Glioblastoma (GBM) is the most common type of primary malignant brain tumor, characterized by a poor prognosis, high recurrence rate, and elevated mortality. In recent years, gene-targeted therapies leveraging small molecule compounds have gained momentum as a promising avenue for GBM intervention. Myoferlin (MYOF), a type II membrane protein of the Ferlin family, has emerged as a key regulator of membrane dynamics-governing processes such as vesicular trafficking, endocytosis, and membrane repair. In this study, we explore the previously uncharted role of MYOF in GBM progression and its potential as a diagnostic and therapeutic target. Our data reveal that silencing MYOF markedly suppresses glioma growth both in vitro and in vivo. Mechanistically, MYOF knockdown disrupts the nuclear translocation of phosphorylated STAT3 (P-STAT3), a critical oncogenic signaling event. Notably, we identified Entacapone (ENT), a small molecule capable of targeting MYOF, which significantly impedes glioma development across experimental models. These findings position MYOF as a novel molecular lever in GBM pathogenesis and highlight ENT as a potential therapeutic agent that exerts anti-glioma effects by blocking MYOF-mediated P-STAT3 nuclear import.The diagram illustrates a novel regulatory mechanism of IL-6/STAT3 signaling involving MYOF. Upon IL-6 stimulation, STAT3 undergoes phosphorylation. MYOF then binds to phosphorylated STAT3 and facilitates its translocation into the nucleus to regulate target gene expression. The compound ENT acts as a targeted inhibitor of this process by binding to MYOF, thereby blocking the nuclear transport of phosphorylated STAT3 and suppressing downstream signaling.
Drug-induced reproductive toxicity is a critical concern in drug safety evaluation, whereas conventional assessment methods are often constrained by high costs and long experimental cycles. In this study, a machine learning-based predictive model for reproductive toxicity was developed and integrated with data from the FDA Adverse Event Reporting System (FAERS), network toxicology analysis, molecular docking, and molecular dynamics simulation to systematically evaluate the post-marketing reproductive toxicity risk of drugs and explore their potential mechanisms. Among the evaluated machine learning algorithms, LightGBM demonstrated the best overall performance, achieving an F1-score of 0.854, a ROC-AUC of 0.933, a PR-AUC of 0.931, and an MCC of 0.705 on the independent test set, with robust generalization confirmed by ten-fold cross-validation. Among drugs approved between 2015 and 2024, 72 were predicted to have a high risk of reproductive toxicity. FAERS-based signal comparison showed that 55 of these drugs (76.39%) were associated with reproductive toxicity-related adverse event reports, indicating consistency between model predictions and FAERS-reported reproductive toxicity-related adverse events. Network toxicology analysis identified 12 key targets, including ESR1, IGF1, and AKT1, that may be involved in reproductive toxicity. Molecular docking showed that drugs with high predicted reproductive toxicity risk could bind effectively to multiple toxicity-related targets, while molecular dynamics simulations confirmed stable interactions between selected drugs and ESR1, mainly through hydrogen-bonding and hydrophobic interactions. Favorable binding free energies further supported their potential multi-target effects. Overall, this integrated strategy combining predictive modeling with FAERS-based signal comparison provides a useful framework for drug safety evaluation and mechanistic investigation of reproductive toxicity.
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.
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.
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.
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 π-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.
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-β 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-3β, 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 (CK1δ), 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.
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 = 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.
