Individual dose recommendations for drug interaction between tacrolimus and voriconazole in adult liver transplant recipients: A semiphysiologically based population pharmacokinetic modeling approach.

The magnitude of drug-drug interaction between tacrolimus and voriconazole is highly variable, and individually tailoring the tacrolimus dose when concomitantly administered with voriconazole remains difficult. This study aimed to develop a semiphysiologically based population pharmacokinetic (semi-PBPK) model and a web-based dashboard to identify the dynamic inhibition of tacrolimus metabolism caused by voriconazole and provide individual tacrolimus regimens for Chinese adult liver transplant recipients. A total of 264 tacrolimus concentrations and 146 voriconazole concentrations were prospectively collected from 32 transplant recipients. A semi-PBPK model with physiological compartments including the gut wall, portal vein, and liver was developed using the nonlinear mixed-effects modeling software NONMEM (version 7.4). A web-based dashboard was established in R software (version 3.6.1) to recommend the individual tacrolimus regimens when concomitantly administered with voriconazole. The reversible inhibition of tacrolimus metabolism caused by voriconazole was investigated in both the liver and the gut wall. Moreover, voriconazole could highly inhibit the CYP3A activity in the gut wall more than in the liver. BMI and postoperative days were identified as significant covariates on intrinsic intestinal and hepatic clearance of tacrolimus, respectively. Age and postoperative days were identified as significant covariates on the volume of distribution of voriconazole. The individual tacrolimus regimens when concomitantly administered with voriconazole could be recommended in the dashboard (https://tac-vor-ddi.shinyapps.io/shinyapp3/). In conclusion, the semi-PBPK model successfully described the dynamic inhibition process between tacrolimus and voriconazole, and the web-based dashboard could provide individual tacrolimus regimens when concomitantly administered with voriconazole.

Population Pharmacokinetic Modeling Combined With Machine Learning Approach Improved Tacrolimus Trough Concentration Prediction in Chinese Adult Liver Transplant Recipients.

This study aimed to develop and evaluate a population pharmacokinetic (PPK) combined machine learning approach to predict tacrolimus trough concentrations for Chinese adult liver transplant recipients in the early posttransplant period. Tacrolimus trough concentrations were retrospectively collected from routine monitoring records of liver transplant recipients and divided into the training data set (1287 concentrations in 145 recipients) and the test data set (296 concentrations in 36 recipients). A PPK model was first established using NONMEM. Then a machine learning model of Xgboost was adapted to fit the estimated individual pharmacokinetic parameters obtained from the PPK model with Bayesian forecasting. The performance of the final PPK model and Xgboost model was compared in the test data set. In the final PPK model, tacrolimus daily dose, postoperative days, hematocrit, aspartate aminotransferase, and concomitant voriconazole, were identified to significantly influence the clearance. The postoperative days along with hematocrit significantly influence the volume of distribution. In the Xgboost model, the first 5 predictors for predicting the clearance were concomitant with voriconazole, sex, single nucleotide polymorphisms of CYP3A4*1G and CYP3A5*3 in recipients, and tacrolimus daily dose, for the volume of distribution were postoperative days, age, weight, total bilirubin and graft : recipient weight ratio. In the test data set, the Xgboost model showed the minimum median prediction error of tacrolimus concentrations, less than the PPK model with or without Bayesian forecasting. In conclusion, a PPK combined machine learning approach could improve the prediction of tacrolimus concentrations for Chinese adult liver transplant recipients in the early posttransplant period.

Effect of albumin and CYP2B6 polymorphisms on exposure of efavirenz: A population pharmacokinetic analysis in Chinese HIV-infected adults.

