Current medicinal chemistry最新文献

This article titled "Construction of a New Ferroptosis-related Prognosis Model for Survival Prediction in Colorectal Cancer", published in Volume 32, Issue 20, 2025 of Current Medicinal Chemistry (10.2174/0109298673296767240116215814). This article has been retracted at the request of the corresponding author due to an unresolved authorship dispute. In the interest of maintaining the integrity of the scientific record, the article has been retracted. The publisher apologizes to the readers for any inconvenience caused. The Bentham Editorial Policy on Retraction can be found at https://benthamscience.com/editorial-policies-main.php. BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure, or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication, the authors agree that the publishers have the legal right to take appropriate action against the authors if plagiarism or fabricated information is discovered. By submitting a manuscript, the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

Introduction: Neutrophils play a key role in host immune defense. At present, neutrophils in chronic bone infections exhibit significant heterogeneity but functional alterations that remain poorly understood.

Materials and methods: A rat model of chronic bone infection induced by Methicillin- Resistant Staphylococcus Aureus (MRSA) was established. Bone marrow cells were analyzed using scRNA-seq with Gene Ontology (GO) and pathway enrichment analysis. Differentially Expressed Genes (DEGs) were identified to assess neutrophil dysfunction, validated by immunofluorescence staining and ROS quantification.

Results: MRSA-induced chronic bone infection was confirmed by Gram and H&E staining, which showed bacterial colonization and inflammation. Neutrophils from infected rats showed downregulated immune-related genes (e.g., Clec7a, Ccr5) and upregulated immunosuppressive factors (e.g., Nfkbia, IL10ra). Enrichment analysis showed that immune responses and neutrophil functions were inhibited. Immunofluorescence showed neutrophil polarization towards N2 phenotype and reduced Reactive Oxygen Species (ROS) production in the infection group.

Discussion: This study established a rat model of MRSA-induced chronic bone infection and identified 7 neutrophil subsets via scRNA-seq analysis, with the NeuP2ry10 subset showing the most significant changes. Neutrophils displayed decreased chemotaxis, phagocytosis, and ROS production, along with elevated anti-inflammatory gene expression, suggesting functional suppression and a shift toward an immunosuppressive state.

Conclusion: Chronic bone infection drives neutrophil polarization toward an N2 anti-inflammatory phenotype, reducing antimicrobial capacity and promoting infection persistence. Targeting neutrophil function may offer new therapeutic strategies for chronic bone infection.

Breast cancer is one of the most prevalent and life-threatening cancers globally. For the treatment of breast cancer, the exploration of novel therapeutic strategies is essential. Recent advances in herbal drug discovery have garnered significant attention as a promising alternative or adjunct to conventional cancer treatments. This review highlights the emerging role of phytoconstituents derived from medicinal plants, different polyherbal formulations, etc., in breast cancer management. It delves into the bioactive compounds with anticancer properties and their mechanisms of action. Herbal drugs modulate pro- and anti-apoptotic proteins and pro-autophagy molecules to promote programmed cell death, autophagy, and suppress the survivability of breast cancer cells. These mechanisms include modulation of cell signaling pathways, induction of apoptosis, inhibition of metastasis, and regulation of the tumor microenvironment. Some polyherbal formulations are reported to have anti-cancerous activity in breast cancer. Many USFDA- approved drug creations from herbal products are available in markets that are used to treat breast cancer with chemotherapy. Furthermore, the review explores the synergistic effects of these phytochemicals when combined with traditional chemotherapy and the potential for reducing the adverse side effects associated with conventional cancer therapies. This article also discusses the challenges and future directions in translating these findings into clinical applications, emphasizing the need for more rigorous clinical trials to validate the efficacy and safety of herbal-based therapies for breast cancer.

Recently, ultrasound (US)-assisted tumor immunotherapy has attracted widespread attention due to its deep penetration as well as its non-invasive and non-ionizing radiation properties. In this review, we briefly elucidated the mechanisms of anti-tumor immunotherapy assisted by the US. The contents include the following: the mechanical effects, thermal effects, and cavitation effects of US, sonodynamic therapy, US combined with programmed cell death protein 1 / programmed cell death 1 ligand 1 antibodies, US-enhanced chimeric antigen receptor T cell immunotherapy, cell pyroptosis and US, US combined with radiotherapy, US combined with glycolysis inhibition, and the use of various US-modulated signaling pathways to enhance tumor immunity. This review provides a broad overview of the mechanisms and roles of US assistance in tumor immunotherapy.

