MétaCan
Menu
Back to cohort
Record W4406049337 · doi:10.1503/jpn.240065

Network pharmacology and molecular docking to explore mechanisms of clozapine-induced cardiac arrest

2025· article· en· W4406049337 on OpenAlexvenueno aff
Ximing Chen, Chuanjun Zhuo, Lei Yang, Qiuyu Zhang, Chao Li

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBioinformatics and Genomic Networks
Canadian institutionsnot available
FundersTianjin Medical UniversityNankai University
KeywordsClozapineKEGGPharmacologyCREBBiologyTranscription factorMedicineGeneBiochemistryGene expressionTranscriptomeSchizophrenia (object-oriented programming)Psychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Clozapine is superior to all other antipsychotics in treating schizophrenia in terms of its curative efficacy; however, this drug is prescribed only as a last resort in the treatment of schizophrenia, given its potential to induce cardiac arrest. The mechanism of clozapine-induced cardiac arrest remains unclear, so we aimed to elucidate the potential mechanisms of clozapine-induced cardiac arrest using network pharmacology and molecular docking. METHODS: We identified and analyzed the overlap between potential cardiac arrest-related target genes and clozapine target genes. We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. We then constructed a protein-protein interaction (PPI) network and screened the core targets. We used molecular docking to evaluate the binding energy between clozapine and core targets. RESULTS: We identified a total of 2405 target genes related to cardiac arrest and 107 target genes for clozapine. Among these, we found 41 overlapping target genes. The main enriched GO biological processes included the upregulation of the mitogen-activated protein kinase (MAPK) cascade and the adenylate cyclase-activating adrenergic receptor signalling pathway. The KEGG enrichment analysis showed that the neuroactive ligand-receptor interaction and the forkhead box O (FoxO) signalling pathway seemed to be the key signalling pathways involved in clozapine-induced cardiac arrest. The 7 core targets identified in the established PPI network were G-protein-coupled receptor kinase 2, 5-hydroxytryptamine 2A receptor, dopamine D2 receptor, glycogen synthase kinase 3β, cyclin-dependent kinase 2, CREB-binding protein, and signal transducer and activator of transcription 3. The molecular docking results indicated a high affinity between clozapine and all of these core targets. LIMITATIONS: The relatively small scope of the predictive and modelling methods, which predominantly comprised network pharmacology and molecular docking strategies, is a limitation of this study. CONCLUSION: Network pharmacology and molecular docking approaches unveiled target genes for clozapine and potential mechanisms by which it may cause cardiac arrest, including the MAPK cascade, neuroactive ligand-receptor interactions, and the FoxO signalling pathway.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.280
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Psychiatry and NeuroscienceSame topicBioinformatics and Genomic NetworksFrench-language works237,207