The increased recognition of intrinsically disordered proteins (IDPs) as critical mediators in viral infections has shifted attention toward fuzzy drug targets, challenging the conventional structure-based paradigms of drug discovery due to their inherent conformational flexibility. The binding of viral IDPs and IDRs to host factors occurs in dynamic, multivalent, and context-dependent interactions and constitute flexible complexes, which form the basis of pathogenicity and immune evasion. In this review, the disordered proteins of viruses are considered with a combination of molecular, computational, and translational insights to assess the next-generation antiviral targets. In this, we have discuss about the energetic landscapes that control the disorder, functional disorder order transitions and the fuzzy interfaces as centers of the networks of virus-host interactions. Special focus is kept on the new lines of computational and AI-directed technologies such as ensemble-based docking, machine-learning computational models of IDP ligand recognition, and multi-omics-driven target prioritization. The experimental approaches that are modified to characterize disordered systems, including NMR spectroscopy and hybrid structural biology, are also reviewed. The translational applicability of the targeting of viral fuzziness is highlighted by case studies of HIV-1, influenza, SARS-CoV-2, and emerging viral pathogens. We also provide directions about the future involving adaptive pharmacophores, customized antiviral approaches, and AI-driven ensemble targeting making disordered viral proteins a paradigm shift in antiviral drug discovery.
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 = 150 μg/mL; MBC = 200 μg/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 π-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.
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.
Protein kinases play a key role in cellular signalling and are key drivers of neoplasia. In comparison, their role in neurodegenerative diseases used to be considered as secondary or downstream effects of neuronal damage. A growing body of data based on genetics, biochemistry, structural biology, and systems-level analysis has completely changed this view and placed kinase dysregulation as a common and unifying pathogenic pathway throughout cancer and neurodegeneration. The current chapter summarizes the recent progress that places kinases in a context-dependent state of molecular switches with disease-specific outcomes determined by structural conformation, spatiotemporal regulation, and network integration as opposed to kinase identity. The structural insights of high-resolution have changed the classical models that were based on pathways into models based on conformation and have shown that pathological kinase signalling is often caused by stabilisation of individual states of activity or regulation. These structural concepts describe the efficacy and drawbacks of first-generation ATP-competitive inhibitors, thereby leading to the development of allosteric, covalent, multi-target, and network-directed therapeutic approaches. The chapter also contrasts oncogenic kinase activation with oncogenic kinase dysfunction in post-mitotic neurons to demonstrate how the same signalling modules can be used to drive cell proliferation, cell survival, or cell degeneration depending on cellular context and microenvironment. The chapter also applies the knowledge in oncology, including mechanisms of resistance, adaptive signalling rewiring, and precision medicine, to neurodegenerative studies, but highlights the need for disease-specific adaptation due to the susceptibility and longevity of neurons.
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. 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.
Medicinal plants offer promising multi-target therapeutic agents due to their diverse bioactive compounds. This study explored LC-MS profiling, network pharmacology, molecular docking, and molecular dynamics simulation to investigate the potential antidepressant-related mechanisms of Leonotis leonurus and Mentha longifolia. A total of 20 and 15 compounds were identified in L. leonurus and M. longifolia, respectively. Target prediction and overlap analysis revealed 16 and 22 depression-associated targets for each plant. These targets were used to construct protein-protein interaction networks and perform Gene Ontology and KEGG pathway enrichment analyses. For L. leonurus, the key targets included SLC 6 A 4, PTGS 2, MAOA, and MAOB were enriched in serotonergic and dopaminergic synapse pathways, neuroactive ligand-receptor interaction, and PI 3 K/Akt signalling, suggesting, suggesting potential involvement in neurotransmission and neuroinflammatory regulation. Molecular docking showed strong interactions of procyanidin B5 and chrysoeriol with SLC 6 A 4 and MAOA, respectively, while baicalin demonstrated the most favourable binding affinity (- 39.45 kcal/mol), exceeding that of fluoxetine (- 14.69 kcal/mol), both surpassing fluoxetine's affinity (- 14.69 kcal/mol) to SLC 6 A 4. For M. longifolia, targets such as DRD 4, DRD 3, GSK 3 β, COMT, and AKT 1 were linked to dopaminergic synapse and neuroactive ligand-receptor pathways. Key compounds, including baicalin, salvianolic acid A, and rosmarinic acid, showed strong binding to DRD 4, DRD 3, and GSK 3 β. Notably, quercetin 3-galactoside exhibited the highest affinity toward DRD 4 (- 46.74 kcal/mol), outperforming fluoxetine (- 24.91 kcal/mol). Collectively, these findings suggest that both plants may possess antidepressant-related potential through predicted multi-component, multi-target modulation of neurotransmitter systems and neuroinflammatory pathways. However, these findings remain computational and require experimental validation to confirm biological efficacy and therapeutic relevance. This study provides a mechanistic framework to support future pharmacological investigations into the traditional use of these medicinal plants for mental health applications. The online version contains supplementary material available at 10.1007/s40203-026-00689-2.