The failure of a potential drug in the clinical study phase represents a significant cost and a loss of time for the process. Therefore, the development of strategies that can increase efficiency, streamline efforts, and lower expenses has become one of the main goals in drug development. In this context, computer-aided drug design (CADD) and the advent of artificial intelligence (AI) have played a pivotal role in driving significant advancements in these processes. Once a bioactive compound is identified, structural optimization to achieve a pharmacokinetic profile compatible with good oral bioavailability becomes indispensable. Scaffold hopping, a time-honored strategy in medicinal chemistry, has witnessed a resurgence in popularity with the advent of artificial intelligence (AI). The combination of traditional techniques with AI models, primarily based on deep learning (DL), has increased the success of these strategies. In this review, we aim to compile articles from recent literature published in the Web of Science, PubMed, and Google Scholar databases. After removing duplicate data, we analyzed the results that identified new bioactive compounds using AI tools. The generation of pharmacophore models, molecules formed by fragment linking, ring modification, and molecular recombination, as well as compounds acting on various targets with the aid of software powered by various types of AI, has shown promising results. However, challenges persist, including issues related to the quality of input data, the interpretation and interpretability of results, regulatory matters, investments in technology, and the formation and training of multidisciplinary teams. Overall, scaffold hopping combined with AI represents a powerful approach to expedite drug discovery and facilitate the development of innovative therapeutic agents with improved efficacy and safety profiles. A general discussion of AI models in the pharmaceutical industry is also presented.

Introduction: Tic disorders are neuropsychiatric conditions characterized by involuntary motor or vocal tics; however, the mechanisms underlying these disorders and potential therapeutic targets remain unknown. Therefore, this study investigated the mechanisms underlying tic disorders, particularly focusing on the role of mitochondrial energy metabolism, and identified potential targets of traditional Chinese medicine for these disorders.

Methods: Mitochondrial energy metabolism-related genes were retrieved from Gene- Cards and relevant literature. Additionally, Wangwei powder components and their potential targets were obtained from the TCMSP, HERB, and PubChem databases. Bioinformatic analysis was employed to identify key genes and mechanisms involved in tic disorders.

Results: Notably, 210 target genes of Wangwei powder, 365 mitochondrial energy metabolism-related genes, and 2020 differentially expressed genes in the tic disorder vs. control groups were identified. Based on the intersections of the differentially expressed genes, mitochondrial energy metabolism-related genes, and target genes, aldehyde dehydrogenase 2 (Aldh2), acetyl-CoA acetyltransferase 1 (Acat1), aldehyde dehydrogenase 1a1 (Aldh1a1), and adenylate kinase 2 (Ak2) were identified as key genes in tic disorder pathophysiology. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that the key genes were mainly involved in liver development, cellular detoxification of aldehydes, pyruvate metabolism, and fatty acid degradation pathways. Additionally, immune infiltration analysis highlighted notable discrepancies in immune cell populations between the tic disorder and control groups.

Discussion: Aldh2, Acat1, Aldh1a1, and Ak2 demonstrate potential as therapeutic targets for TD in WWS. The role of Acat1 in immune modulation and disease progression highlights its promise for immunotherapy. However, further experimental validation is needed to address study limitations.

Conclusion: The results indicate that the key genes (Aldh2, Acat1, Aldh1a1, and Ak2) play a crucial role in the pathogenesis of tic disorders through metabolic pathways and immune cell regulation.

Introduction: The objective of the present study was to explore the bi-directional causal relationship between IPF and diabetes (type 1 diabetes and type 2 diabetes)/ diabetic nephropathy/glycemic traits [fasting glucose and glycated hemoglobin (HbA1c)]/fasting blood insulin through MR analysis.

Methods: A bi-directional two-sample Mendelian randomization (MR) study design was adopted to evaluate the causal relationship between IPF and diabetes (type 1 diabetes and type 2 diabetes), diabetic complications (diabetic nephropathy) and glycemic traits [fasting blood glucose, glycated hemoglobin (HbA1c), fasting insulin] in a European population. Genome-wide association study summary data was obtained. The inverse variance weighted (IVW) method with a fixed-effects model was used to estimate the primary causal effects. The causal effects are represented by reporting odds ratios (OR) and their corresponding 95% confidence intervals (CI). The robustness of results was assessed using the MR-Egger and Weighted Median methods.

