Search Results (285)

Botulinum neurotoxins (BoNTs) are known to inhibit synaptic transmission by targeting SNARE proteins, but their selectivity toward central excitatory and inhibitory pathways is not yet fully understood. In this study, the interaction of serotypes A (BoNT/A) and B (BoNT/B) with the glutamatergic and GABAergic systems has been investigated using a pharmacological approach in an animal model of inflammatory pain, i.e., the formalin test in mice. BoNTs were administered intracerebroventricularly, three days before testing, followed 15 min before testing by systemic administration of sub-analgesic doses of MK801, an NMDA receptor antagonist, or muscimol, a GABA_A receptor agonist. BoNT/A reduced the second phase of the formalin test without affecting both the first phase and the interphase, suggesting a selective action on excitatory glutamatergic circuits while sparing GABAergic inhibition. Co-administration of MK801 with BoNT/A did not enhance analgesia, and muscimol did not further reduce interphase, confirming preserved GABAergic transmission. In contrast, BoNT/B abolished the interphase, consistent with impaired GABA release. Co-administration of MK801 or muscimol with BoNT/B restored the interphase, indicating compensatory rebalancing of excitatory-inhibitory networks. These results demonstrate that BoNT/A and BoNT/B exert distinct effects on central neurotransmission and support the hypothesis that BoNT/A preferentially targets excitatory synapses, while BoNT/B targets inhibitory synapses. This work contributes to a deeper understanding of anti-inflammatory mechanisms of BoNTs and their selective interaction with central pain pathways. Full article

The serotonergic system, originating in the raphe nuclei, differentially modulates the dorsal and ventral hippocampus, which are implicated in cognition and emotion, respectively. Emerging evidence from rodent models (e.g., neonatal ventral hippocampal lesion, pharmacological NMDA receptor antagonist exposure) and human postmortem studies indicates dorsoventral serotonergic alterations in schizophrenia. These data include elevated 5-HT1A receptor expression in the dorsal hippocampus, linking serotonergic hypofunction to cognitive deficits, and hyperactive 5-HT2A/3 receptor signaling and denser serotonergic innervation in the ventral hippocampus driving local hyperexcitability associated with psychosis and stress responsivity. These dorsoventral serotonergic alterations are shown to disrupt the excitation–inhibition balance, impair synaptic plasticity, and disturb network oscillations, as established by in vivo electrophysiology and functional imaging. Synthesizing these multi-level findings, we propose a novel “dorsoventral serotonin imbalance” model of schizophrenia, in which ventral hyperactivation predominantly contributes to psychotic symptoms and dorsal hypoactivity underlies cognitive deficits. We further highlight promising preclinical evidence that selective targeting of region- and receptor-specific targeting, using both pharmacological agents and emerging delivery technologies, may offer novel therapeutic opportunities enabling symptom-specific strategies in schizophrenia. Full article

Objectives: Saffron, a traditional Chinese medicine, is renowned for its pharmacological effects in promoting blood circulation, resolving blood stasis, regulating menstruation, detoxification, and alleviating mental disturbances. Trans-crocetin, its principal bioactive component, exhibits significant anti-hypoxic activity. The clinical development and therapeutic efficacy of trans-crocetin are limited by its instability, poor solubility, and low bioavailability. Conversion of trans-crocetin into trans-sodium crocetinate (TSC) enhances its solubility, stability, and bioavailability, thereby amplifying its anti-hypoxic potential. Methods: This study integrates network pharmacology with in vivo and in vitro validation to elucidate the molecular targets and mechanisms underlying TSC’s therapeutic effects against high-altitude acute lung injury (HALI), aiming to identify novel treatment strategies. Results: TSC effectively reversed hypoxia-induced biochemical abnormalities, ameliorated lung histopathological damage, and suppressed systemic inflammation and oxidative stress in HALI rats. In vitro, TSC mitigated CoCl₂-induced hypoxia injury in human pulmonary microvascular endothelial cells (HPMECs) by reducing inflammatory cytokines, oxidative stress, and ROS accumulation while restoring mitochondrial membrane potential. Network pharmacology and pathway analysis revealed that TSC primarily targets the EGFR/PI3K/AKT/NF-κB signaling axis. Molecular docking and dynamics simulations demonstrated stable binding interactions between TSC and key components of this pathway. ELISA and RT-qPCR confirmed that TSC significantly downregulated the expression of EGFR, PI3K, AKT, NF-κB, and their associated mRNAs. Conclusions: TSC alleviates high-altitude hypoxia-induced lung injury by inhibiting the EGFR/PI3K/AKT/NF-κB signaling pathway, thereby attenuating inflammatory responses, oxidative stress, and restoring mitochondrial function. These findings highlight TSC as a promising therapeutic agent for HALI. Full article

