搜索结果：Drug target insights

Developing advanced chemotherapeutic drug delivery systems (DDS) is critical for expanding the therapeutic index and reducing the off-target toxicity of potent anticancer agents. In this study, a novel multifunctional nanocomposite, CS/M/MIL, was engineered by integrating magnetite (M) nanoparticles and chitosan (CS) into a metal-organic framework (MOF), MIL-100(Fe), using a modified hydrothermal method. This platform was utilized to encapsulate the anticancer drug epirubicin (EPR), achieving a remarkably high drug entrapment efficiency (EE) of 89.4%. Physicochemical characterization, including XRD and SEM, confirmed successful composite formation and structural integrity, with the modified CS/M/MIL showing a particle size increase to approximately 400 nm compared to the pristine MOF. Textural analysis revealed a transition from a highly microporous structure (BET surface area: 1804.68 m2/g) to a hierarchical macroporous architecture (50.95 m2/g) following functionalization. In vitro release kinetics demonstrated a pH-responsive behavior, with significantly accelerated drug release in acidic conditions (pH 5.0) simulating the acidic endo/lysosomal compartments. Biological evaluations against MCF-7 breast cancer cells revealed that the EPR-loaded nanocomposite significantly enhanced therapeutic efficacy, yielding an IC50 value of 7.57 µg/mL. Furthermore, flow cytometric analysis confirmed that the system promotes substantial apoptosis (31.66%) and induces G2/M phase cell cycle arrest. The significance and novelty of this work lie in the synergistic integration of magnetic responsiveness, biocompatible polymeric coating, and high-porosity MOF architecture into a single platform. Additionally, density functional theory (DFT) calculations provided unique mechanistic insights, identifying Fe3+ coordination and hydrogen bonding as the primary drivers for the high affinity between EPR and the nanocarrier. These findings position CS/M/MIL as a superior, targeted carrier for anthracycline-type drugs, offering a pathway to improved clinical outcomes with reduced systemic side effects.

Network pharmacology and bioinformatics approaches may provide valuable insights into pharmacological effects by enabling a system-level understanding of how drugs interact with biological networks rather than single targets. The study aimed to elucidate the therapeutic mechanisms of Azadirachta indica leaf extract (AILE) in hepatotoxicity and hepato-injury (HT/HI) through the identification of pathways and molecular targets via network pharmacology. Phytochemical profiling of AILE was performed by Gas Chromatography-Mass Spectrometry (GC-MS). SMILES structures of identified phytochemicals were retrieved from PubChem, and ADMET properties were assessed. Five non-hepatotoxic compounds with high absorption were prioritized. Their potential targets and hepatotoxicity-related genes were predicted using SwissTargetPrediction and GeneCards, followed by drug-target network construction in Cytoscape. Hub genes were identified through protein-protein interaction (PPI) analysis (STRING) and enrichment studies (ShinyGO, KEGG). Gene regulatory networks were built using TRRUST and miRNet 2.0, and molecular docking was performed to evaluate target-protein binding affinities. In the results, ADMET profiling identified five candidate phytochemicals: Butane, 1,1-diethoxy-3-methyl (B), 1,1,3-triethoxybutane (T), Propane, 1,1,3-triethoxy (P), Gamma-Sitosterol (G), and Caryophyllene (C), with favorable absorption. A total of 478 potential compound targets (BTPGC) were predicted, while 1243 HT/HI-related genes were identified, of which 73 overlapped as potential therapeutic targets. PPI analysis generated a network of 73 nodes and 582 edges. GO enrichment revealed involvement in lipid response, oxidative response, programmed cell death, and apoptosis. CytoHubba highlighted six hub genes (TNF, CASP3, ESR1, MAPK3, EGFR, and HSP90AA1). TRRUST identified 15 transcription factors, while miRNet predicted four regulatory miRNAs (miR-155-5p, miR-122-5p, miR-328, and miR-16). This integrative computational network pharmacology analysis provides novel insights into the pathogenesis of liver diseases (HT/HI) and identifies potential therapeutic targets, exploring biomarkers for future experimental validation and clinical translation.

