Search Results (8,030)

Background/Objectives: First-generation CDK4/6 inhibitors (palbociclib, ribociclib, abemaciclib) target the conserved ATP-binding pocket of CDK4 and, despite clinical success, are limited by acquired resistance and insufficient exploration of alternative regulatory sites. This study aimed to identify a putative allosteric small-molecule candidate at the CDK4 αE-helix–Cyclin D1 α1-helix protein–protein interaction (PPI) interface within the CDK4/Cyclin D1/p21 ternary complex using RapidFunnel-AI, a decision-interpretable virtual-screening pipeline. Methods: Starting from 50,000 ChEMBL 33 molecules, the pipeline sequentially applied a Q-Fold/RapidFunnel topological Tanimoto scan based on clinical CDK4/6 inhibitor motifs, fragment-level electronic-property enrichment, ADMET/PAINS filtering, dry Vina-GPU docking, hydration-mediated AutoDock-GPU (Version 1.6) docking, explicit-solvent molecular dynamics, contact-retention analysis, and MM-GBSA energy decomposition. The Q-Fold Thermo-Core surrogate model provided fragment-level enrichment, predicting the HOMO–LUMO gap (R² = 0.93) and isotropic polarizability (R² = 0.98) on QM9. Candidate selection did not rely on the lowest docking or MM-GBSA score alone, but on pose persistence, contact continuity, and energy-component consistency. Results: The workflow reduced the initial library to 43 topologically prioritized candidates, 25 ADMET/PAINS-filtered ligands, and 9 docking-derived complexes for MD validation. Ligand_020 emerged as the only candidate that preserved a persistent binding mode at Site 2 during a 500 ns simulation—an interface engagement reproduced across three independent 500 ns replicates with no full dissociation in any replicate—with a protein Cα RMSD of 2.88 ± 0.32 Å, a ligand heavy-atom RMSD of 3.56 ± 0.28 Å, and a van der Waals-dominated MM-GBSA profile (ΔGbind = −28.23 ± 3.57 kcal/mol). In contrast, palbociclib and ribociclib, forcibly placed at Site 2 as negative controls, lost most initial contacts within 5 ns and tended to detach despite more favorable MM-GBSA values. Conclusions: These results suggest that single-score docking or MM-GBSA ranking can generate false positives at shallow PPI interfaces. By integrating AI-assisted prioritization, multipocket docking, explicit-solvent MD, contact-retention analysis, and energy-component consistency, RapidFunnel-AI nominated Ligand_020 as an experimentally testable putative allosteric hit targeting the CDK4/Cyclin D1 interface, offering a reusable platform for PPI-focused oncological drug discovery. Full article

Background/Objectives: Fraxetin (7,8-dihydroxy-6-methoxycoumarin), a coumarin constituent of Cortex Fraxini (Qinpi) used in traditional Chinese medicine, is metabolised mainly by UGT1A9, but its potential to inhibit UGT enzymes and cause herb–drug interactions (HDIs) is largely unstudied. Methods: Fraxetin and four related coumarins were screened against 11 recombinant human UGTs; isoforms inhibited ≥80% underwent full kinetic analysis with 4-methylumbelliferone as probe. Binding was examined by molecular docking on AlphaFold structures with PLIP, triplicate 100 ns molecular dynamics, and MM/GBSA and MM/PBSA free-energy calculations, and interaction risk by FDA 2020 in vitro–in vivo extrapolation (IVIVE). Results: Fraxetin alone inhibited both UGT1A1 and UGT1A9 by >80% and was characterised in detail, acting as a mainly competitive mixed-type inhibitor (UGT1A1 IC₅₀ 15.99 μM, K_i 8.32 μM; UGT1A9 IC₅₀ 8.44 μM, K_i 5.90 μM). A structure–activity comparison identified a dual-element pharmacophore comprising the C-6 methoxy group and the 7,8-dihydroxycoumarin aglycone. MM/GBSA favoured UGT1A9 over UGT1A1 (ΔΔG = −4.06 kcal/mol, p = 0.005), concordant with the kinetic ranking. IVIVE predicted a borderline systemic signal (R₁ > 1.02) but an intestinal R_1,gut approximately five- to seven-fold above the high-risk threshold of 11 after capping the luminal concentration at fraxetin aqueous solubility. Conclusions: This is the first characterisation of fraxetin as a moderate-potency inhibitor of UGT1A1 and UGT1A9 and points to a previously under-recognised herb–drug interaction risk concentrated in the intestinal lumen rather than systemically; the finding constitutes an interaction signal requiring clinical confirmation rather than an established risk. Full article

