Integrating pharmacokinetics, tissue distribution, RNA sequencing, and network pharmacology identifies STAT1 and SERPINE1 as potential targets of moscatilin in treating liver diseases

This study established a quantitative analysis method for moscatilin using ultrahigh-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) to investigate its pharmacokinetics and tissue distribution in Kunming mice. Additionally, RNA sequencing (RNA-seq) and network pharmacology were employed to elucidate the molecular mechanisms underlying moscatilin's therapeutic potential in liver diseases, including non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), viral hepatitis, liver injury, liver fibrosis, liver cirrhosis, and liver cancer. The analytical method was validated for selectivity, carryover, linearity, precision, accuracy, dilution integrity, matrix effect, recovery, and stability following US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) bioanalytical guidelines. Pharmacokinetic analysis revealed rapid absorption of moscatilin in vivo, with a bioavailability of 85.82%. Tissue distribution studies indicated widespread presence across various organs, with elevated concentrations in the lungs and liver. RNA-seq and network pharmacology analysis identified signal transducer and activator of transcription 1 (STAT1) and serine protease inhibitor clade E member 1 (SERPINE1) as potential targets for moscatilin in liver disease treatment. Overall design: Total RNA extraction, cDNA library construction, and mRNA transcriptome sequencing were conducted by Guangzhou IGE Biotechnology Co., Ltd. High-throughput paired-end sequencing was executed on the Illumina NovaseqTM 600 platform. The raw sequencing data underwent filtering to remove adapter sequences, low-quality reads, and reads containing 'N'. Clean reads were then aligned to the reference genome using HISAT2 software. Differential gene expression among samples was analyzed using DESeq2 software, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene Ontology (GO) function annotation for the differentially expressed genes.