This study aims to explore the anti-inflammatory pharmacological components and anti-inflammatory mechanisms of the alcohol extract of Saposhnikoviae Radix (SR). The components of the alcohol extract of SR were analyzed using the UPLC-MS/MS system. The anti-inflammatory efficacy of the alcohol extract and core components of SR was evaluated using the LPS-induced inflammation model of RAW264.7 cells. The anti-inflammatory mechanism of SR in a mouse model of rheumatoid arthritis was expounded by means of serum metabolomics, network pharmacology, and molecular docking. A total of 12 chromones and 13 coumarins were identified in the alcohol extract of SR. The alcohol extract of SR and its components all had good anti-inflammatory activities. In the mouse model of rheumatoid arthritis, the glycoside compounds of SR were transformed into aglycones, thereby exerting anti-inflammatory effects. Moreover, the alcohol extract of SR alleviated the inflammatory response by up-regulating the expression levels of metabolites such as phenylalanine and tyrosine. Network pharmacology and molecular docking results show that SR could exert an anti-inflammatory effect by regulating AGE-RAGE, PI3K-Akt, TNF, MAPK, and Toll-like signaling pathways. In this study, the anti-inflammatory efficacy and mechanisms of the alcohol extract of SR are explored, with the aim of providing a reference for subsequent research. Full article

Traditional Chinese medicine (TCM), a holistic medical system rooted in dialectical theories and natural product-based therapies, has served as a cornerstone of healthcare systems for millennia. While its empirical efficacy is widely recognized, the polypharmacological mechanisms stemming from its multi-component nature remain poorly characterized. The conventional trial-and-error approaches for bioactive compound screening from herbs raise sustainability concerns, including excessive resource consumption and suboptimal temporal efficiency. The integration of artificial intelligence (AI) and multi-omics technologies with network pharmacology (NP) has emerged as a transformative methodology aligned with TCM’s inherent “multi-component, multi-target, multi-pathway” therapeutic characteristics. This convergent review provides a computational framework to decode complex bioactive compound–target–pathway networks through two synergistic strategies, (i) NP-driven dynamics interaction network modeling and (ii) AI-enhanced multi-omics data mining, thereby accelerating drug discovery and reducing experimental costs. Our analysis of 7288 publications systematically maps NP-AI–omics integration workflows for natural product screening. The proposed framework enables sustainable drug discovery through data-driven compound prioritization, systematic repurposing of herbal formulations via mechanism-based validation, and the development of evidence-based novel TCM prescriptions. This paradigm bridges empirical TCM knowledge with mechanism-driven precision medicine, offering a theoretical basis for reconciling traditional medicine with modern pharmaceutical innovation. Full article