This review aims to evaluate the potential of endometriosis models, especially patient-derived iPSC models, to gain deeper insights into the disease, thereby advancing our understanding and treatment of endometriosis. This comprehensive narrative review utilized a structured search of the PubMed, Scopus, and Web of Science databases, primarily covering literature published between January 2000 and May 2025. An expansive search strategy was employed to capture the full breadth of the field using keywords such as “endometriosis,” “induced pluripotent stem cells (iPSCs),” “patient-derived organoids,” “disease modeling,” and “epigenetics” without restrictive filtering, ensuring the integration of both foundational theories and emerging biotechnological advances. In total, over 170 peer-reviewed publications were analyzed, ranging from landmark genomic meta-analyses that have identified significant risk loci to state-of-the-art 3D-culture systems for modeling patient-specific endometrial disease. By synthesizing these diverse sources, the review bridges the gap between traditional anatomical classifications and modern molecular modeling to evaluate the potential of iPSC platforms for personalized medicine and therapeutic discovery. Endometriosis is a multifactorial gynecological condition that affects 176 million women worldwide and can significantly impair quality of life. It occurs when endometrium-like tissue grows outside the uterus, responsive to ovarian hormones, causing inflammation, pain, and discomfort, and leading to fibrotic tissue. World Health Organization estimates indicate that 6–10% of women suffer from this disorder, which can cause infertility and increase the risk of developing various types of cancer and autoimmune disorders. The use of patient-derived iPSC models serves to gain deeper insights into the disease by mimicking the endometrial tissue or lesions observed in affected individuals, thereby advancing our understanding and treatment of endometriosis. Full article

Lung cancer remains the leading cause of cancer-related mortality worldwide, largely due to late-stage diagnosis, biological heterogeneity, and persistent challenges in staging and treatment selection. This narrative review summarizes current and emerging applications of AI across lung cancer screening and early detection, imaging-based staging and prognostication, tissue and liquid biopsy-based tumor characterization, treatment planning, surgical and intraoperative guidance, and drug discovery. In imaging, deep learning models have demonstrated high performance in pulmonary nodule detection, risk stratification, and prediction of molecular alterations, while also showing promise in improving screening efficiency and reducing interpretive variability. In pathology and liquid biopsy domains, AI enables prediction of driver mutations, immunotherapy response, and survival outcomes directly from histopathology slides, circulating tumor DNA, and other blood-based biomarkers, facilitating minimally invasive precision oncology approaches. In treatment planning and delivery, AI systems are being developed to support clinical decision-making, surgical planning (through advanced image segmentation and delineation of operative anatomy), and intraoperative navigation through robotic and computer vision-enabled platforms. Despite these advances, significant barriers remain, including limited real-world validation, algorithmic biases, workflow integration issues, and unresolved ethical and legal concerns. Future progress will depend on the development of transparent, clinically validated, and generalizable AI systems that augment rather than replace the expertise of clinical providers and healthcare teams. Active engagement from pulmonologists, oncologists, radiologists, and thoracic surgeons will be essential in guiding safe implementation and ensuring that AI-driven innovations translate into meaningful improvements in patient outcomes. Full article