Cervical cancer remains a serious global health issue, particularly in low- and middle-income countries, with inadequate access to early detection and diagnostics. This study investigates the anticancer effect of esculin, a coumarin known for its pharmacological benefits, as a safer and more effective alternative to standard chemotherapeutics. This research implements an integrative in silico method, which includes ADME profiling, toxicity prediction, molecular docking, molecular dynamics (MD) simulations, and network pharmacology analyses. Esculin performed well in drug-likeness properties and showed satisfactory water solubility and low predicted toxicity, which is even better than that of the reference drug paclitaxel in safety and pharmacokinetics. Although no direct connection was established between esculin's predicted targets and genes perturbed in cervical cancer, PARP1 came up as a candidate target that is functionally and biologically relevant, since the protein is involved in DNA repair and is overexpressed in cervical tumours. Molecular docking revealed that esculin binds strongly to PARP1and interacts with ASP770 and ARG878. A subsequent 100 ns molecular dynamics (MD) simulation experiment showed the constant RMSD and hydrogen bonding profiles that were consistent and thus proved the stability of the complex structure. Protein-protein interaction and enrichment analyses supported PARP1's role in critical cancer-associated pathways, particularly in DNA damage response and chromatin remodelling. Considering all the findings, the efforts to develop a targeted cervical cancer therapy with esculin as a potential candidate are gaining traction and being warranted by further experimental validation.

Justicidins are naturally occurring arylnaphthalene lignans found mainly in Justicia species, with documented anticancer, anti-inflammatory, antiviral, neuroprotective, and cardioprotective activities. They target several biochemical pathways, such as NF-κB, MAPK, and PI3K/Akt/mTOR, and have the potential to serve as multi-target drugs for complex diseases. Although there have been several in vitro and in vivo studies, most remain preclinical and isolated, with little consideration of structure-activity relationships, pharmacokinetics, and translational relevance. This review provides a pathway-centric, integrative review of justicidins, links structures to activities, pharmacokinetic and toxicological properties of analogues, and explores synergy with current drugs. We also discuss developments in analytical, biosynthetic, and formulation that might expedite drug discovery. However, key challenges include low water solubility, low bioavailability, the absence of chronic toxicity studies, and a lack of clinical trials. To overcome these challenges, it is important to focus on optimization, GMP production, chronic toxicity evaluation, and early clinical evaluation. With focused development, justicidins have the potential to go from natural products to therapeutic leads against cancer, inflammation, and viral infections.

For decades, oncology dose selection has been guided by the maximum tolerated dose (MTD) and plasma pharmacokinetics (PK), reflecting assumptions appropriate for classical cytotoxic chemotherapies. However, the advent of high-affinity, targeted therapies, including kinase inhibitors, epigenetic modulators, and radioligands challenges this paradigm. These agents achieve robust target engagement at doses far below the MTD, and systemic plasma concentrations often fail to reflect pharmacologically relevant exposure at tumor or hematologic sites. Physiologically-based pharmacokinetic (PBPK) modeling, extended to incorporate target-site dynamics, offers a mechanistic framework linking dose, systemic exposure, and local pharmacology. By integrating tissue physiology, drug properties, and target interactions, target-site PBPK provides insights into heterogeneous tumor penetration, intracellular distribution, and variable target occupancy that plasma PK alone cannot capture. Clinical examples, such as PSMA-targeted radioligands and tyrosine kinase inhibitors, illustrate how these models can inform rational dose selection, optimize ligand design, and guide individualized therapy. As oncology moves toward mechanism-driven, biology-aligned development, target-site PBPK represents a pivotal tool for translating preclinical insights into patient-specific dosing strategies and for redefining the standard of precision pharmacology.