Polypharmacology, the rational design of small molecules that act on multiple therapeutic targets, offers a transformative approach to overcome biological redundancy, network compensation, and drug resistance. This review outlines the scientific rationale for polypharmacology, highlighting its success across oncology, neurodegeneration, metabolic disorders, and infectious diseases. Emphasis is placed on how polypharmacological agents can synergize therapeutic effects, reduce adverse events, and improve patient compliance compared to combination therapies. We also explore how computational methods—spanning ligand-based modeling, structure-based docking, network pharmacology, and systems biology—enable target selection and multi-target ligand prediction. Recent advances in artificial intelligence (AI), particularly deep learning, reinforcement learning, and generative models, have further accelerated the discovery and optimization of multi-target agents. These AI-driven platforms are capable of de novo design of dual and multi-target compounds, some of which have demonstrated biological efficacy in vitro. Finally, we discuss the integration of omics data, CRISPR functional screens, and pathway simulations in guiding multi-target design, as well as the challenges and limitations of current AI approaches. Looking ahead, AI-enabled polypharmacology is poised to become a cornerstone of next-generation drug discovery, with potential to deliver more effective therapies tailored to the complexity of human disease. Full article

The significant burden of polypharmacy in clinical settings contrasts sharply with the narrow research focus on drug–drug interactions (DDIs), revealing an important gap in understanding the complexity of real-world multi-drug regimens. The present study addresses this gap by conducting a high-resolution, multidimensional bibliometric and network analysis of 19,151 DDI publications indexed in the Web of Science Core Collection (1975–2025). Using advanced tools, including VOSviewer version 1.6.20, Bibliometrix 5.0.0, and AI-enhanced terminology normalization, global research trajectories, knowledge clusters, and collaborative dynamics were systematically mapped. The analysis revealed an exponential growth in publication volume (from 55 in 1990 to 1194 in 2024), with output led by the United States and a marked acceleration in Chinese contributions after 2015. Key pharmacological agents frequently implicated in DDI research included CYP450-dependent drugs such as statins, antiretrovirals, and central nervous system drugs. Thematic clusters evolved from mechanistic toxicity assessments to complex frameworks involving clinical risk management, oncology co-therapies, and pharmacokinetic modeling. The citation impact peaked at 3.93 per year in 2019, reflecting the increasing integration of DDI research into mainstream areas of pharmaceutical science. The findings highlight a shift toward addressing polypharmacy risks in aging populations, supported by novel computational methodologies. This comprehensive assessment offers insights for researchers and academics aiming to navigate the evolving scientific landscape of DDIs and underlines the need for more nuanced system-level approaches to interaction risk assessment. Future studies should aim to incorporate patient-level real-world data, expand bibliometric coverage to underrepresented regions and non-English literature, and integrate pharmacogenomic and time-dependent variables to enhance predictive models of interaction risk. Cross-validation of AI-based approaches against clinical outcomes and prospective cohort data are also needed to bridge the translational gap and support precision dosing in complex therapeutic regimens. Full article

Background/Objectives: Understanding the molecular interactions between small bioactive compounds and serum albumins is essential for drug development and pharmacokinetics. Noraucuparin, a biphenyl-type phytoalexin with promising pharmacological properties, has shown a strong binding affinity to bovine serum albumin (BSA), a model protein for drug transport. This study aims to elucidate the structural and energetic characteristics of the noraucuparin–BSA complex under physiological and slightly elevated temperatures. Methods: Microsecond-scale molecular dynamics (MD) simulations and Molecular Mechanics Generalized Born Surface Area (MMGBSA)-binding-free energy calculations were performed to investigate the interaction between noraucuparin and BSA at 298 K and 310 K. Conformational flexibility and per-residue energy decomposition analyses were conducted, along with interaction network mapping to assess ligand-induced rearrangements. Results: Noraucuparin preferentially binds to site II of BSA, near the ibuprofen-binding pocket, with stabilization driven by hydrogen bonding and hydrophobic interactions. Binding at 298 K notably increased the structural mobility of BSA, affecting its global conformational dynamics. Key residues, such as Trp213, Arg217, and Leu237, contributed significantly to complex stability, and the ligand induced localized rearrangements in the protein’s intramolecular interaction network. Conclusions: These findings offer insights into the dynamic behavior of the noraucuparin–BSA complex and enhance the understanding of serum albumin–ligand interactions, with potential implications for drug delivery systems. Full article

