Pharmaceuticals最新文献

Background: Colorectal cancer (CRC) remains a major clinical challenge, in part due to the limited efficacy of 5-fluorouracil (5-FU)-based chemotherapy, which is often compromised by the emergence of acquired resistance. Aronia berry extract (ABE), a phenolic-rich natural compound, has gained increasing attention for its anticancer and chemosensitizing properties. This study aimed to investigate whether ABE can overcome 5-FU resistance (5-FU-R) in CRC and to elucidate the molecular mechanisms underlying its therapeutic effects. Methods: We conducted a series of in vitro experiments using 5-FU-R CRC cell lines to evaluate the synergistic effects of combined ABE and 5-FU treatment. Genome-wide transcriptomic profiling was performed to identify key regulatory pathways associated with chemoresistance and to determine potential ABE-responsive targets. Findings were further validated using patient-derived 3D organoids (PDOs). Results: Co-treatment with ABE and 5-FU significantly reduced the effective concentration of 5-FU required to inhibit 5-FU-R CRC cells, yielding a Bliss synergy score greater than 10. The combination markedly suppressed cell viability, clonogenic potential, migration, and invasion. ABE also reduced cancer stemness, as evidenced by reduced CD44, Nanog, and Oct4 expression. Functional inhibition of Toll-like receptor 3 (TLR3) impaired spheroid growth, and PDO experiments corroborated these findings, demonstrating reduced organoid growth, diminished survival, and decreased NF-κB expression following ABE treatment. Conclusions: Our findings reveal that ABE effectively overcomes 5-FU resistance in CRC by targeting the TLR3/NF-κB signaling axis. This study highlights ABE as a safe, accessible, and promising adjunctive strategy to enhance therapeutic responses in 5-FU-resistant CRC.

Objectives: The industrial production of lanolin alcohol currently employs batch saponification, which suffers from high energy consumption, prolonged processing time, and excessive solid waste generation, rendering it incompatible with green chemistry principles. This study aimed to develop an efficient, sustainable saponification process by addressing these limitations through integrating large language models (LLMs) with microfluidic technology. Methods: An LLM-based intelligent agent called SapoMind (version 1.0) was constructed. SapoMind employs LLMs as its software core and a continuous-flow microreactor as the experimental platform. Its performance was enhanced through supervised fine-tuning. The system enables automated recommendation of saponification process parameters, autonomous experimental design, and automatic execution of experiments. Saponification conditions were automatically optimized considering product quality, energy consumption, material consumption, and time consumption. Results: The optimal continuous-flow saponification conditions were determined as 70 °C reaction temperature and 9 min residence time, producing lanolin alcohol complying with European Pharmacopoeia standards. Compared to batch processing, the optimized process reduced carbon emissions by 53% and solid waste by 37%, with the greenness score increasing from 82 to 93. Conclusions: This study demonstrates the effectiveness of LLM-driven intelligent agents in optimizing green chemical processes. SapoMind offers significant environmental and operational benefits for lanolin alcohol production.

Background: Platelet hyperreactivity contributes to occlusive thrombus formation in vessels, precipitating acute cardiovascular events such as myocardial infarction and stroke. Traditional Chinese Medicine (TCM) has been used for centuries, and numerous TCM herbs have been reported to exert anti-inflammatory and anticoagulant effects. Objectives: We sought to identify key compounds within the TCM-derived herbal extracts that regulate platelet activity. Methods: Crude and fractioned herbal extracts were screened for their ability to inhibit platelet activation in response to multiple agonists. Platelet aggregation and flow cytometry were used to assess the potency and selectivity of the compounds within the extracts. Results: Three extracts, di gu pi (DGP), san qi (SQ), and zi cao (ZC), demonstrated inhibitory activity and were subsequently fractionated. Fractions derived from DGP, the root bark of Lycium chinense, inhibited platelet aggregation and suppressed integrin activation and granule secretion downstream of collagen receptor signaling. Further analysis identified the oxidized lipids 9(S)-hydroxy-9Z,11E-octadecadienoic acid (9-HODE), 13(S)-HODE, and 13(S)-hydroxy-9Z,11E,15Z-octadecatrienoic acid (13-HOTrE) as constituents of the bioactive fractions. Both 13-HODE and 13-HOTrE selectively inhibited collagen-mediated platelet aggregation without affecting thrombin-induced activation. Conclusions: Collectively, these findings identify oxylipins in TCM as promising candidates for the development of antiplatelet therapies targeting platelet activity and thrombosis. These oxylipins may represent novel approaches for thrombosis and have high therapeutic potential for development as next-generation antiplatelet drugs.