Major depressive disorder (MDD) is clinically heterogeneous, and peripheral inflammatory biomarkers may help clarify early biological mechanisms before illness chronicity or pharmacologic treatment confound interpretation. This systematic review synthesized evidence on peripheral inflammatory biomarkers in first-episode, drug-naïve major depressive disorder (FEDN-MDD) compared with healthy controls and examined associations with clinical severity. Following PRISMA 2020, searches of PubMed/MEDLINE, Embase, PsycINFO, and Scopus from inception to 19 March 2026 identified 313 records; after screening, 16 publications were included in qualitative synthesis. Studies varied in age group, biological matrix, assay platform, and statistical reporting, precluding meta-analysis. The most frequently assessed biomarkers were IL-1β, TNF-α, IL-6, and CRP/hs-CRP. IL-6 showed the clearest recurrent tendency toward elevation in FEDN-MDD, whereas CRP/hs-CRP findings were partially positive but methodologically limited. TNF-α and IL-1β findings were mixed, and clinical correlations with depressive severity were sparse and inconsistent. Overall, the evidence supports heterogeneous early immune dysregulation in a subset of patients with FEDN-MDD rather than a single reproducible inflammatory signature. Peripheral inflammatory biomarkers should currently be considered research tools for biological stratification and mechanistic hypothesis generation, pending larger standardized longitudinal studies. Full article

H9N2 avian influenza viruses inherently carry cross-species transmission potential, making continuous surveillance critical for pandemic prevention. This study focused on monitoring the 2024 H9N2 epidemic in Jiangsu Province’s external environment, analyzing its molecular evolution and receptor binding properties, assessing cross-species transmission and pandemic risks, and investigating serological antibody levels across different human populations. Environmental samples were collected from live poultry markets, farms, slaughterhouses, and bird habitats across Jiangsu, screened via quantitative PCR (qPCR), with positive samples used for virus isolation and whole-genome sequencing. Receptor binding properties were tested by hemagglutination assay, and H9N2 antibody levels were measured in 370 occupationally exposed individuals and 240 non-exposed individuals using hemagglutination inhibition (HI) assays. Among the 5779 collected samples, 6.89% tested H9N2-positive, and 12 strains belonging to the Eurasian lineage Y280-like clade G57 genotype were successfully isolated. All strains carried the HA-Q226L mutation, with 11 showing preferential binding to human α-2,6 receptors and one strain possessing dual receptor binding capability. Internal genes harbored mammalian adaptation mutations, and M2 proteins contained mutations conferring complete resistance to amantadine-class antiviral drugs. Serological tests revealed antibody positive rates of 4.05% in exposed populations and 2.5% in non-exposed populations, with no statistically significant difference between groups. These findings confirm that Jiangsu’s circulating H9N2 viruses have acquired human receptor preference and mammalian adaptation, posing silent infection and pandemic risks. Enhanced surveillance and the development of candidate vaccine stockpiles are strongly recommended. Full article

Endogenous gene tagging using CRISPR has changed the understanding of the role played by different proteins due to the ability to track and study proteins in their natural state. With CRISPR-based gene tagging, it is possible to insert fluorescent, luminescent, epitope, affinity, and proximity labels into the target protein at its endogenous genomic location without affecting its physiological expression and dynamics. Here, we discuss the DNA-repair mechanisms employed in endogenous gene tagging, including homology-dependent repair, NHEJ-based integration, and alternative approaches that can be used with challenging cell types. Key aspects of efficient CRISPR tagging experiments are also described. Additionally, we review recent advances in the increasing array of protein tag technologies, including fluorescent proteins, split-reporter technologies, NanoLuc/HiBiT, peptide epitopes, and proximity biotinylation enzymes. Lastly, we review the scalability of endogenous tagging approaches using multiplex editing, atlas-scale proteome tagging, iPSC-based disease modeling, and drug discovery platforms for assessing target engagement, protein degradation, phenotype screening, and mechanism of action of compounds. Although difficult in primary and pluripotent cells, new methods based on avoiding double-strand breaks, such as prime editing, PASTE, and CRISPR associated transposases, will drive the future expansion of endogenous tagging approaches. Such developments firmly set up CRISPR gene tagging as a fundamental technology in quantitative cell biology and translational pharmacology. Full article

