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Results for “Network toxicology”

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11 recordsLinked to original sources

PubMed2026

Toxicological effects of propyl 4-hydroxybenzoate on gallstone pathogenesis: An integrated mendelian randomization, network toxicology, and experimental study.

BACKGROUND: Gallstone disease is a prevalent digestive disorder with substantial global socioeconomic burden. Propyl 4-hydroxybenzoate (PP), a widely used paraben preservative, exhibits potential metabolic and hepatic toxicity, yet its role in gallstone pathogenesis remains unclear. This study aimed to explore the causal association between PP exposure and gallstone formation and the underlying mechanism. METHODS: Two-sample Mendelian randomization (MR) was performed using genome-wide association study (GWAS) data. Network toxicology, molecular docking, and molecular dynamics simulation were applied to screen for core targets. In vivo experiments, transcriptome sequencing, Western blot (WB), and ELISA were conducted for mechanistic validation. RESULTS: MR confirmed a causal link between circulating PP levels and an elevated risk of gallstones (P < 0.05), with AKT1 identified as the key target. In mice, PP aggravated gallstone formation by activating the AKT1-NF-κB-CXCL1 pathway, enhancing hepatic inflammation and neutrophil extracellular traps (NETs) formation; these effects were reversed by AKT inhibition. CONCLUSION: PP promotes gallstone formation via the AKT1-NF-κB-CXCL1-NETs axis. Our findings highlight PP as an environmental risk factor for gallstones, providing novel insights into their prevention and targeted therapy.

Animals↗

PubMed2026

Multitarget interactions of bisphenol A in polycystic ovary syndrome: evidence from integrated network toxicology, mendelian randomization, and molecular docking.

OBJECTIVE: To study the potential pathogenic mechanisms of bisphenol A (BPA) in polycystic ovary syndrome (PCOS) using an integrative computational strategy. DESIGN: Integrative computational study combining network toxicology, Mendelian randomization (MR), and molecular docking. SUBJECTS: For MR analysis, genetic data were sourced from large European-ancestry cohorts, including plasma protein quantitative trait loci data and genome-wide association study summary statistics for PCOS (3,045 cases and 267,780 controls). EXPOSURE: In silico exposure to BPA for target prediction; genetically predicted plasma protein levels for causal inference. MAIN OUTCOME MEASURES: Identification of overlapping targets between BPA and PCOS; functional enrichment pathways; causal effects of prioritized proteins on PCOS risk (odds ratios with 95% confidence intervals); binding affinities between BPA and core targets (kcal/mol). RESULTS: Network toxicology identified 310 overlapping targets between BPA and PCOS. Enrichment analyses revealed significant involvement in endocrine signaling, inflammatory pathways (eg, IL-17), and cellular processes. MR demonstrated that genetically elevated levels of RET, CXCL8, HTR6, MMP1, MMP9, NTRK1, and TNNI2 were significantly associated with increased PCOS risk, whereas higher PSAP and SHBG levels were protective. Molecular docking confirmed stable binding between BPA and all nine key targets, with strongest affinity for SHBG (-8.4 kcal/mol), followed by NTRK1, TNNI2, and RET. CONCLUSION: This integrative investigation suggests that BPA may contribute to PCOS pathogenesis through multitarget interactions involving inflammatory mediators, endocrine regulators, and tissue remodeling proteins. The findings provide prioritized targets and mechanistic insights for future experimental validation and environmental risk assessment.

Female↗

PubMed2026

Analysis of the molecular mechanism underlying di(2-ethylhexyl) phthalate-induced bladder carcinogenesis via network toxicology and molecular docking approaches: An observational study.

This study aims to investigate the toxicity of di(2-ethylhexyl) phthalate (DEHP) and the potential molecular mechanisms of DEHP-induced bladder cancer (BLCA) using network toxicology and molecular docking strategies. The toxicity of DEHP was assessed using Prox-II software, and potential targets for DEHP-induced BLCA were identified by integrating data from ChEMBL database, Search Tool for Interactions of Chemicals, SwissTargetPrediction, GeneCards, Therapeutic Target Database, Online Mendelian Inheritance in Man, and The Cancer Genome Atlas. STRING database and Cytoscape were employed to construct target networks and determine core targets. The expression levels of core targets were analyzed using R. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed on potential and core targets. Molecular docking was carried out using CB-Dock 2 to verify the interactions between DEHP and core targets. A total of 105 potential targets related to DEHP-induced BLCA were identified, from which 7 core targets were selected: cyclin-dependent kinase 1, interleukin 6, cyclin-dependent kinase 2, cyclin B1, Erb-B2 receptor tyrosine kinase 2, cyclin B2, and B-cell lymphoma 2. IL-6 and B-cell lymphoma 2 showed downregulated expression in tumor tissues, while cyclin-dependent kinase 1, cyclin-dependent kinase 2, cyclin B1, Erb-B2 receptor tyrosine kinase 2, and cyclin B2 were upregulated. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses indicated that these targets were enriched in cell signaling and cancer-related pathways. Molecular docking confirmed that DEHP interacts with these core targets. DEHP may promote the development of BLCA by interacting with key proteins and signaling pathways. This study provides a theoretical basis for understanding the molecular mechanisms of DEHP-induced BLCA and offers references for future prevention and treatment strategies.