M2 macrophages are closely associated with an immunosuppressive tumor microenvironment (TME) and may influence drug response, but their clinical relevance in osteosarcoma (OS) remains to be comprehensively elucidated. This study developed an M2-related model to predict the risk, immune response, and drug sensitivity for patients with OS. Bulk RNA-seq data (TARGET-OS), microarray data (GSE21257), and scRNA-seq data (GSE162454) were obtained and analyzed. Single-cell data were processed using the Seurat package for cell annotation and cellular heterogeneity characterization. Intercellular communication networks were inferred using the CellChat R package. Next, based on M2 macrophage-associated genes identified through ssGSEA, we developed a four-gene prognostic model using WGCNA and LASSO Cox regression analysis. Prognostic performance of the four-gene model was evaluated by using Kaplan-Meier (KM) survival analysis and time-dependent ROC curves. Immune infiltration was assessed by ssGSEA, ESTIMATE, and MCP-counter, while drug sensitivity was predicted using oncoPredict. The scRNA-seq analysis identified myeloid and osteoblastic cells as the dominant cell populations in OS, with M2 macrophages exhibiting extensive intercellular crosstalk. M2 macrophage activity scores were computed for samples in the TARGET-OS via ssGSEA. Based on these scores, WGCNA identified the M2 module as a key module, which comprised 93 genes. Among these modular genes, four genes were selected by LASSO Cox regression analysis to establish a four-gene RiskScore. High- -risk patients showed worse survival (p < 0.05), which was also observed in the independent GSE21257 cohort. The high-risk group also exhibited lower ImmuneScore and reduced infiltration of T cells, B cells, dendritic cells (DCs), and macrophages. The RiskScore was correlated with predicted IC50 values for multiple drugs, including AZD8055_1059, suggesting a potential link between the M2 macrophage-related model and in silico drug sensitivity profiles. This study developed an M2 macrophage-related risk model based on LPAR5, MS4A4A, TNFSF8, and VSIG4, which was associated with survival outcomes, TME features, and predicted drug response profiles in OS. This study developed an M2 macrophage-related four-gene model that was closely related to the immune microenvironment features, drug sensitivity, and survival outcomes in OS. These findings offer preliminary insights into risk stratification and therapeutic treatment for OS.

Proteomics research provides significant insights for the diagnosis and treatment of diseases. Protein ratios reflect biological connections between related proteins and can help identify clinically relevant loci. Exploring the relationship between plasma protein-to-protein ratios and aneurysms may help identify new targets for prevention and treatment. We performed a two-sample Mendelian Randomization (MR) analysis to assess the associations between 2,821 protein ratios and various types of aneurysms. Twostep MR was utilized to investigate the potential mediating role of cardiometabolic factors. Through pathway enrichment analysis and drug target evaluation, we further elucidated the potential mechanisms and therapeutic targets related to aneurysms. After MR analysis, we identified 12 protein ratios with significant causal associations with aneurysms. Among them, seven protein ratios were associated with Thoracic Aortic Aneurysms (TAA), three with Aortic Aneurysms (AA), one with Abdominal Aortic Aneurysms (AAA), and one with Subarachnoid Hemorrhage (SAH). No significant causal associations were identified for unruptured intracranial aneurysm. We further performed a two-step MR analysis and found associations between cardiometabolic traits like blood pressure and lipids with aneurysms, confirming their potential mediating roles between protein ratios and aneurysms. Lastly, our drug target evaluation suggested that anti-inflammatory and lipid-lowering drugs are associated with aneurysm-related protein ratios. We identified 12 plasma protein ratios significantly associated with aneurysms, with blood pressure and lipids potentially mediating these associations. Additionally, our findings suggest that anti-inflammatory and lipid-lowering drugs are associated with the 12 plasma protein ratios, offering new insights for the treatment of aneurysms. Our findings elucidate the significant associations between 12 plasma protein ratios and aneurysms. These results provide new perspectives for the prevention and management of aneurysms.

Alzheimer's disease (AD) remains the leading cause of dementia worldwide, imposing an enormous and growing societal burden with more than 55 million people affected globally. Despite decades of intensive investigation, existing therapeutic options provide only modest symptomatic relief and fail to prevent or slow disease progression, emphasizing the critical need for interventions that target the fundamental molecular mechanisms of neurodegeneration. Pathologically, Alzheimer's disease is characterized by extracellular accumulation of amyloid-&#x3b2; plaques, intracellular neurofibrillary tangles formed by hyperphosphorylated tau, profound synaptic loss, chronic neuroinflammation, and extensive neuronal degeneration. Although amyloid-focused strategies have long dominated drug development, their limited clinical benefit and safety liabilities highlight the multifactorial nature of AD and the need to move beyond amyloid-centric paradigms. Protein kinases have emerged as key integrators of multiple pathogenic processes in AD, governing tau phosphorylation, amyloid precursor protein processing, synaptic signaling, and neuroimmune responses. Aberrant kinase signaling drives tau pathology and propagation, promotes amyloidogenic pathways, disrupts synaptic function, and perpetuates inflammatory cascades. While extensive work on kinases such as GSK-3&#x3b2;, CDK5, JNKs, and CSF1R has firmly established the relevance of kinase dysregulation in AD, no kinase-directed therapy has yet translated into clinical success. This review highlights emerging kinase targets beyond these classical pathways, including Fyn, Casein Kinase 1 Delta (CK1&#x3b4;), Tau-Tubulin Kinase 1 (TTBK1), and Dual Leucine Zipper Kinase (DLK), which are supported by mechanistic insights and compelling preclinical evidence. Continued advances in brain-penetrant, isoform-selective, and mechanism-driven kinase inhibitor design may enable the development of next-generation disease-modifying therapies for Alzheimer's disease.

