Study on the hepatotoxicity and potential mechanism of gefitinib based on CYP450 in mice and AML12 cells

Glycyrrhizic acid alleviates gefitinib-induced liver injury by regulating the p53/p21 pathway and releasing cell cycle arrest

Gefitinib, a first-line tyrosine kinase inhibitor (TKI) target to non-small cell lung cancer (NSCLC) treatment, is known to cause hepatotoxicity, which seriously limiting its therapeutic application. This study investigated the underlying mechanisms of gefitinib-induced liver injury and the protective effects of glycyrrhizic acid (GL) in mice and AML12 cells. Sixty mice were randomly divided into six groups: control, gefitinib, glutathione (200 mg/kg), and three doses of GL (50, 100, and 200 mg/kg). Liver injury was induced in mice through daily oral administration of gefitinib (400 mg/kg) for 16 days, with hepatotoxicity was assessed through serum alanine transaminase (ALT) and aspartate transaminase (AST) levels, and hepatic histopathology. Hepatic mRNA profiles were analyzed using RNA sequencing, with differentially expressed genes (DEGs) confirmed by reverse transcription quantitative polymerase chain reaction (RT-qPCR) and Western blot. Finally, the effect of GL on the anticancer efficacy of gefitinib was assessed in A549 and Lewis lung carcinoma (LLC) lung cancer cells, as well as in a urethane-induced lung cancer mouse model. GL treatment significantly reduced liver index and serum ALT and AST levels, while also improving hepatic histopathology. Transcriptomic analysis identified 114 DEGs linked to the p53 pathway and cell cycle regulation. Further study indicated that GL inhibited the expression of p53 and p21, thereby upregulated Cyclin D1 expression, thereby alleviating gefitinib-induced cell cycle arrest without impairing its anticancer activity in vivo and in vitro. These findings highlight the potential of GL as a safe adjunct therapy, effectively mitigating gefitinib-induced hepatotoxicity while preserving its anticancer efficacy.

Hepatotoxicity of epidermal growth factor receptor - tyrosine kinase inhibitors (EGFR-TKIs)

Drug-induced liver injury (DILI) is one of the most frequently adverse reactions in clinical drug use, usually caused by drugs or herbal compounds. Compared with other populations, cancer patients are more prone to abnormal liver function due to primary or secondary liver malignant tumor, radiation-induced liver injury and other reasons, making potential adverse reactions from liver damage caused by anticancer drugs of particular concernduring clinical treatment process. In recent years, the application of epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) has changed the treatment status of a series of solid malignant tumors. Unfortunately, the increasing incidence of hepatotoxicitylimits the clinical application of EGFR-TKIs. The mechanisms of liver injury caused by EGFR-TKIs were complex. Despite more than a decade of research, other than direct damage to hepatocytes caused by inhibition of cellular DNA synthesis and resulting in hepatocyte necrosis, the rest of the specific mechanisms remain unclear, and few effective solutions are available. This review focuses on the clinical feature, incidence rates and the recent advances on the discovery of mechanism of hepatotoxicity in EGFR-TKIs, as well as rechallenge and therapeutic strategies underlying hepatotoxicity of EGFR-TKIs.

Windows Scanning Multiomics: Integrated Metabolomics and Proteomics

Metabolomics and proteomics offer significant advantages in understanding biological mechanisms at two hierarchical levels. However, conventional single omics analysis faces challenges due to the high demand for specimens and the complexity of intrinsic associations. To obtain comprehensive and accurate system biological information, we developed a multiomics analytical method called Windows Scanning Multiomics (WSM). In this method, we performed simultaneous extraction of metabolites and proteins from the same sample, resulting in a 10% increase in the coverage of the identified biomolecules. Both metabolomics and proteomics analyses were conducted by using ultrahigh-performance liquid chromatography mass spectrometry (UPLC-MS), eliminating the need for instrument conversions. Additionally, we designed an R-based program (WSM.R) to integrate mathematical and biological correlations between metabolites and proteins into a correlation network. The network created from simultaneously extracted biomolecules was more focused and comprehensive compared to those from separate extractions. Notably, we excluded six pairs of false-positive relationships between metabolites and proteins in the network established using simultaneously extracted biomolecules. In conclusion, this study introduces a novel approach for multiomics analysis and data processing that greatly aids in bioinformation mining from multiomics results. This method is poised to play an indispensable role in systems biology research.