Investigating the Mechanisms of Pazopanib-induced Hepatotoxicity: Insights from Network Toxicology, Microarray Analysis, and Machine Learning

Background: Pazopanib is an oral multi-kinase inhibitor that is effective in treating various tumors. However, it is commonly associated with hepatotoxicity, which can interrupt treatments and cause delays, thereby increasing the risk of tumor progression. The mechanisms underlying pazopanib-induced hepatotoxicity remain unclear and limit the development of effective preventive strategies. This study aimed to identify the core gene products and investigate the mechanisms associated with pazopanib-induced hepatotoxicity by integrating network toxicology, microarray analysis, and machinelearning. Methods: Potential pazopanib targets were identified from multiple databases. Differential expression analysis was conducted using microarray data from hepatocyte-like cells treated with pazopanib and from matched control samples. Genes that overlapped between pazopanib targets and differentially expressed genes (DEGs) were considered potential pathogenic targets for hepatotoxicity. Multiple machine learning algorithms were employed for gene selection to improve accuracy and predictive capability. Molecular docking was used to evaluate the binding affinity of pazopanib to core proteins. Functional enrichment analysis was conducted to elucidate the potential toxic mechanisms. Results: Our analysis identified 162 target genes for pazopanib and 291 DEGs, revealing seven shared genes as potential pathogenic targets for pazopanib-induced hepatotoxicity. Using machine learning, we further detected four core target proteins: CYP1A1, DDR2, FGF1, and PLK4. Molecular docking confirmed that pazopanib stably bound to these core proteins. Functional enrichment analysis indicated that the hepatotoxicity associated with pazopanib may involve p53 signaling, impaired cell cycle, and immune modulation. Conclusion: This study enhances our understanding of the molecular mechanisms underlying pazopanib-induced hepatotoxicity, which is essential for developing protective strategies and therapeutic interventions. By integrating network toxicology, microarray analysis, and machine learning, this study provides a comprehensive framework for investigating the complex toxicological processes of specific compounds and offers insights that could improve the clinical applications and regulatory safety of targeted therapies.