Metal complexes offer unique opportunities as scaffolds in chemical biology and drug discovery, with tunable geometries, modular coordination environments, and structural features not readily accessible with organic molecules. Here, we introduce reactive metallo-scaffolds (r-mS) as a class of metal complexes designed to map ligandable cysteines across the mammalian proteome. These covalent warhead-bearing metal complexes use the metal centre and ligand architecture to modulate cysteine engagement, with cysteine labelling occurring through the electrophilic chloroacetamide warhead. Using chemoproteomics, we profiled a focused r-mS series in HEK293T lysate, identifying novel cysteine ligandability and demonstrating how metal identity, arene substituents and overall molecular topography govern cysteine reactivity and proteome-wide targeting. Among the series screened, r-mS-2 emerged as the most productive scaffold, which engaged cysteine 119 within the functionally relevant SAM-binding domain of PRMT1. This interaction was validated by intact protein LC-MS and was determined to functionally inhibit the activity of PRMT1. Structural modelling and docking provided insights into the molecular basis of binding, which implied &#x3c0;-stacking and electrostatic complementarity in driving covalent engagement. Together, these results position reactive metallo-scaffolds (r-mS) as a versatile platform for proteome-wide covalent ligand discovery and the rational development of next-generation metallodrugs.

Interpretable machine learning approaches to quantitative structure-activity relationship (QSAR) modelling are increasingly applied in drug discovery, yet most studies remain confined to single targets and report feature attributions without translating them into chemically meaningful insights. We introduce a cross-target SHAP entropy framework for quantifying shared versus target-specific structure-activity relationships across protein families, applied to 31 human kinase targets from BindingDB under scaffold-based train-test evaluation. Random Forest classifiers trained on Morgan ECFP4 fingerprints achieved a median AUROC of 0.994, AUPRC of 0.9998, and MCC of 0.674, confirming genuine SAR learning beyond class prevalence exploitation. Pairwise Spearman rank correlation of mean absolute SHAP profiles across targets yielded moderate cross-target consistency (mean r&#x2009;=&#x2009;0.332; 465 pairs). Shannon entropy-based classification of the top 200 fingerprint bits identified 15 consensus features dominated by aromatic N-heterocycles, aliphatic rings, and hydrogen bond environments, and 50 divergent features showing 2.8-fold higher SHAP magnitude than consensus features. SAR validation confirmed genuine enrichment of two top consensus fragments in active compounds. These findings indicate that kinase QSAR models share a low-magnitude consensus descriptor signal across the kinase family, while target-specific features dominate predictive decision boundaries. All SHAP attributions describe model decision behavior and should not be interpreted as causal binding mechanisms. The entropy decomposition framework is generalisable to other protein families and provides a transferable workflow for converting SHAP outputs into chemically actionable insights. All code and data are publicly available.

Single-target therapies face growing constraints from complex disease etiologies, dose-limiting toxicities, and drug resistance. Integrating multi-target natural products into conventional regimens offers a promising synergistic strategy. Naringenin and naringin, well-defined flavanones with broad bioactive properties, exhibit multi-target profiles that make them attractive candidates for drug combination. This review consolidates evidence on the synergistic efficacy and organ-protective effects of these flavonoids combined with clinically approved drugs. Through pleiotropic mechanisms, these combinations enhance therapeutic efficacy while mitigating adverse effects. We systematically discuss the underlying pharmacological mechanisms, synthesizing a framework that decodes the interplay between pharmacokinetic modulation and pharmacodynamic engagement. Overall, these findings highlight the potential of naringenin and naringin as versatile adjuvants, providing insights for developing improved, multi-target clinical regimens.