Diethylhexyl Phthalate↗

PubMed2025

Exploring potential targets and molecular mechanisms of traumatic brain injury exacerbated by Benzo(a)pyrene via network toxicology and molecular dynamics simulation.

Benzo(a)pyrene (BaP) is a common environmental pollutant from combustion sources that promotes oxidative stress, neuroinflammation and disruption of blood-brain barrier (BBB). However, its contribution to worsening traumatic brain injury (TBI) remains unclear. In this study, we aimed to assess the contribution of BaP to secondary injury in TBI. By integrating data from e.g., the Comparative Toxicogenomics Database, GeneCards, and Online Mendelian Inheritance in Man, 121 overlapping core targets were identified between BaP and TBI. Enrichment analyses via Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, combined with protein-protein interaction networks and topological algorithms (degree, closeness centrality, betweenness centrality, average shortest path length, topological coefficient and partner of multi-edged node pairs), highlighted five hub genes (TP53, EGFR, AKT1, ACTB, and TNF) implicated in mitogen-activated protein kinase signaling, oxidative stress, and neuroinflammation. Molecular docking showed strong binding affinities of BaP to these hub proteins, with energies from -9.3 to -12.1 kcal/mol, tighter than co-crystal ligands and existing protein-binding drugs. Molecular dynamics simulations confirmed interaction stability through low root-mean-square deviation (< 0.5 nm), fluctuation, and radius of gyration values. Calculation of binding free energies using MM-PBSA validated the strong binding affinity between BaP and binding pockets of each hub genes. Toxicity prediction analysis revealed an oral LD50 of 316 mg/kg for BaP, with high probabilities for neurotoxicity, BBB permeability, carcinogenicity, and mutagenicity, associated with aryl hydrocarbon receptor activation. These findings reveal a "neurovascular homeostasis disruption" network underlying BaP-exacerbated TBI pathology and highlight potential targets to reduce pollution-related risks in TBI management.

Benzo(a)pyrene↗

PubMed2026

Investigating the mechanisms of PhIP-induced colorectal cancer through network toxicology, machine learning, and molecular dynamics simulation.

BACKGROUND: Over the past few years, 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP)- a compound from grilled or processed meats-has emerged as a major player in cancer development, especially colorectal cancer (CRC). This work dives into its potential links to CRC and uncovers the key genes that bridge this connection. METHODS: We tapped into various databases to pinpoint target genes tied to PhIP and CRC, then ran protein-protein interaction (PPI) analyses for visualization. Next, we explored underlying mechanisms through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. To nail down predictions, we tested 107 machine learning pipelines and picked the best one, validating its accuracy and the core genes' prognostic value across datasets. Next, molecular docking and dynamics simulations probed the interactions between these genes and PhIP. Finally, cell proliferation was assessed using Cell Counting Kit-8 (CCK-8) and 5-ethynyl-2'-deoxyuridine (EdU) assays, and polymerase chain reaction (PCR) was performed to validate the expression levels of the hub genes. RESULTS: Our analysis identified 39 overlapping genes, from which a machine learning model (glmBoost + Enet) identified six candidate targets: CDK4, CEBPB, COMT, SOX9, TIMP1, and TOP2A. To prioritize these, a hierarchical screening framework was applied. Molecular docking and dynamics simulations identified CDK4, COMT, and TIMP1 as the most stable interactors with PhIP. Functional assays confirmed that PhIP treatment significantly enhanced the proliferation of CRC cells. Crucially, quantitative PCR (qPCR) validation in multiple CRC cell lines identified TIMP1 as the primary target, showing the most consistent and significant upregulation upon PhIP exposure. CONCLUSIONS: In essence, these genes drive PhIP is role in CRC, offering novel insights into its molecular pathways. This could reshape how we tackle food-related pollutants, paving the way for better prevention and targeted therapies.

Colorectal cancer (CRC)↗

PubMed2026

Screening of core targets for Di(2-ethylhexyl) Phthalate-related gastric cancer based on machine learning, molecular docking, and SHAP analysis.