Dipeptidyl peptidase-4 (DPP-4) is a key therapeutic target for type 2 diabetes (T2D). Several synthetic anti-DPP-4 drugs are currently available for the treatment of T2D; however, the need for safe and effective therapies remains unmet due to the side effects associated with existing DPP-4 inhibitors. This study aimed to integrate structure-based and machine learning (ML)-based virtual high-throughput screening to identify natural DPP-4 inhibitors. Random forest, logistic regression, support vector machine (SVM), and multilayer perceptron (MLP) models were trained on DPP-4 IC₅₀ datasets. Among these, the SVM and MLP models achieved high predictive performance, with areas under the curve of 0.928 and 0.923, respectively. Screening of a natural compound database identified 107 compounds for further analysis. Subsequent structure-based screening, using sitagliptin as a positive control, identified sennidin B and doxorubicin hydrochloride as promising candidates with strong binding affinity for DPP-4. Molecular dynamics simulations (200 ns) and MM-PBSA calculations confirmed stable interactions with DPP-4. Further, sennidin B and doxorubicin hydrochloride inhibited DPP-4 activity in a concentration-dependent manner, with estimated IC₅₀ values of 39.39 and 19.78 μM, respectively. Sennidin B also reduced DPP-4 mRNA and protein expression levels in Caco-2 cells. Overall, sennidin B shows promise as a natural DPP-4 inhibitor and warrants further investigation as a potential antidiabetic agent. Full article

Chemical contaminants in food pose a serious threat to public health, driving the need for sensitive, rapid, and on-site screening methods. Lateral flow immunoassay (LFIA) is rapid and portable but suffers from single-signal readout and insufficient label stability. Nanozymes, nanomaterials with enzyme-like catalytic activity and excellent stability, have emerged as promising signal labels to address these limitations. Moreover, their diverse physiochemical properties enable multiplex signal readout, where two or more complementary signals (e.g., colorimetric, fluorescent, chemiluminescent, photothermal, and surface-enhanced Raman scattering) are generated simultaneously from a single test line. This multiplex strategy significantly enhances detection sensitivity, accuracy, and reliability through signal amplification and self-calibration. This review provides a systematic overview of the catalytic properties and their major types used in multiplex signal LFIA. The signal combination strategies employed in nanozyme-based multiplex signal LFIA were also summarized, and their applications in detecting veterinary drugs, mycotoxins, pesticides, and other food chemical contaminants are highlighted. Ultimately, current challenges and future prospectives in this field are discussed. This review offers guidance for designing high-performance, nanozyme-based multiplex signal LFIA platforms for food safety monitoring. Full article

Background: Brain metastasis is associated with poor prognosis in lung adenocarcinoma (LUAD). Anoikis resistance may contribute to tumor cell survival during metastatic dissemination and brain colonization; however, robust biomarkers for prognostic stratification and brain metastasis-associated classification remain limited. This study aimed to investigate anoikis-related molecular features in LUAD brain metastasis and develop a machine learning-based signature for prognostic assessment and exploratory classification of primary and brain-metastatic LUAD samples. Methods: We integrated single-cell and multi-cohort bulk transcriptomic data. Single-cell analysis was performed to characterize anoikis-related cellular states and intercellular communication in primary and brain-metastatic LUAD samples. In the bulk transcriptomic analysis, TCGA-LUAD was used for prognostic feature selection and risk-model construction, and GSE26939 was used for external prognostic validation. The classification performance of the fixed signature for distinguishing primary LUAD from brain-metastatic LUAD samples was further evaluated in GSE161116 and GSE271259. Immune microenvironment features were assessed, and an LLM-assisted exploratory drug-screening strategy combined with molecular docking was used to prioritize candidate compounds. Results: Single-cell analysis suggested that metastatic epithelial cells exhibited enhanced anoikis-related activity, accompanied by macrophage-associated SPP1-CD44 and MIF-(CD74+CXCR4) communication patterns. Machine learning-based feature selection identified an eight-gene signature consisting of BIRC3, CCL20, CLEC7A, CTSL, GOLM1, ICAM3, MTUS1, and SERPINH1. The signature showed prognostic value in TCGA-LUAD and GSE26939 and demonstrated exploratory classification performance in distinguishing primary LUAD from brain-metastatic LUAD samples. High-risk patients exhibited immune microenvironment alterations and enrichment of tumor progression-related pathways. LLM-assisted compound prioritization and molecular docking highlighted resveratrol and SB431542 as hypothesis-generating candidates with predicted interactions with core targets. Conclusions: This study identified an anoikis-related eight-gene signature for LUAD prognostic stratification and exploratory brain metastasis-associated classification. The findings suggest the potential involvement of anoikis-related tumor–microenvironment interactions in LUAD brain metastasis and provide candidate genes and compounds for further experimental validation. Full article