Medulloblastoma (MB), the most common malignant pediatric brain tumor, is classified into four molecular groups: WNT, SHH, G3, and G4. G3 and G4 MBs are characterized by poor prognosis and high metastatic potential. Although USP2 (ubiquitin-specific peptidase 2) has been described as a prognostic biomarker and a promising therapeutic target in several cancer types, its role in the pathogenesis of G3 and G4 MBs remains unexplored. In this study, USP2 expression was investigated through in silico analyses, public datasets (GSE85217), and tumor samples from 54 pediatric MB patients. In addition, the effects of USP2 inhibition were evaluated in two G3/G4 MB cell lines (D283 Med and USP-13) using ML364, a selective USP2 inhibitor. USP2 was overexpressed in G3 and G4 MBs and was associated with metastasis and unfavorable clinical outcomes. Furthermore, ML364 treatment reduced USP2 protein expression level, as well as cell viability, migration, invasion, and colony-forming capacity in both cell lines. These findings indicate that USP2 is upregulated in pediatric G3 and G4 MBs and support its potential role as a biomarker of poor prognosis and a therapeutic target in these molecular subgroups.

Predicting cancer drug responses (CDRs) accurately remains a significant challenge due to the complexity of tumor biology and the limitations of existing "black-box" machine learning models. To address this, we propose ProphDR, an interpretable deep learning framework that integrates multiomics data and drug structural information using a hierarchical attention mechanism. ProphDR incorporates a Criss-Cross Gene-level Multiomics Integration (CGMI) module to capture gene-level features and a cross-attention (CA) module to model drug-gene interactions. Evaluated on datasets from GDSC and CCLE, ProphDR achieves state-of-the-art performance in predicting ln(IC50) values (PCC = 0.938, RMSE = 0.978) and classifying drug sensitivity (AUC = 0.981). It also demonstrates strong generalizability in cold-start scenarios involving unseen drugs or cell lines. Crucially, ProphDR generates biologically interpretable attention maps that highlight key pharmacophores and resistance-related genes such as ERBB2 (HER2), consistent with established mechanisms in NSCLC and BRCA. These insights bridge genomic features with phenotypic outcomes, offering valuable guidance for target prioritization and drug repurposing. ProphDR represents a robust and explainable AI tool for advancing precision oncology.

Bacillus cereus is a major foodborne pathogen characterized by robust biofilm formation and increasing antimicrobial resistance. This study identified 2-Methoxycinnamaldehyde (MCA) from Toona sinensis as the principal antibacterial compound and evaluated its inhibitory mechanisms and preservation potential. MCA exhibited potent activity against B. cereus ATCC 11778 (MIC&#x202f;=&#x202f;150&#x202f;&#x3bc;g/mL; MBC&#x202f;=&#x202f;200&#x202f;&#x3bc;g/mL) and resistant strains. In vitro assays demonstrated that MCA induced concentration-dependent membrane disruption, DNA damage, and intracellular protein leakage. Furthermore, treatment at 1 MIC and 2 MIC significantly impeded biofilm maturation; the secretion of extracellular proteins was reduced by 45.3% and 54.5%, polysaccharides by approximately 93%, and extracellular DNA (eDNA) by over 99%. Correspondingly, biofilm metabolic activity declined by 85.9% and 90.3%, and initial cellular adhesion was reduced by up to 88.1%. Untargeted metabolomic analysis revealed that these phenotypic defects stem from profound disturbances in amino acid biosynthesis, the TCA cycle, and nucleotide metabolism. Molecular docking revealed that MCA targets multiple essential bacterial enzymes, including ribonucleotide reductase, lysyl-tRNA synthetase, pyruvate kinase, betaine aldehyde dehydrogenase, and 5'-nucleotidase, through stable hydrogen bonding and &#x3c0;-interactions. In a refrigerated pork model, MCA completely eliminated B. cereus by day 7 while significantly delaying pH increases and color deterioration. These findings provide the first evidence for MCA as an antibacterial and antibiofilm agent against B. cereus, highlighting its potential in food preservation.