Arteriovenous malformations (AVMs) are congenital vascular anomalies defined by abnormal direct connections between arteries and veins due to their complex structure or endovascular approaches. Pharmacological strategies targeting the underlying molecular mechanisms are thus gaining increasing attention in an effort to determine the mechanism involved in AVM regulation. In this study, we examined 30 human tissue samples, comprising 10 vascular samples, 10 human fibroblasts derived from AVM tissue, and 10 vascular samples derived from healthy individuals. The pharmacological agents thalidomide, U0126, and rapamycin were applied to the isolated endothelial cells (ECs). The pharmacological treatments reduced the proliferation of AVM ECs and downregulated miR-135b-5p, a biomarker associated with AVMs. The expression levels of angiogenesis-related genes, including VEGF, ANG2, FSTL1, and MARCKS, decreased; in comparison, CSPG4, a gene related to capillary networks, was upregulated. Following analysis of these findings, skin samples from 10 AVM patients were reprogrammed into induced pluripotent stem cells (iPSCs) to generate AVM blood vessel organoids. Treatment of these AVM blood vessel organoids with thalidomide, U0126, and rapamycin resulted in a reduction in the expression of the EC markers CD31 and α-SMA. The establishment of AVM blood vessel organoids offers a physiologically relevant in vitro model for disease characterization and drug screening. The authors of future studies should aim to refine this model using advanced techniques, such as microfluidic systems, to more efficiently replicate AVMs’ pathology and support the development of personalized therapies. Full article

Background: Acute lung injury (ALI) is driven by inflammatory cascades and reactive oxygen species (ROS) generation, with the progression to severe cases markedly increasing mortality. Sinapine thiocyanate (ST), a bioactive natural compound isolated from Sinapis Semen Albae (SSA), demonstrates both anti-inflammatory and antioxidant pharmacological activities. However, no monotherapeutic formulation of ST has been developed to date. A dry powder inhaler (DPI) enables targeted pulmonary drug delivery with excellent stability profiles and high inhalation efficiency. Methods: ST was purified and prepared as inhalable dry powder particles via an antisolvent crystallization technique. The therapeutic mechanisms of ST against ALI were elucidated by network pharmacology and pharmacokinetic analyses, with the therapeutic efficacy of the ST DPI in ALI mitigation being validated using LPS-induced rat models. Results: The ST DPI showed ideal aerodynamic characteristics. Notably, ST exhibited a three-compartment (triexponential) pharmacokinetic profile following both intravenous tail vein injection and inhalation administration. Furthermore, the inhaled formulation displayed a prolonged systemic residence time, which confers therapeutic advantages for pulmonary disease management. Furthermore, the inhalation administration of ST demonstrated a 2.7-fold increase in AUC compared with oral gavage, with a corresponding enhancement in systemic exposure. The ST DPI formulation demonstrated significant therapeutic efficacy against ALI in rats by downregulating inflammatory cytokines and modulating oxidative stress levels, mechanistically achieved through the MAPK-mediated regulation of cellular apoptosis via a positive feedback loop. Conclusions: The unique triexponential plasma level profiles of an ST DPI provide a promising pharmacokinetics-based therapeutic strategy for ALI, leveraging its marked efficacy in attenuating inflammation, oxidative stress, and pulmonary injury. Full article

Memory impairment, ranging from mild memory impairment to neurodegenerative diseases such as Alzheimer’s disease, poses an escalating global health challenge that necessitates multi-targeted interventions to prevent progression. Health functional foods (HFFs), which include bioactive dietary compounds that not only provide basic nutrition but also function beyond that to modulate physiological pathways, offer a promising non-pharmacological strategy to preserve memory function. This review presents an integrative framework for the discovery, evaluation, and clinical translation of biomarkers responsive to HFFs in the context of preventing memory impairment. We examine both established clinical biomarkers, such as amyloid-β and tau in the cerebrospinal fluid, neuroimaging indicators, and memory assessments, as well as emerging nutritionally sensitive markers including cytokines, microRNAs, gut microbiota signatures, epigenetic modifications, and neuroactive metabolites. By leveraging systems biology approaches, we explore how network pharmacology, gut–brain axis modulation, and multi-omics integration can help to elucidate the complex interactions between HFF components and memory-related pathways such as neuroinflammation, oxidative stress, synaptic plasticity, and metabolic regulation. The review also addresses the translational pipeline for HFFs, from formulation and standardization to regulatory frameworks and clinical development, with an emphasis on precision nutrition strategies and cross-disciplinary integration. Ultimately, we propose a paradigm shift in memory health interventions, positioning HFFs as scientifically validated compounds for personalized nutrition within a preventative memory function framework. Full article