Results: In the forward MR analysis, the IVW method revealed a significant causal effect of IPF on type 2 diabetes (OR=1.031, 95% CI: 1.011-1.052). Similar estimates were obtained through the Weighted Median method. However, no significant causal effects were observed on type 1 diabetes, diabetic nephropathy, fasting blood glucose, HbA1c, and fasting insulin, respectively (p>0.05). We performed the reverse MR analysis using similar methods to the forward MR approach. MR analysis only showed a significant causal association of fasting insulin with IPF risk, with an OR of 3.576 (95% CI: 1.958-6.531).

Discussion: Genetically determined IPF was linked to an elevated risk of type 2 diabetes. The inverse MR analysis indicated that there was no causal impact of genetically predicted type 2 diabetes on the IPF risk.

Conclusion: Genetically predicted fasting blood insulin was found to be positively associated with IPF risk.

Introduction: Osteoporosis (OP) and aortic stenosis (AS) are highly prevalent age-related disorders that frequently coexist. Epidemiological studies suggest a pathological link between OP and AS beyond age, yet the molecular mechanisms underlying this bone-vascular axis remain poorly defined. This study aimed to identify shared genes and pathways contributing to the comorbidity of OP and AS.

Methods: Publicly available AS and OP transcriptomic datasets were retrieved from the GEO database. Weighted gene co-expression network analysis (WGCNA) and differential gene expression (DEG) analysis were conducted to identify disease-associated genes. Candidate hub genes were screened through protein-protein interaction (PPI) network analysis using twelve network topology algorithms. High-confidence genes were obtained by intersecting candidates with AS-related genes from the Comparative Toxicogenomics Database (CTD). Independent cohorts were used to validate candidate genes, and least absolute shrinkage and selection operator (LASSO) regression was performed to assess their diagnostic potential.

Results: WGCNA revealed 665 shared genes enriched in immune and inflammatory processes, cell adhesion, and glycosaminoglycan biosynthesis. PPI network analysis identified 32 candidate hub genes, and integration with CTD yielded 15 high-confidence genes. Validation across independent datasets confirmed dysregulated expression of CD4, GZMB, and SDC1 in both AS and OP samples. ROC analysis demonstrated high diagnostic accuracy of these genes, with a combined AUC of 0.94.

Discussion: These findings highlight immune and inflammatory pathways as convergent mechanisms driving both AS and OP. The hub genes CD4, GZMB, and SDC1 participate in immune regulation and extracellular matrix remodeling, suggesting their involvement in the shared pathogenesis of skeletal and cardiovascular degeneration.

Conclusion: Integrative bioinformatics identified CD4, GZMB, and SDC1 as key genes linking OP and AS, providing potential biomarkers and therapeutic targets for managing these age-related comorbidities.

Introduction: Follicular lymphoma (FL) is the most prevalent form of indolent lymphoma, characterized by intermittent relapse and remission periods. This study aims to identify potential biomarker genes for FL and elucidate their roles in the disease.

Methods: FL-related microarray datasets were downloaded from the Gene Expression Omnibus database. Differential expression and weighted gene co-expression network analyses (WGCNA) were conducted to identify potential hub genes. Various machine learning algorithms were applied to improve gene selection accuracy and predictive performance. Mendelian randomization (MR) analysis was carried out to identify genes with causal relationships to FL. Functional enrichment analysis was performed to explore the underlying mechanisms. Finally, the identified biomarker gene was validated in clinical samples using quantitative real-time PCR.

Results: A total of 60 hub genes were identified through differential expression analysis and WGCNA. Subsequently, 11 characteristic genes were identified using machine learning algorithms. MR analysis revealed 173 genes with causal effects on FL, leading to the identification of one key co-expressed gene, NDRG family member 2 (NDRG2), as a potential biomarker for FL. NDRG2 demonstrated strong predictive performance. Functional enrichment analysis revealed significant associations between NDRG2 and immune-related pathways in FL. Validation in clinical samples confirmed the relevance of NDRG2 as a biomarker.

Discussion: The integration of machine learning and MR successfully identified NDRG2 as a promising biomarker with a causal relationship to FL. Validation in clinical samples reinforced the reliability of these findings in clinical practice.

Conclusion: By combining machine learning, MR, and experimental validation, NDRG2 was identified and validated as a promising biomarker for FL.