PURPOSE: Given the existing uncertainties regarding the link between Di(2-ethylhexyl) phthalate (DEHP) exposure and gastric cancer (GC) progression, this study aimed to clarify their association, identify the toxic targets of DEHP, and elucidate the underlying molecular mechanisms. METHODS: Multiple integrated approaches were employed, including Gene Expression Omnibus (GEO) data analysis, network toxicology, molecular docking, and machine learning. STRING and Cytoscape tools were utilized to identify key targets, while Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the functional enrichment of intersecting targets. Machine learning and SHAP analysis were applied to screen core targets in GC. Molecular docking was performed to evaluate the binding affinity of DEHP toward core targets, and 200 ns molecular dynamics simulations were further conducted for representative complexes to validate their dynamic stability. RESULTS: A total of 18 key targets were identified using STRING and Cytoscape. GO and KEGG enrichment analyses demonstrated that these intersecting targets were primarily enriched in the extracellular region, as well as the Calcium signaling pathway and cAMP signaling pathway. Through machine learning analyses, 7 key genes (ADRB2, ESRRG, GRIA4, IL13RA2, NR3C2, PLA2G1B, and SULT2A1) were identified as core targets in GC through machine learning analyses. Molecular docking simulations revealed strong binding specificity between DEHP and the target proteins. Among them, NR3C2 and ADRB2 exhibited relatively high predictive importance in the machine learning models. DEHP showed favorable binding affinity toward these core targets, and molecular dynamics simulations further confirmed that ADRB2-DEHP and NR3C2-DEHP complexes maintained stable conformations throughout the simulation. CONCLUSIONS: Our findings identified GC associated genes that were computationally predicted as potential targets of DEHP. These results indicated structural compatibility between DEHP and its target proteins but did not prove that DEHP exposure accounts for the gene expression changes in GC.

Molecular Docking Simulation↗

PubMed2026

Exploring the mechanism of Acanthopanax in treating vertigo: A network pharmacology and molecular docking study.

Acanthopanax has therapeutic efficacy against vertigo; however, the underlying mechanism remains unclear. This study aimed to elucidate the mechanism by which Acanthopanax treats vertigo through integrated network pharmacology and molecular docking techniques, and retrieved all target genes of Acanthopanax for vertigo treatment from July to October 2025. Vertigo-related target genes were subsequently identified from public databases, including GeneCards and Online Mendelian Inheritance in Man. The intersection between Acanthopanax-derived targets and vertigo-related targets was analyzed to identify candidate target genes. Using the STRING platform, we constructed protein-protein interaction networks for the identified candidate targets and mined the core functional modules within these networks. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed on candidate targets via the clusterProfiler package. A carp bile poisoning-liver injury target-pathway network was constructed via Cytoscape 3.8.2 software, network topology analysis was conducted, and the core components and targets were screened. The results found that A total of 295 candidate targets for the treatment of vertigo caused by Eleutherococcus senticosus were identified. Pathway enrichment analysis revealed that Eleutherococcus senticosus treatment for vertigo may be closely associated with pathways related to IL-17, TNF, phosphoinositide 3-kinase (PI3K)-Akt, p53, HIF-1, and Forkhead box O signaling. The core targets for the treatment of A. senticosus vertigo include TP53, AKT1, STAT3, TNF, and JUN. Network pharmacology and molecular docking studies suggest that A. senticosus may treat vertigo by regulating targets such as JUN, TNF, AKT1, STAT3, and STAT3 through pathways such as the IL-17, TNF, phosphoinositide 3-kinase-Akt, p53, HIF-1, and Forkhead box O signaling pathways. These mechanisms warrant further investigation in future o and in vitro studies.

Molecular Docking Simulation↗

PubMed2026

Micro- and nanoplastics-induced neurotoxicity: a CNS-centered, evidence-graded adverse outcome pathway framework based on systematic weight-of-evidence assessment.

