The Herb-Drug Interaction of Clopidogrel and Xuesaitong Dispersible Tablet by Modulation of the Pharmacodynamics and Liver Carboxylesterase 1A Metabolism

Objective. Clopidogrel and Xuesaitong dispersible tablet (XST) have been clinically proven to be effective for treating cardiocerebrovascular disease. The present study was to investigate the herb-drug interaction of Clopidogrel and XST by modulation of the pharmacodynamics and liver Carboxylesterase 1A(CES1A) metabolism. Methods. 30 male SD rats were randomly divided into a control group (equal volumes of saline, 6 rats for mRNA analysis), a clopidogrel group (clopidogrel with dose 30 mg/kg), and a combination group (clopidogrel and XST, with dose 30 and 50 mg/kg respectively, each group continuous administration once daily for 30 days). The clopidogrel and combination group comprised 12 rats, with 6 designated for mRNA analysis and 6 for the pharmacokinetic study. The 2-bromo-3’-methoxyacetophenone- (MPB-) derivatized clopidogrel active thiol metabolite (CAMD) was measured by UHPLC-MS/MS for pharmacokinetics (n=6). The expression of CES1A mRNA was examined with real-time RT-PCR (n=6). Molecular simulation was used to investigate the inhibition effect of XST on the CES1A protein. The CAMD pharmacodynamics and CES1A metabolism were investigated to evaluated the herb-drug interaction. Results. Clopidogrel and XST coadministration appreciably increased the Cmax, AUC, and MRT of CAMD. However, the expression of CES1A mRNA was decreased accordingly. It also indicated that the bioactive components in XST had good interaction with the CES1A metabolism target by molecular simulation. The animal study indicated that clopidogrel and XST coadministration produced significant herb-drug interactions at active CAMD pharmacokinetic and CES1A metabolic enzyme aspect. Conclusion. 30-days dose of coadministration altered hepatic CES1A protein and resulted in reduced plasma levels of active CAMD. both the decreased CES1A mRNA expression and the inhibition on the protein were due to the combination of XST, which accordingly upregulated the pharmacokinetics of plasma active CAMD

Are medical record front page data suitable for risk adjustment in hospital performance measurement? Development and validation of a risk model of in-hospital mortality after acute myocardial infarction

Objectives To develop a model of in-hospital mortality using medical record front page (MRFP) data and assess its validity in case-mix standardisation by comparison with a model developed using the complete medical record data.Design A nationally representative retrospective study.Setting Representative hospitals in China, covering 161 hospitals in modelling cohort and 156 hospitals in validation cohort.Participants Representative patients admitted for acute myocardial infarction. 8370 patients in modelling cohort and 9704 patients in validation cohort.Primary outcome measures In-hospital mortality, which was defined explicitly as death that occurred during hospitalisation, and the hospital-level risk standardised mortality rate (RSMR).Results A total of 14 variables were included in the model predicting in-hospital mortality based on MRFP data, with the area under receiver operating characteristic curve of 0.78 among modelling cohort and 0.79 among validation cohort. The median of absolute difference between the hospital RSMR predicted by hierarchical generalised linear models established based on MRFP data and complete medical record data, which was built as ‘reference model’, was 0.08% (10th and 90th percentiles: −1.8% and 1.6%). In the regression model comparing the RSMR between two models, the slope and intercept of the regression equation is 0.90 and 0.007 in modelling cohort, while 0.85 and 0.010 in validation cohort, which indicated that the evaluation capability from two models were very similar.Conclusions The models based on MRFP data showed good discrimination and calibration capability, as well as similar risk prediction effect in comparison with the model based on complete medical record data, which proved that MRFP data could be suitable for risk adjustment in hospital performance measurement