BACKGROUND: Efavirenz is a vital component used to treat HIV-1 infection. Nevertheless, it shows large between-subject variability, which affects both its therapeutic response and adverse effects. OBJECTIVE: To investigate the impact of gene polymorphisms and non-genetic factors on the variability of efavirenz pharmacokinetics and to propose the optimal dose regimens. METHODS: A total of 769 plasma samples from 376 HIV-infected Han Chinese outpatients were collected to develop a population pharmacokinetic model using NONMEM software. The impact of patient demographics, laboratory tests, concomitant medication, and genetic polymorphisms of CYP2B6 and ABCB1 on efavirenz pharmacokinetics were explored. According to the final model, the model-informed dose optimization was conducted. RESULTS: The pharmacokinetics of efavirenz was characterized by a one-compartment model with first-order absorption and elimination. The typical values of the estimated apparent oral clearance, volume of distribution, and absorption rate constant in the final model were 9.44 L/h, 200 L, and 0.727 h - 1, respectively. Efavirenz clearance was significantly influenced by CYP2B6 variants, including rs2099361, rs3745274, and rs2279343, along with albumin and weight. The volume of distribution was affected by albumin and weight. Based on the CYP2B6 polymorphisms of patients, the recommended daily doses of efavirenz were 100 mg for CYP2B6 slow metabolizers, 400 or 600 mg for intermediate metabolizers, and 800 or 1000 mg for extensive metabolizers. CONCLUSIONS: Polymorphisms of CYP2B6, along with albumin and weight, resulted as the predictors of efavirenz pharmacokinetic variability, which could be used in prescribing optimal efavirenz doses.

External Evaluation of Vancomycin Population Pharmacokinetic Models at Two Clinical Centers.

Background: Numerous vancomycin population pharmacokinetic models in neonates have been published; however, their predictive performances remain unknown. This study aims to evaluate their external predictability and explore the factors that might affect model performance. Methods: Published population pharmacokinetic models in neonates were identified from the literature and evaluated using datasets from two clinical centers, including 171 neonates with a total of 319 measurements of vancomycin levels. Predictive performance was assessed by prediction- and simulation-based diagnostics and Bayesian forecasting. Furthermore, the effect of model structure and a number of identified covariates was also investigated. Results: Eighteen published pharmacokinetic models of vancomycin were identified after a systematic literature search. Using prediction-based diagnostics, no model had a median prediction error of ≤ ± 15%, a median absolute prediction error of ≤30%, and a percentage of prediction error that fell within ±30% of >50%. A simulation-based visual predictive check of most models showed there were large deviations between observations and simulations. After Bayesian forecasting with one or two prior observations, the predicted performance improved significantly. Weight, age, and serum creatinine were identified as the most important covariates. Moreover, employing a maturation model based on weight and age as well as nonlinear model to incorporate serum creatinine level significantly improved predictive performance. Conclusion: The predictability of the pharmacokinetic models for vancomycin is closely related to the approach used for modeling covariates. Bayesian forecasting can significantly improve the predictive performance of models.

Population pharmacokinetics and dosing regimen optimisation of lopinavir in Chinese adults infected with HIV.

Lopinavir (LPV) is a protease inhibitor (PI) for the treatment of human immunodeficiency virus (HIV) infections. Current studies on LPV are mainly focused on Caucasians, and none have investigated the population pharmacokinetics (PPK) of LPV in Chinese population. The present study aimed to develop a PPK model for oral LPV in Chinese adults who are HIV-infected. A total of 460 LPV concentrations from 174 Chinese patients who received LPV/ritonavir (LPV/r) 400/100 mg orally every 12 hours (q12h) were analysed using the non-linear mixed-effects modelling approach. Simulations of the LPV concentration profile were performed with different dosing regimens. A one-compartment model with first-order absorption and elimination process described the data. The estimated apparent clearance (CL/F) and volume of distribution (V/F) (% relative standard error [RSE]) for oral LPV were 5.9 L/h (3%) and 117 L (8%), respectively. Body-weight was identified as a covariate on CL/F. In patients who weighed between 45 and 115 kg and received the standard 400/100 mg q12h regimen, the probability of achieving target trough concentration (Ctrough ) of 1 mg/L was >98% for PI-naïve patients and the probability of achieving target Ctrough of 4 mg/L was <80% for PI-pretreated patients. This is the first population pharmacokinetic study to characterise the PK of LPV in Chinese patients with HIV infection. There were no obvious ethnic differences in the PK of LPV between the Chinese population and Caucasian population. The simulations demonstrated that the standard dosing regimen of 400/100 mg q12h (LPV/r tablets) appears to be sufficient for PI-naïve patients but suboptimal for PI-pretreated patients. Therefore, the regimen of 800/200 mg q12h was recommended for PI-pretreated patients. Further investigation of dosage recommendation could be helpful in optimising LPV therapy for HIV infections.