Purpose: The purpose of this study is to investigate the bioactive constituents of Atractylodes Rhizome (AR) and to explore its mechanism of action in the treatment of rheumatoid arthritis (RA). Methods: The research mainly adopts the methods of tissue metabolomics and network pharmacology. Firstly, we employed a metabolomics strategy to obtain the metabolite profile and utilized PCA/OPLS-DA analyses to identify the differential metabolites involved in the treatment of RA by AR. Subsequently, we determined the key target metabolic pathways of AR in RA treatment. Next, a network pharmacology approach was employed to identify active compounds, potential targets, and signaling pathways for AR in RA treatment, with a PPI network constructed. These predictions were then validated through molecular docking simulations, followed by in vivo verification using a CFA-induced RA rat model. The anti-RA efficacy was evaluated through synovial histopathology and cytokine assays, with the key mechanistic insights being confirmed at the molecular level by RT-qPCR and WB. Results: The results of the metabolomics study showed that AR regulated 28 differential metabolites linked to glycerophospholipid, linoleic acid, and alpha-linolenic acid metabolism. Network pharmacology identified Wogonin, Atractyloyne, and Atractylenolide II as key active compounds, acting through pathways such as Pathways in cancer and PI3K-Akt signaling, combined with the metabolites to jointly analyze the metabolic pathways, and were verified by correlation analysis. Molecular docking confirmed the main active ingredients' strong binding to core targets. In AIA rats, AR treatment reduced synovial inflammation and lowered serum levels of IL-6 and MMP-9. At the molecular level, AR up-regulated Bcl-2, down-regulated Bax, and inhibited the SRC/JAK2-STAT3 pathway by decreasing EGFR, SRC, JAK2, and p-STAT3 expression. Conclusion: These findings may illuminate the mechanism by which Atractylodes Rhizome exerts its effects via the JAK2/SRC-STAT3 axis, thereby revealing its potential mechanism of action against rheumatoid arthritis.

Background/Objectives: Fluvoxamine (FLU) is a selective serotonin reuptake inhibitor and one of the most potent agonists of the sigma-1 receptor. Emerging evidence shows that FLU exerts protective effects in multiple organs, making it a promising candidate for topical ocular therapy. Developing an FLU eyedrop for glaucoma can address a significant treatment gap with potentially fewer side effects compared with conventional therapies. To optimise formulation development, precise quantification of FLU in ocular compartments such as aqueous humour, as well as systemic circulation, is essential to characterise drug absorption, ocular bioavailability, and safety. Methods: We developed and validated a UHPLC-MS method for FLU detection in aqueous humour and serum using simple sample preparation steps. Results: The 11-min-long reverse phase chromatography followed by SRM-based mass spectrometry detection provides a highly selective and sensitive FLU detection method. Our method was proved to be linear in the 0.0625-1.5 µg/mL range and was validated according to the EMA guidelines. Conclusions: The simplicity of sample preparation, the tolerable matrix effects, and the favourable detection parameters provide a robust tool for preclinical pharmacokinetic and pharmacodynamic studies of FLU's ocular protective effects.

Background: Epilepsy comprises a range of disorders affecting the central nervous system (CNS) characterized by recurrent seizures, impacting approximately 60 million individuals globally. In this study, we designed and derived a series of peptide analogs 1-30 of 3,4,5-trimethoxycinnamic acid (TMCA) from the herbal combinations of Polygala tenuifolia and Gastrodia elata, recognized in Traditional Chinese Medicine (TCM). Methods: All the analogs were synthesized, and their anticonvulsant efficacy was subsequently assessed. The anticonvulsant activity of all TMCA analogs was evaluated using two acute seizure models in mice: the maximal electroshock (MES) and the sc-pentylenetetrazole (PTZ) models. Furthermore, we explored the electroencephalogram (EEG) and double-labeling immunofluorescence experiments were carried out as well. Results: Our findings indicated that compounds 11, 19, 22, and 23 demonstrated favorable anticonvulsant activities during the initial assessment. Compounds 19 and 23 also played roles in controlling the onset of epilepsy in EEG, modulating levels of GABA aminotransferase (GABA-AT)/gamma-aminobutyric acid type A receptor (GABA_AR), interacting with active sites of GABA-AT and GABA_AR obtained from docking simulation methods. Conclusions: Novel derivatives in this study provide new cores for further design and optimization inspired by TCM herb pair drugs P. tenuifolia and G. elata, with the aim of exploring new anticonvulsant agents.