Lycopene exhibits a broad spectrum of biological activities with potential therapeutic applications. Despite its established antioxidant and anti-inflammatory properties, the molecular basis for its pharmacological actions remains incompletely defined. Here we investigated the molecular targets, pharmacodynamic feasibility, and tissue-specific expression of lycopene targets using a computational pharmacology approach combined with affinity and protein–protein interaction (PPI) analyses. Lycopene-associated human protein targets were predicted using a Swiss target screening platform. Molecular docking was used to estimate binding affinities, and concentration-affinity (CA) ratios were calculated based on physiologically relevant plasma concentrations (75–210 nM). PPI networks of lycopene targets were constructed to identify highly connected targets, and tissue expression analysis was assessed for high-affinity targets using protein-level data from the Human Protein Atlas database. Of the 94 predicted targets, 37% were nuclear receptors and 18% were Family A G Protein Coupled Receptors (GPCRs). Among the top 15 high-affinity targets, nuclear receptors and GPCRs comprised 40% and 26.7%, respectively. Twenty targets had affinities < 10 μM, with six key targets (MAP2K2, SCN2A, SLC6A5, SCN3A, TOP2A, and TRIM24) showing submicromolar binding. CA ratio analysis identified MAP2K2, SCN2A, and SLC6A5 as pharmacodynamically feasible targets (CA > 1). PPI analysis revealed 32 targets with high interaction and 9 with significant network connectivity. Seven targets (TRIM24, GRIN1, NTRK1, FGFR1, NTRK3, CHRNB4, and PIK3CD) showed both high affinity and centrality in the interaction network. The expression profiling of submicromolar targets revealed widespread tissue distribution for MAP2K2 and SCN3A, while SCN2A, TOP2A, and TRIM24 showed more restricted expression patterns. This integrative analysis identifies a subset of lycopene targets with both high affinity and pharmacological feasibility, particularly MAP2K2, SCN2A, and TRIM24. Lycopene appears to exert its biological effects through modulation of interconnected signalling networks involving nuclear receptors, GPCRs, and ion channels. These findings support the potential of lycopene as a multi-target therapeutic agent and provide a rationale for future experimental and clinical validation. Full article

Background/Objectives: Cardiac aging involves the progressive structural and functional decline of the myocardium. Endurance training is a well-recognized non-pharmacological intervention that counteracts this decline, yet the molecular mechanisms driving exercise-induced cardiac rejuvenation remain inadequately elucidated. This study aimed to identify key effector genes and regulatory pathways by integrating human cardiac aging transcriptomic data with multi-omic exercise response datasets. Methods: A systems biology framework was developed to integrate age-downregulated genes (n = 243) from the GTEx human heart dataset and endurance-exercise-responsive genes (n = 634) from the MoTrPAC mouse dataset. Thirty-seven overlapping genes were identified and subjected to Enrichr for pathway enrichment, KEA3 for kinase analysis, and ChEA3 for transcription factor prediction. Candidate effector genes were ranked using ToppGene and ToppNet, with integrated prioritization via the FLAMES linear scoring algorithm. Results: Pathway enrichment revealed complementary patterns: aging-associated genes were enriched in mitochondrial dysfunction and sarcomere disassembly, while exercise-responsive genes were linked to protein synthesis and lipid metabolism. TTN, PDK family kinases, and EGFR emerged as major upstream regulators. NKX2-5, MYOG, and YBX3 were identified as shared transcription factors. SMPX ranked highest in integrated scoring, showing both functional relevance and network centrality, implying a pivotal role in mechano-metabolic coupling and cardiac stress adaptation. Conclusions: By integrating cardiac aging and exercise-responsive transcriptomes, 37 effector genes were identified as molecular bridges between aging decline and exercise-induced rejuvenation. Aging involved mitochondrial and sarcomeric deterioration, while exercise promoted metabolic and structural remodeling. SMPX ranked highest for its roles in mechano-metabolic coupling and redox balance, with X-inactivation escape suggesting sex-specific relevance. Other top genes (e.g., KLHL31, MYPN, RYR2) form a regulatory network supporting exercise-mediated cardiac protection, offering targets for future validation and therapy. Full article