Micro- and nanoplastics (MPs/NPs) are ubiquitous anthropogenic particulate pollutants posing emerging threats to human neurological health. Severe heterogeneity in particle physicochemical properties, environmental aging status, exposure paradigms and experimental platforms has created persistent mechanistic uncertainties in MP/NP neurotoxicology, hindering reliable hazard characterization and risk translation. Here, we systematically consolidate empirical toxicological evidence and construct a dedicated central nervous system (CNS)-targeted adverse outcome pathway (AOP) network integrated with rigorous weight-of-evidence (WoE) grading to elucidate the hierarchical, particle-specific toxic cascades underlying MP/NP-induced neural injury. Our synthesis overturns the conventional linear toxicity paradigm, demonstrating that MPs/NPs trigger neurotoxicity via a complex multi-input mechanistic network. We definitively establish oxidative stress as a robust early convergent key event-rather than a universal molecular initiating event-orchestrating ROS overproduction, lipid peroxidation, mitochondrial dysfunction, and neuroinflammation to propagate neuronal damage. This core module is driven by five distinct particulate upstream triggers: particle-biomolecule interfacial perturbation, corona-facilitated cellular internalization, plastic-associated chemical leaching, aging-derived free radical reactivity, and gut-borne systemic neurotoxic signaling. Downstream pathogenic outcomes encompass glial overactivation, neurotransmitter dyshomeostasis, autophagy-lysosome dysfunction, metabolic reprogramming, regulated neuronal cell death, and behavioral impairments. Tiered WoE analysis confirms strong validation for early oxidative/inflammatory cascades, moderate support for gut-brain axis crosstalk and intracellular trafficking disruption, and nascent evidence for synaptic dysfunction and neurodegeneration-linked proteostatic defects. Extrapolation to human health risk remains constrained by the frequent use of high-dose exposure paradigms, limited validated data on internal dosimetry in the human brain, discrepancies between effective concentrations in experimental models and environmentally relevant human tissue burdens, and insufficient causal validation of distal adverse outcomes. We highlight key research priorities including aged mixed-particle exposure systems, leachate-controlled assays, quantitative internal dose evaluation, and mechanistic intervention verification. This evidence-stratified AOP framework resolves longstanding mechanistic ambiguities in particulate neurotoxicity, providing a standardized, causality-based foundation for future mechanistic exploration and health risk assessment of global plastic pollution.

Adverse outcome pathway↗

PubMedDate not supplied

A computer-based toxicology search system.

An interactive computer search system, based on the National Institute for Occupational Safety and Health's Registry of Toxic Effects of Chemicals (NIOSH-RTECS) has been developed. This system permits the location and retrieval of specified toxicity data defined by test animal, dosage method, toxicity level, and compound identity. All available toxicity data for a given chemical substance, identified by name or structure, may be retrieved using either the Chemical Abstracts Service (CAS) Registry Number or the RTECS Accession Number for that compound. The search system is running upon an international computer network, and may be used by anyone interested on a fee-for-service basis.

Computers↗

PubMed2026

HLA and non-HLA genetic analyses reveal suggestive variants associated with statin-induced liver injury.

BACKGROUND: Statins are widely prescribed for cardiovascular risk reduction and are generally well tolerated. However, they can cause drug-induced liver injury (DILI), and the genetic factors contributing to statin-DILI remain poorly understood. METHODS: HLA association and genome-wide association (GWAS) studies were conducted to identify genetic variants associated with statin-DILI. High-confidence cases (n=71) were identified from the Drug-Induced Liver Injury Network (DILIN) and compared with statin-exposed controls without liver injury (n=551) from the Indiana Biobank. Association testing was performed across ancestries and within ancestry, adjusting for age, sex, and three principal components of genotypes. Top variants were further evaluated in non-statin DILI cases and unexposed controls. In addition, we investigated the frequency of candidate variants among a comprehensive list of pharmacogenetic variants related to statins. RESULTS: HLA-DQA1*03:01 was significantly associated with increased risk of statin-DILI (OR=3.49, 95% CI 2.21-5.51, p-value=1.27×10-7), with enrichment observed across multiple ancestry groups, particularly non-Hispanic Black and Hispanic individuals. From the GWAS, three loci showed suggestive associations (p-value <5×10-06) with statin-DILI, including rs35197737 in RGS1 (OR=5.03, 95% CI 1.11-3.66, p=1.14×10-7), rs75629598 in FRMD4A (OR=4.4, 95% CI=2.33-8.12, p=3.97×10-6), and rs7658630 in the intergenic region on chromosome 4 (OR=4.86, 95% CI 2.66-8.85, p=2.68×10-7). No pharmacogenetic variants revealed statistical significance. CONCLUSION: We identified HLA and non-HLA genetic variants associated with statin DILI. Future studies with larger sample sizes should confirm these observations.

Humans↗

PubMed1978

Analysis of the registry of toxic effects of chemical substances (RTECS) files and conversion of the data in these files for input to the environmental chemicals data and information network (ECDIN).

A data bank for environmental chemicals, ECDIN, is being developed at the Joint Research Centre of the European Communities in cooperation with universities and research institutes in the nine member states as a part of the Environmental Research Programme of the EC. During the pilot phase of the project, data from the Registry of Toxic Effects of Chemical Substances have been incorporated into the data bank. Conversions of the data into ECDIN input format was necessary before inclusion of the toxicity data in ECDIN, and the computer programs used for this format conversion have produced various statistics for the contents of the RTECS files. Analyses of the data in three editions of RTECS are presented.

Environmental Pollutants↗