Background/Objectives: Cytochrome P450 3A4 (CYP3A4) metabolizes approximately 30-50% of clinically used drugs; thus, accurate prediction of CYP3A4 inhibition is essential for early assessment of drug-drug interaction (DDI) risk and toxicity. This study evaluated an integrated artificial intelligence framework for predicting CYP3A4 inhibition (%) using a large, curated chemical dataset. Methods: A dataset of 23,713 compounds was compiled from the Korea Chemical Bank and multiple commercial and public databases. Vector-based machine learning (ML) models (LightGBM, XGBoost, CatBoost, and a weighted ML ensemble) and graph neural network (GNN) models (O-GNN with contrastive learning and manifold mixup (O-GNN + CL + Mixup), D-MPNN, GINE, and GATv2) were evaluated. Manifold mixup was applied during GNN training, and SMILES enumeration-based test-time augmentation was used at inference. The best-performing ML and GNN models were integrated using a weighted ensemble strategy. Model interpretability was examined using SHAP analysis for ML models and occlusion sensitivity analysis for O-GNN + CL + Mixup. Results: The weighted ML ensemble achieved the best performance among ML models (RMSE = 19.1031, Pearson correlation coefficient (PCC) = 0.7566); the O-GNN + CL + Mixup model performed the best among GNN models (RMSE = 20.1002, PCC = 0.7265). The hybrid model achieved improved predictive accuracy (RMSE = 19.0784, PCC = 0.7570). External validation on 100 newly generated experimental data points confirmed generalizability (Custom Metric = 0.8035). Conclusions: This study demonstrated that integrating ML and GNN models with data augmentation strategies improves the robustness and interpretability of CYP3A4 inhibition prediction and established a practical framework for metabolic screening and DDI risk assessment.

Background/Objectives: Antimicrobial resistance (AMR) presents a medical risk as well as a significant global socioeconomic challenge. Key contributors to AMR include the excessive use and incorrect application of antibiotics in humans and agriculture, nosocomial infections, and the absence of new classes of antibiotics. Methods: Novel dibrominated tricyclic flavonoids have been synthesized from the corresponding 3-dithiocarbamic flavanones and their antimicrobial and cytotoxicity properties have been investigated. Results: It has been found that these tricyclic flavonoids exhibit strong antimicrobial properties against clinically relevant pathogens such as Staphylococcus aureus, Acinetobacter baumannii, and Escherichia coli with MIC and MBC values against S. aureus ATCC 25923 as low as 0.12 µg/mL and 1.9 µg/mL, respectively. Conclusions: The synthetic tricyclic flavonoids exhibit strong antibacterial activity against selected WHO priority pathogens, including Staphylococcus aureus and Acinetobacter baumannii, surpassing the efficacy of both natural and synthetic flavonoids and several conventional antibiotics.

Background: The development of Multi-Target-Directed Ligands (MTDLs) has emerged as a significant strategy for addressing complex, overlapping pathologies such as cancer and Alzheimer's disease (AD). This study aims to provide a robust computational framework for the design of dual-target inhibitors. Methods: This study presents an integrated and rigorous computational pipeline combining Quantitative Structure-Activity Relationship (QSAR) modeling, Molecular Docking, and Molecular Dynamics (MD) simulations with Dynamic Cross-Correlation Matrix (DCCM) analysis. Using a dataset of 57 known tubulin inhibitors, two high-performing QSAR models were developed to guide the rational design of 16 novel trimethoxyphenyl-based analogues. Results: Following ADMET and drug-likeness filtering, Lead Candidates 15 and 16 were identified. Quantitative activity predictions confirmed their enhanced potency thresholds, which were subsequently validated through static docking against β-tubulin (PDB: 4O2B) and Acetylcholinesterase (PDB: 1EVE). In total, 100 ns MD simulations and MM-GBSA calculations demonstrated superior binding stability and energetically favorable profiles for both targets, while DCCM analysis confirmed the functional synchrony of the protein-ligand complexes. Conclusions: The results provide a validated structural hypothesis for dual-target inhibition. The identified leads, 15 and 16, demonstrate strong predictive potential and are prioritized for chemical synthesis and in vitro biological evaluation.

Colon cancer (CC) remains a leading cause of cancer-related mortality worldwide, with multidrug resistance (MDR) presenting a formidable barrier to successful chemotherapy. Ferroptosis-an iron-dependent, lipid peroxidation-driven form of cell death-offers a novel therapeutic avenue to bypass MDR by exploiting metabolic vulnerabilities distinct from traditional apoptosis pathways. Emerging evidence reveals a dynamic interplay between MDR and ferroptosis: MDR cancer cells suppress ferroptosis through NRF2/GPX4-mediated antioxidant upregulation, iron sequestration by ferritin, and lipid metabolism reprogramming, including SREBP1-driven monounsaturated fatty acid accumulation, while ABC transporters actively efflux ferroptosis inducers. On the other hand, ferroptosis inducers such as erastin and RSL3 have the potential to overcome apoptotic resistance and avoid efflux pathways, which recover therapeutic efficacy. This review first describes the primary mechanisms of chemotherapy resistance in colon cancer and then explains the molecular processes that prevent ferroptosis in resistant cells. We also review recent data on the complex interactions between resistance to chemotherapy and ferroptosis, and outline approaches that may stimulate iron accumulation to reverse MDR. By emphasizing novel methods to induce ferroptosis, this review highlights that this approach is a promising strategy to overcome chemotherapy resistance in colon cancer and will facilitate the development of more precise and efficient treatment.