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[Display omitted] • The influence of CYP3A polymorphisms on tacrolimus concentrations was investigated. • CYP3A polymorphisms of recipients affected tacrolimus concentrations. • CYP3A polymorphisms of donors did not influence tacrolimus concentrations. The purpose of this study was to investigate the effect of CYP3A7 , CYP3A4 , and CYP3A5 genetic polymorphisms in liver transplant recipients and donors on tacrolimus concentrations in the early stages after liver transplantation. One hundred and thirty-eight liver transplant recipients and matched donors were genotyped for CYP3A7 (rs10211 and rs2257401), CYP3A4 (rs4646437 and rs2242480), and CYP3A5 *3 (rs776746) polymorphisms. The relationships between dose-adjusted trough concentrations (C 0 /D) of tacrolimus and corresponding genotypes were investigated. Recipient CYP3A polymorphisms were associated with tacrolimus concentrations. The CYP3A7 rs10211 AA carriers (186.2 vs 90.5, p < 0.001), CYP3A4 rs4646437 CC carriers (184.0 vs 88.8, p < 0.001), CYP3A4 *1G rs2242480 CC carriers (189.8 vs 99.7, p < 0.001), and CYP3A5 *3 rs776746 GG carriers (197.3 vs 86.0, p < 0.001) had an almost twofold increase in the tacrolimus C 0 /D compared to that of the non-carriers. We further investigated the effect of the combination of recipient (intestinal) and donor (hepatic) genotypes on tacrolimus concentrations. Regardless of the genotype of the matched donor, CYP3A7 rs10211, CYP3A4*1G (rs2242480), and CYP3A5 *3 (rs776746) polymorphisms of recipients could affect tacrolimus concentrations. For the CYP3A4 rs4646437 polymorphisms, when the donor carried CYP3A4 rs4646437 CC, the recipient CYP3A4 rs4646437 polymorphism was associated with the C 0 /D of tacrolimus, and when the donor carried CYP3A4 rs4646437 CT/TT genotype, the recipient CYP3A4 rs4646437 polymorphism also affected on tacrolimus C 0 /D, although the effect was not significant. The large inter-individual variation in tacrolimus concentrations in the early stages after liver transplantation is influenced by genetic polymorphisms of CYP3A7 , CYP3A4 , and CYP3A5. Recipient (intestinal) CYP3A7 , CYP3A4 , and CYP3A5 polymorphisms seem to contribute more to such variation than donors. Therefore, the detection of CYP3A polymorphisms in recipients could help to predict the tacrolimus starting dose in the early stages after liver transplantation. [ABSTRACT FROM AUTHOR]

• Predictive performance of nonlinear model was superior to linear models. • Nonlinear kinetics of tacrolimus may be attributed to the properties of the drug itself. • Published models performed inadequately in prediction- and simulation-based diagnostics. • Bayesian forecasting could improve the predictive performance of the models. Diverse tacrolimus population pharmacokinetic (popPK) models in adult liver transplant recipients have been established to describe the PK characteristics of tacrolimus in the last two decades. However, their extrapolated predictive performance remains unclear. Therefore, in this study, we aimed to evaluate their external predictability and identify their potential influencing factors. The external predictability of each selected popPK model was evaluated using an independent dataset of 84 patients with 572 trough concentrations prospectively collected from Huashan Hospital. Prediction- and simulation-based diagnostics and Bayesian forecasting were conducted to evaluate model predictability. Furthermore, the effect of model structure on the predictive performance was investigated. Sixteen published popPK models were assessed. In prediction-based diagnostics, the prediction error within ± 30% was below 50% in all the published models. The simulation-based normalised prediction distribution error test and prediction- and variability-corrected visual predictive check indicated large discrepancies between the observations and simulations in most of the models. Bayesian forecasting showed improvement in model predictability with two to three prior observations. Additionally, the predictive performance of the nonlinear Michaelis–Menten model was superior to that of linear one- and two-compartment models with first-order elimination, indicating the underlying nonlinear kinetics of tacrolimus in liver transplant recipients, which was consistent with the findings in adult kidney transplant recipients. The published models performed inadequately in prediction- and simulation-based diagnostics. Bayesian forecasting may improve the predictive performance of the models. Furthermore, nonlinear kinetics of tacrolimus may be mainly caused by the properties of the drug itself, and incorporating nonlinear kinetics may be considered to improve model predictability. Image, graphical abstract [ABSTRACT FROM AUTHOR]