Aim: To establish a population pharmacokinetic model and optimise tacrolimus dosing regimens in Chinese Han lung transplant recipients. Methods: Tacrolimus trough concentrations and clinical data of 70 adult lung transplant recipients were collected. Population pharmacokinetic analysis was performed using a nonlinear mixed effects model. A Monte Carlo simulation was conducted to determine the optimal dosing regimen. Results: The pharmacokinetics of tacrolimus could be best described by a one-compartment model, with the CYP3A5 genotype, haematocrit (HCT), and alanine transaminase (ALT) as significant covariates. The clearance of tacrolimus in the CYP3A5 rapid and intermediate metabolisers were 3.03 and 1.99 times higher than those of CYP3A5 poor metaboliser, respectively. When HCT decreased from 0.30 to 0.20, the clearance of tacrolimus increased by 31.14%, and the apparent volume of distribution increased by 28.58%. The clearance of tacrolimus decreased by 8.67% when ALT increased from 20 IU·L-1 to 40 IU·L-1. Monte Carlo simulation indicated that recipients with CYP3A5*1/*1 receiving 3.5 mg twice daily, recipients with HCT < 0.2 receiving 5 mg twice daily, and recipients with ALT < 4IU·L-1 received 3 mg twice daily, could achieve the target concentrations of 10–15 ng·mL-1. Conclusions: A population pharmacokinetic model of tacrolimus in Chinese Han lung transplant recipients was successfully constructed. Recipients with the CYP3A5*1/*1 genotype, low HCT value, and low ALT value after surgery needed a higher maintenance dose to reach the therapeutic window, which provided a reference for the formulation of individualised tacrolimus regimen.

Abstract Background: We aimed to use preoperative clinical data from paediatric patients with simple congenital heart disease to predict the risk of prolonged mechanical ventilation after surgery. Methods: The clinical data from paediatric patients with simple congenital heart disease who underwent anatomical correction under cardiopulmonary bypass in a single centre during a continuous period were retrospectively collected. Univariate and multivariate logistic regression analyses were performed to identify the risk factors for prolonged mechanical ventilation (>24 h) after surgery, and a mathematical model was established. Then, using data from another centre, we adopted an ROC curve to verify the scalability of the model. Results: A total of 585 paediatric patients were eligible for inclusion in this study. Multivariate logistic regression analysis showed that weight (kg), the size of the ventricular septal defect, the size of the atrial septal defect and the shunt direction of the defect site were significantly correlated with prolonged mechanical ventilation (>24 h) after surgery. The risk prediction model was established and the area under the curve of the model was 0.853 (ROC curve). A set of data from another heart centre, with equivalent inclusion criteria, was used to validate the scalability of the model, and the area under the curve of the accepted validated data was 0.841 (ROC curve). Conclusions: The risk of prolonged mechanical ventilation (>24 h) after surgery in paediatric patients with simple congenital heart disease with anatomical correction assisted by cardiopulmonary bypass can be well predicted by using preoperative clinical data.