Plant secondary metabolites possess significant antioxidant and anti-inflammatory properties, but their complex polypharmacological mechanisms remain poorly understood. Network pharmacology has emerged as a powerful systems-level approach for investigating multi-target interactions of natural products. This review systematically analyzes network pharmacology applications in elucidating the antioxidant and anti-inflammatory mechanisms of plant metabolites, evaluating concordance between computational predictions and experimental validation. A comprehensive literature search was conducted across major databases (2015–2025), focusing on network pharmacology studies with experimental validation. Analysis revealed remarkable convergence toward common molecular mechanisms, despite diverse chemical structures. For antioxidant activities, the Nrf2/KEAP1/ARE pathway emerged as the most frequently validated mechanism, along with PI3K/AKT, MAPK, and NF-κB signaling. Anti-inflammatory mechanisms consistently involved NF-κB, MAPK, and PI3K/AKT pathways. Key targets, including AKT1, TNF-α, COX-2, NFKB1, and RELA, were repeatedly identified. Flavonoids, phenolic acids, and terpenoids dominated as bioactive compounds. Molecular docking studies supported predicted interactions, with experimental validation showing good concordance for pathway modulation and cytokine regulation. Network pharmacology provides a valuable framework for investigating the complex bioactivities of plant metabolites. The convergence toward common regulatory hubs suggests that natural compounds achieve protective effects by modulating central nodes that integrate redox balance and inflammatory responses. Despite limitations, including database dependency, integrating network pharmacology with experimental validation accelerates mechanistic understanding in natural-product drug discovery. Full article

Classical epidemic models treat vaccine uptake as an exogenous parameter, yet real-world coverage emerges from strategic choices made by individuals facing uncertain risks. During the last two decades, vaccination games, which combine epidemic dynamics with game theory, behavioural economics, and network science, have become a very important tool for analysing this problem. Here, we synthesise more than 80 theoretical, computational, and empirical studies to clarify how population structure, psychological perception, pathogen complexity, and policy incentives interact to determine vaccination equilibria and epidemic outcomes. Papers are organised along five methodological axes: (i) population topology (well-mixed, static and evolving networks, multilayer systems); (ii) decision heuristics (risk assessment, imitation, prospect theory, memory); (iii) additional processes (information diffusion, non-pharmacological interventions, treatment, quarantine); (iv) policy levers (subsidies, penalties, mandates, communication); and (v) pathogen complexity (multi-strain, zoonotic reservoirs). Common findings across these studies are that voluntary vaccination is almost always sub-optimal; feedback between incidence and behaviour can generate oscillatory outbreaks; local network correlations amplify free-riding but enable cost-effective targeted mandates; psychological distortions such as probability weighting and omission bias materially shift equilibria; and mixed interventions (e.g., quarantine + vaccination) create dual dilemmas that may offset one another. Moreover, empirical work surveys, laboratory games, and field data confirm peer influence and prosocial motives, yet comprehensive model validation remains rare. Bridging the gap between stylised theory and operational policy will require data-driven calibration, scalable multilayer solvers, and explicit modelling of economic and psychological heterogeneity. This review offers a structured roadmap for future research on adaptive vaccination strategies in an increasingly connected and information-rich world. Full article