Background/Objectives: Inflammatory bowel disease (IBD) represents a growing therapeutic challenge, and the identification of novel treatment strategies remains an unmet clinical need. Drug repurposing offers a pragmatic and cost-effective alternative to de novo drug development. This study aimed to identify candidate drugs for repurposing in IBD through inverse signal detection within a large spontaneous pharmacovigilance database. Methods: In this observational, retrospective pharmacovigilance study, data from the FDA Adverse Event Reporting System (FAERS) were analyzed using OpenVigil 2.1. Drugs inversely associated with IBD were identified based on a ROR < 1 and an adjusted p-value < 0.05. Candidates were subsequently filtered to exclude agents with implausible indications, unfavorable pharmacokinetic profiles, or recognized contraindications to use in IBD. Although this screening process yielded a broader set of repurposing candidates across multiple drug classes, the present study focused specifically on antidiabetic medications, which were subjected to a targeted literature review evaluating their immunomodulatory properties, anti-inflammatory mechanisms, and existing preclinical and clinical evidence in the context of IBD. Results: Of 3585 initial drug–event combinations evaluated, 73 candidates met predefined criteria for statistical significance, pharmacokinetic feasibility, and clinical relevance. Within this broader pool, ten antidiabetic agents which demonstrated meaningful inverse signal strength were selected for in-depth analysis: dulaglutide (ROR 0.181, 95% CI 0.136–0.242), insulin lispro (ROR 0.206, 95% CI 0.161–0.263), insulin glargine (ROR 0.246, 95% CI 0.205–0.295), insulin (ROR 0.340, 95% CI 0.295–0.390), insulin aspart (ROR 0.349, 95% CI 0.267–0.455), empagliflozin (ROR 0.400, 95% CI 0.311–0.514), liraglutide (ROR 0.419, 95% CI 0.319–0.552), metformin (ROR 0.446, 95% CI 0.407–0.489), sitagliptin (ROR 0.460, 95% CI 0.376–0.563), and semaglutide (ROR 0.622, 95% CI 0.507–0.764). The subsequent literature review discussed relevant immunomodulatory and anti-inflammatory properties for each of these agents, providing a mechanistic rationale for their potential therapeutic role in IBD. Conclusions: This study identifies antidiabetic drugs as plausible repurposing candidates for IBD, supported by both pharmacovigilance-derived inverse signals and a body of mechanistic and clinical literature suggesting shared pathophysiological pathways between the two conditions. However, it should be acknowledged that the clinical evidence supporting the therapeutic efficacy of several candidates remains variable or incomplete, and robust interventional data are largely lacking. Ultimately, the findings of this study generate testable hypotheses and highlight a set of candidate therapies that warrant dedicated experimental and clinical investigation, including well-designed prospective trials, to determine their true therapeutic potential in IBD management. Full article