Pseudomonas aeruginosa biofilm polymer matrix formation contributes to antibiotic tolerance. The antibiofilm effects of sub-minimum inhibitory concentrations (MICs) of ceftriaxone (CTX), the molecular mechanisms by which these sub-MICs modulate biofilm polymer production and quorum sensing (QS), and the binding interactions of CTX with key biofilm regulatory proteins (LasR and RhlR QS receptors) have not been previously investigated. To determine the role of sub-MIC CTX in regulating biofilm polymer matrix formation, bacterial adhesion, QS gene expression (rhlR and lasR), and to perform molecular docking analysis of CTX interactions with LasR and RhlR QS receptor proteins and biofilm EPS polymer-associated targets. MICs and biofilm formation were determined. The effects of CTX sub-MICs on biofilm formation, adhesion to mouse bladder epithelial cells (BECs), and QS gene expression (rhlR and lasR, by qRT-PCR) were assessed. In silico molecular docking of CTX against the ligand-binding domains of LasR (PDB: 2UV0) and RhlR (PDB: 3T5K) was performed using AutoDock Vina. Interaction fingerprinting with biofilm EPS polymer-associated enzymes (AlgD and PelB) was also performed. CTX sub-MICs regulated biofilm formation in an isolate-dependent manner, reduced P.&#xa0;aeruginosa adhesion to mouse BECs, and downregulated the rhlR and lasR genes in a concentration-dependent manner. Molecular docking revealed that CTX binds favorably within the ligand-binding pockets of LasR (-8.3 kcal/mol) and RhlR (-7.1 kcal/mol) via hydrogen bonding and hydrophobic interactions, suggesting competitive interference with QS autoinducer binding. CTX also exhibited affinity for AlgD (-7.6 kcal/mol), a key enzyme in alginate polymer biosynthesis. CTX sub-MICs modulate biofilm EPS polymer matrix formation and epithelial adhesion by downregulating QS regulatory genes. lasR was more responsive to CTX sub-MIC stress than rhlR. Molecular docking supports a direct molecular interaction mechanism through which CTX may interfere with QS receptor signaling and alginate polymer biosynthesis, providing a structural basis for its antibiofilm activity at sub-inhibitory concentrations.

Osteoporosis (OP) poses a significant health burden. Circulating proteins represent promising drug targets. However, previous Mendelian randomization (MR) studies have limited evidence for their causal role due to single-source protein quantitative trait loci (pQTL) data and lack of systematic, multi-layered validation. To identify and prioritize circulating proteins causally implicated in OP using a proteome-wide MR framework integrating five pQTL datasets, Bayesian colocalization, transcriptome-wide analyses, and single-cell expression mapping. A two-stage (discovery and replication) proteome-wide MR analysis was performed using cis-pQTLs from five large-scale studies (n&#x202f;=&#x202f;2968 unique proteins). Associations with four bone mineral density (BMD) traits (total body, femoral neck, lumbar spine, and forearm) were assessed and validated in three independent OP case-control datasets. Significant proteins underwent Bayesian colocalization (coloc.abf and coloc.susie), summary-data-based MR (SMR), and heterogeneity in dependent instruments (HEIDI) tests. A systematic evidence-tiered framework (Tiers 1-3) was applied. Single-cell RNA sequencing mapped cell-type-specific expression of prioritized genes. Seven circulating proteins showed significant MR associations with BMD. LRP4 was identified as a Tier 1 (high-confidence) target, demonstrating robust colocalization (PPH4&#x202f;=&#x202f;0.915) and passing SMR/HEIDI criteria. TNFSF11 and IBSP were classified as Tier 2 (moderate-confidence) targets (TNFSF11: PPH4&#x202f;=&#x202f;0.635; IBSP showed significant MR associations but lacked colocalization evidence). Remaining proteins (GCKR,ANPEP,ANGPTL7,LYAR) were categorized as Tier 3 (low-confidence). Single-cell analysis revealed enriched expression of TNFSF11, LRP4, and IBSP in bone marrow mesenchymal stem cells. This study provides a rigorous, multi-layered prioritization of circulating proteins causally implicated in OP. LRP4 emerges as the strongest candidate, with TNFSF11 moderately supported. These findings offer prioritized targets for future drug development and experimental validation.

