Neratinib is a tyrosine kinase inhibitor that has been approved by the US Food and Drug Administration for the treatment of breast cancer. However, its metabolism remains unknown. This study was carried out to investigate the in vitro and in vivo metabolism of neratinib using an UHPLC-DAD-Q Exactive Orbitrap-MS instrument with dd-MS on-line data acquisition mode. The post-acquisition data was processed using MetWorks software. Under the current conditions, a total of 12 metabolites were detected and structurally identified based on their accurate masses, fragment ions and chromatographic retention times. Among these metabolites, M3, M10 and M12 were unambiguously identified using chemically synthesized reference standards. M6 and M7 (GSH conjugates) were the major metabolites. The metabolic pathways of neratinib were proposed accordingly. Our findings suggested that neratinib was mainly metabolized via O-dealkylation (M3), oxygenation (M8), N-demethylation (M10), N-oxygenation (M12), GSH conjugation (M1, M2, M4, M5, M6 and M7) and N-acetylcysteine conjugation (M9 and M11). The α,β-unsaturated ketone was the major metabolic site and GSH conjugation was the predominant metabolic pathway. In conclusion, this study provided valuable metabolic data and would benefit the assessment of the contributions to the overall activity or toxicity from the key metabolites.
After paraquat (PQ) poisoning, it is difficult to accurately diagnose patients' condition by only measuring their blood PQ concentration. Therefore, it is important to establish an accurate method to assist in the diagnosis of PQ poisoning, especially in the early stages. In this study, a gas chromatography−mass spectrometry (GC−MS) metabonomics strategy was established to obtain metabolite information. A random forest algorithm was used to search for potential biomarkers of PQ poisoning, and data mining and network pharmacological analysis were used to evaluate the active components, drug−disease targets, and key pathways of Xuebijing (XBJ) injection in the treatment of PQ-induced pulmonary fibrosis. Targets from the network pharmacology analysis and metabolites from plasma metabolomics were jointly analyzed to select crucial metabolic pathways. Finally, molecular docking technology and in vitro experiments were used to verify the pathway targets to further reveal the potential mechanisms underlying the antipulmonary fibrosis effect of XBJ. Metabonomics studies showed that L-valine, glycine, citric acid, D-mannose, D-galactose, maltose, L-tryptophan, and arachidonic acid contributed more to the differentiation of different groups than other metabolites. Compared with the control group, the PQ poisoning group had higher levels of L-valine, glycine, citric acid, L-tryptophan, and arachidonic acid, and lower levels of D-mannose, D-galactose, and maltose. After treatment with XBJ injection, the relative levels of these metabolites were reversed. The network pharmacological analysis screened a total of 180 targets, mainly involving multiple signaling pathways and metabolic pathways, which jointly played an antipulmonary fibrosis effect. Based on the combined analysis of 180 targets and 8 different metabolites, arachidonic acid metabolism was selected as the key metabolic pathway. Molecular docking analysis showed that the XBJ compound had strong binding activity with the target protein. Western blot results showed that XBJ injection could reduce the inflammatory response by downregulating the expressions of p-p65, p-IKBα, and p-IKKβ, thus inhibiting the development of PQ-induced pulmonary fibrosis. In summary, the combined results from metabolomics and network pharmacology studies showed that Xuebijing has the characteristics of multitarget, multichannel, and multicomponent action in the treatment of pulmonary fibrosis caused by PQ.
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