KEGG pathway enrichment analysis were performed on subnetworks.

Results: Xuantu Granules lowered fasting blood glucose, improved renal function, reduced proteinuria, and improved renal tissue pathological changes in DKD rats. 36 chemical components were identified, among which 12 compounds, including β -Carboline-1-propionic acid, Morin, Afzelin, Schizandrin, Gomisin A were identified as blood-absorbed components. Bioinformatics analysis indicated that AKT1, TNF, TP53, IL6, SRC, IL1B, EGFR, JUN, BCL2, and CASP3 might be the main therapeutic targets. The involved pathways included the IL-17 signaling pathway, PI3K-Akt signaling pathway, AGE-RAGE signaling pathway in diabetic complications and so on.

Conclusion: Xuantu Granules may exert therapeutic effects on DKD through multiple targets and pathways.

Aims: This study aims explore the impact of catechol, dopamine, and L-DOPA on the stability and toxicity of β-amyloid peptides, which play a key role in the neurodegenerative process of Alzheimer's disease, to assess their potential as therapeutic agents.

Background: Alzheimer's disease is marked by the aggregation of β-amyloid peptides, which contribute to neurodegeneration. Exploring how various compounds interact with β-amyloid peptides can offer valuable insights into potential therapeutic strategies.

Objective: The objective of this research is to explore the interaction mechanisms of catechol, dopamine, and L-DOPA with β-amyloid peptides and assess their impact on peptide stability and aggregation.

Method: This study employs molecular dynamics simulations combined with density functional theory to investigate the interactions between β-amyloid and the three compounds. It evaluates changes in peptide stability and salt bridge lengths and performs electronic structure analyses using the Electron Localization Function (ELF) and Harmonic Oscillator Model of Aromaticity (HOMA).

Results: The findings reveal that β-amyloid stability decreases significantly when interacting with dopamine and L-DOPA compared to catechol. All three compounds inhibit β-amyloid, with dopamine and L-DOPA showing stronger effects. Catechol primarily interacts through hydrophobic interactions, while dopamine and L-DOPA also form hydrogen bonds with β-amyloid. Electronic structure analysis shows catechol has higher electron localization and anti-aromatic character, affecting its interactions differently than dopamine and L-DOPA. A decrease in the HOMO-LUMO gap from catechol to L-DOPA to dopamine indicates increasing reactivity towards β-amyloid.

Conclusion: Dopamine and L-DOPA more effectively disrupt β-amyloid aggregation than catechol, likely due to additional hydrogen bonding and increased electronic reactivity. These insights are crucial for developing therapeutic strategies targeting β-amyloid aggregation in Alzheimer's disease, emphasizing the importance of molecular interactions in modulating peptide stability and toxicity. The study also provides a comparative analysis of the electronic properties and interaction dynamics of the compounds, which can guide future research in the design of β-amyloid inhibitors. The utilization of advanced simulation techniques underscores the potential for computational methods in understanding complex biological interactions and developing novel therapeutic agents. Furthermore, the insights into the differential effects of hydrophobic interactions versus hydrogen bonding offer valuable information for the synthesis of new compounds aimed at mitigating β-amyloid toxicity.