Previous observational studies on the relationship between proton pump inhibitors (PPIs) and asthma have yielded controversial results, with the underlying mechanisms remaining unclear. This study aimed to elucidate the causal relationship between PPIs and asthma using Mendelian randomization (MR) approaches. Instrumental variables proxying the expression of PPI target genes were derived from the eQTLGen Consortium and GTEx project. Asthma summary statistics were obtained from the Trans-National Asthma Genetic Consortium (TAGC) and FinnGen consortium. Drug-target MR and summary-data-based MR (SMR) analyses were employed to assess the causal associations between PPI target genes and asthma. Two-sample MR was further conducted to evaluate the causal effects of inflammatory proteins on asthma, followed by mediation analysis to explore potential mediators. A series of sensitivity analyses and colocalization analyses were performed to test the robustness of our findings. Genetically proxied elevation of MAPT and DDAH1 levels was associated with an increased risk of asthma, while elevated AHR levels were linked to a decreased risk. The SMR analysis further supported the association between MAPT and asthma. We also identified causal associations between four inflammatory proteins and asthma. Mediation analysis suggested that interleukin-2 (IL-2) may serve as a potential mediator in the pathways from AHR and MAPT to asthma, with estimated mediation proportions of 26.5% and 28.7%, respectively. This study provides genetic evidence supporting causal links between PPI-related genes (AHR, DDAH1, MAPT) and asthma. IL-2 may play a mediating role in the pathways connecting AHR and MAPT to asthma, offering novel insights into the potential mechanisms underlying the PPI-asthma relationship.

Type 2 diabetes (T2D) is a major global health problem driven largely by insulin resistance, the impaired cellular response to insulin. Few current therapies directly address this underlying cause. Thiazolidinediones, potent peroxisome proliferator-activated receptor gamma (PPAR&#x3b3;) agonists, remain the only true small-molecule insulin sensitisers, but their clinical use is limited by adverse effects associated with non-selective activation of this target. Consequently, research has shifted toward alternative pathways that enhance insulin signalling without relying on PPAR&#x3b3; agonism. This review summarises advances from 2013 to 2025 in the development of novel small-molecule insulin sensitisers across diverse scaffolds and mechanisms. Structure-activity relationships, established and emerging molecular targets, and structural insights that inform rational drug design are highlighted. Selected leads were also evaluated using the BOILED-Egg model to assess oral drug-likeness and early "technology readiness." Overall, this review aims to inspire medicinal chemists by presenting promising leads and strategies for the development of next-generation insulin-sensitising therapeutics.

Current therapeutic strategies for senile osteoporosis inadequately address its low-turnover pathology driven by mitochondrial dysfunction and cellular senescence. This study identifies menaquinone-7 (MK-7), a vitamin K2 isoform, as a novel therapeutic agent targeting mitochondrial homeostasis in senile osteoporosis. Through RNA sequencing analysis and intramedullary adeno-associated virus (AAV)-based gene manipulation in aged mice, cellular communication network factor 2 (Ccn2) was identified as a critical mediator of MK-7's bone-protective effects. Biochemical and proteomic assays revealed that MK-7 binds to the nuclear receptor pregnane X receptor (PXR), activating the extracellular signal-regulated kinases 1/2 (ERK1/2)/cyclic AMP-responsive element-binding protein (CREB) signaling cascade to upregulate Ccn2 in senescent bone marrow mesenchymal stem cells (BMSCs). This pathway enhanced PTEN-induced kinase 1 (PINK1)/Parkin-mediated mitophagy, reducing mitochondrial DNA damage, reactive oxygen species (mtROS), and senescence-associated secretory phenotype (SASP), while restoring metabolic function. MK-7 redirected BMSC differentiation from adipogenic to osteogenic lineages, effectively mitigating age-related bone loss in vivo. Mechanistically, MK-7 stabilized PXR via direct interaction at the F285 residue, as confirmed by drug affinity responsive target stability (DARTS), cellular thermal shift assay (CETSA), and molecular docking. PXR activation further promoted ERK1/2/CREB-dependent Ccn2 expression, which orchestrated mitochondrial quality control and cellular energy metabolism. Our findings establish MK-7 as a dual-function agent that concurrently alleviates senescence and metabolic imbalance in bone tissue, offering a safe and targeted strategy for senile osteoporosis. This study provides critical insights into the pharmacological modulation of mitochondrial pathways and highlights MK-7's translational potential in geriatric bone health.