Staphylococcus aureus is the leading pathogen responsible for hospital- and community-acquired infections. The increasing prevalence of nosocomial infections in healthcare settings presents a significant challenge, particularly due to the strong biofilm-forming capability of clinical strains, which contributes to biofilm-mediated multidrug resistance. The biofilm-associated protein (BAP) plays a pivotal role in the initial adhesion and maturation of biofilms, significantly increasing the likelihood of failure of conventional antimicrobial therapies. Given its crucial function in biofilm formation, BAP represents a promising target for anti-biofilm drug development. A high-throughput virtual screening technique was implemented to identify potent BAP inhibitors, utilizing triple-mode docking with the Glide module of the Schrödinger Maestro suite. About 28,831 compounds from the ENAMINE-targeted antibacterial library were screened against BAP in S. aureus. Among the selected ligands, Z1430813924 and Z1738791774 exhibited the lowest binding energy, demonstrating superior docking scores alongside favorable ADME and physicochemical properties, which suggests an enhanced inhibitory potential. To validate the docking findings, a 100-ns molecular dynamics simulation was employed to assess the stability of the protein-ligand complex within a dynamic environment. The essential dynamics analysis, including free energy landscape (FEL) and principal component analysis (PCA) evaluations, affirmed the stability and efficacy of the top compounds, Z1430813924 and Z1738791774, as promising BAP inhibitors. These insights provide a strong foundation for subsequent experimental validation and the potential development of novel anti-biofilm therapeutics targeting S. aureus infections.
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 (PPARγ) 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 PPARγ 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.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by profound cognitive decline, wherein chronic neuroinflammation plays a pivotal pathogenic role. Central to this inflammatory milieu is pyroptosis, a highly inflammatory form of programmed lytic cell death mediated by gasdermin proteins. This comprehensive review provides an in-depth synthesis of the cellular and molecular mechanisms underlying pyroptosis in AD. We detail the distinct roles of microglia as primary initiators responding to amyloid-beta (Aβ) and tau aggregates, alongside the specific vulnerabilities of neurons facing oxidative stress, astrocytes impacting metabolic support, and endothelial cells whose pyroptotic death contributes directly to blood-brain barrier disruption. At the molecular level, the priming and activation of the NLRP3 and NLRP1 inflammasomes by diverse triggers, including classical markers like Aβ, environmental neurotoxicants and metabolic stressors, converge on caspase-1 and caspase-8 activation. This cascade culminates in gasdermin D (GSDMD) and gasdermin E (GSDME) pore formation, leading to cellular lysis and the massive release of pro-inflammatory cytokines such as IL-1β and IL-18. Furthermore, this paper explores the emerging and critical concept of PANoptosis, highlighting the intricate crosstalk between pyroptosis, apoptosis, and necroptosis within PANoptosome complexes triggered by mitochondrial dysfunction. We evaluate current and prospective therapeutic strategies, ranging from multi-target natural and traditional herbal remedies to advanced nanomedicine, synthetic small molecules, and epigenetic gene therapies. By integrating insights from blood-based pyroptosis-associated molecular signatures and advanced targeted drug delivery systems, we emphasize the critical need for personalized, multi-targeted approaches to successfully harness pyroptosis modulation in the clinical management and treatment of AD.