RESUMO
Taletrectinib is a potent selective ROS and pan-NTRK tyrosine kinase inhibitor (TKI) and has been developed to treat non-small cell lung cancer (NSCLC). To facilitate pharmacokinetic and toxicokinetic studies of taletrectinib, we developed a procedure for ultra-high-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) to detect the plasma level of taletrectinib in dogs. This assay procedure was validated in compliance with FDA guidance. The dog plasma samples were spiked with internal standard (IS), followed by protein precipitation, and analyzed using a Waters ACQUITY BEH C18 column coupled to a Thermo triple quadrupole mass spectrometer. Separation was executed using the acetonitrile-0.1 % formic acid solution with gradient elution, at a flow rate of 0.4 mL/min. Taletrectinib and IS were monitored by multiple reaction monitoring (MRM) with m/z 406.2 > 349.2 and m/z 441.2 > 138.1, respectively. The procedure demonstrated excellent linearity with a correlation coefficient greater than 0.999 within the concentration range of 0.2-200 ng/mL. The inter- and intra-day accuracy ranged from -5.25 % to 5.26 %, and the precision was below 6.39 %. Acetonitrile-mediated protein precipitation showed high extraction efficiency and a recovery above 85 %. The procedure was then applied to quantify taletrectinib in beagle dog plasma after oral and intravenous doses and achieved success. The obtained pharmacokinetic parameters indicated high bioavailability of taletrectinib (>85 %) and extensive tissue distribution (>40 L/kg).
RESUMO
Objective: The purpose of this study was to establish an N6-methylandenosine (m6A)-related long non-coding RNA (lncRNA) signature to predict the prognosis of hepatocellular carcinoma (HCC). Methods: Pearson correlation analysis was used to identify m6A-related lncRNAs. We then performed univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression analysis to construct an m6A-related lncRNA signature. Based on the cutoff value of the risk score determined by the X-title software, we divided the HCC patients into high -and low-risk groups. A time-dependent ROC curve was used to evaluate the predictive value of the model. Finally, we constructed a nomogram based on the m6A-related lncRNA signature. Results: ZEB1-AS1, MIR210HG, BACE1-AS, and SNHG3 were identified to comprise an m6A-related lncRNA signature. These four lncRNAs were upregulated in HCC tissues compared to normal tissues. The prognosis of patients with HCC in the low-risk group was significantly longer than that in the high-risk group. The M6A-related lncRNA signature was significantly associated with clinicopathological features and was established as a risk factor for the prognosis of patients with HCC. The nomogram based on the m6A-related lncRNA signature had a good distinguishing ability and consistency. Conclusion: We identified an m6A-related lncRNA signature and constructed a nomogram model to evaluate the prognosis of patients with HCC.