Hypertension is one major risk factor of cardiovascular diseases, and RAS plays vital role during the development of hypertension. To obtain a novel antihypertensive peptide, Coix glutelin was hydrolyzed by trypsin and further separated by Sephadex G10. Based on 751 identified sequences, pharmacophore mapping, molecular docking, and in silico proteolysis were applied to screen and optimize the candidate sequence. Finally, a novel peptide, ENWAAL, was generated with IC₅₀ of 210.57 μM, which acted with ACE in a competitively inhibitory pattern. The in vivo antihypertensive effect was evaluated in SHRs. Significant improvements were observed in hypertension-related characteristics, including blood pressure, cardiac structure and function, and serum angiotensin II (Ang II) level. In the brain, quantitative real-time PCR analysis revealed significant downregulation of angiotensin II type 1 receptor (AT1R) mRNA expression, concomitant with upregulation of angiotensin-converting enzyme 2 (ACE2) and MAS receptor. The protein expression of ACE and AT1R in the ENWAAL group also significantly decreased. This study can provide a candidate antihypertensive drug targeting RAS. Full article

Ribosome-inactivating proteins are a class of toxins that target eukaryotic ribosomes, inhibit protein synthesis, and ultimately induce cell death. Several of these toxins pose significant clinical and public health threats. Among these, ricin, derived from the castor bean plant (Ricinus communis), is a highly potent biotoxin with recognized bioterrorism potential. Other ribosome-inactivating proteins, including Shiga toxin produced by pathogenic Shigella and Escherichia coli, as well as mucoricin from Mucorales fungi, contribute to disease severity and can lead to life-threatening complications. Despite these risks, no approved therapeutics are currently available. The development of effective inhibitors depends on robust and well-defined strategies to identify and validate small molecules that disrupt toxin–ribosome interactions. Efforts to target the catalytic active site have met with limited success, largely due to its broad, shallow, and highly polar architecture, which is not conducive to high-affinity binding by drug-like molecules. In contrast, the ribosome-binding interface represents a more tractable target, as it is essential for toxin recruitment and offers more structurally defined and druggable features. Inhibitors targeting this interface can also exert allosteric effects by disrupting long-range conformational coupling between the ribosome-binding region and the active site, thereby attenuating catalytic activity without directly engaging the catalytic pocket. In this review, we compile and evaluate biophysical and biochemical assays for the discovery and characterization of small-molecule inhibitors that target toxin–ribosome interactions. We examine in vitro binding approaches, including surface plasmon resonance-based fragment screening and fluorescence anisotropy assays for ranking inhibitory activity. We further review biochemical and molecular assays that assess ribosome protection from toxin-mediated depurination, along with complementary cell-based assays that evaluate functional rescue in cellular systems. Collectively, this review consolidates current screening methodologies and highlights opportunities to refine assay strategies, thereby supporting the advancement of targeted therapeutics. Full article

Ubiquitination, a crucial cellular protein regulation process, is linked to various diseases, including cancer. Deubiquitinases (DUBs) can reverse ubiquitination, offering a therapeutic strategy. USP7, a DUB, is a key target in oncology due to its role in destabilizing p53, and small-molecule inhibitors could restore p53 activity and combat tumor growth. In this study, we integrated a machine learning (ML)-based screening approach with molecular docking and molecular dynamics (MD) simulations in order to identify potential small-molecule inhibitors of USP7. ML-based screening identified 22 active molecules from a library of 2301 natural compounds. Among the 22 active compounds, only fifteen compounds fulfilled the drug-likeness criteria. Subsequently, molecular docking found three compounds, PubChem 162957515, 114917, and 442879 as potential inhibitors based on binding affinity and interactions. Further, MD simulations and MM-PBSA analyses were performed to evaluate the stability and dynamic behavior of the complexes. Binding energy calculations Molecular Mechanics Poisson–Boltzmann Surface Area (MM-PBSA) revealed that compounds PubChem 114917 and 162957515 exhibited strong binding affinities of −20.98 kcal/mol and −18.68 kcal/mol, respectively, implying that these compounds could serve as promising inhibitors for the development of anticancer therapeutics. Full article