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Register a new userEffect of protein binding on exposure of unbound and total mycophenolic acid: a population pharmacokinetic analysis in Chinese adult kidney transplant recipients
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AIMS A population pharmacokinetic (PK) analysis was performed to: (1) characterise the PK of unbound and total mycophenolic acid (MPA) and its 7-O-mycophenolic acid glucuronide (MPAG) metabolite, and (2) identify the clinically significant covariates that cause variability in the dose-exposure relationship to facilitate dose optimisation. METHODS A total of 740 unbound MPA (uMPA), 741 total MPA (tMPA) and 734 total MPAG (tMPAG) concentration-time data from 58 Chinese kidney transplant patients were analysed using a nonlinear mixed-effect model. The influence of covariates was tested using a stepwise procedure. RESULTS The PK of unbound MPA and MPAG were characterised by a two- and one-compartment model with first-order elimination, respectively. Apparent clearance of uMPA (CLuMPA/F) was estimated to be 852 L/h with a relative standard error (RSE) of 7.1%. The tMPA and uMPA were connected using a linear protein binding model, in which the protein binding rate constant (kB) increased non-linearly with the serum albumin (ALB) concentration. The estimated kB was 53.4 /h (RSE, 2.3%) for patients with ALB of 40 g/L. In addition, model-based simulation showed that changes in ALB substantially affected tMPA but not uMPA exposure. CONCLUSIONS The established model adequately described the population PK characteristics of the uMPA, tMPA, and MPAG. The estimated CLuMPA/F and unbound fraction of MPA (FUMPA) in Chinese kidney transplant recipients were comparable to those published previously in Caucasians. We recommend monitoring uMPA instead of tMPA to optimise mycophenolate mofetil (MMF) dosing for patients with lower ALB levels.
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Background:Diverse tacrolimus population pharmacokinetic 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. Methods: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.Results: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 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 compartment models,indicating the underlying nonlinear kinetics of tacrolimus in liver transplant recipients.Conclusions: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.
Multiple myeloma (MM), a plasma cell cancer, is associated with many health challenges, including damage to the kidney by tubulointerstitial fibrosis. We develop a mathematical model which captures the qualitative behavior of the cell and protein populations involved. Specifically, we model the interaction between cells in the proximal tubule of the kidney, free light chains, renal fibroblasts, and myeloma cells. We analyze the model for steady-state solutions to find a mathematically and biologically relevant stable steady-state solution. This foundational model provides a representation of dynamics between key populations in tubulointerstitial fibrosis that demonstrates how these populations interact to affect patient prognosis in patients with MM and renal impairment.
Cancer cells maintain a high level of aerobic glycolysis (the Warburg effect), which is associated with their rapid proliferation. Many studies have reported that the suppression of glycolysis and activation of oxidative phosphorylation can repress the growth of cancer cells through regulation of key regulators. Whether Warburg effect of cancer cells could be switched by some other environmental stimulus? Herein, we report an interesting phenomenon in which cells alternated between glycolysis and mitochondrial respiration depending on the type of radiation they were exposed to. We observed enhanced glycolysis and mitochondrial respiration in HeLa cells exposed to 2-Gy X-ray and 2-Gy carbon ion radiation, respectively. This discovery may provide novel insights for tumor therapy.
Background Little is known about the population pharmacokinetics (PPK) of tacrolimus (TAC) in pediatric primary nephrotic syndrome (PNS). This study aimed to compare the predictive performance between nonlinear and linear PK models and investigate the significant factors of TAC PK characteristics in pediatric PNS. Methods Data were obtained from 71 pediatric patients with PNS, along with 525 TAC trough concentrations at steady state. The demographic, medical, and treatment details were collected. Genetic polymorphisms were analyzed. The PPK models were developed using nonlinear mixed effects model software. Two modeling strategies, linear compartmental and nonlinear Michaelis Menten (MM) models, were evaluated and compared. Results Body weight, age, daily dose of TAC, co-therapy drugs (including azole antifungal agents and diltiazem), and CYP3A5*3 genotype were important factors in the final linear model (onecompartment model), whereas only body weight, codrugs, and CYP3A5*3 genotype were the important factors in the nonlinear MM model. Apparent clearance and volume of distribution in the final linear model were 7.13 L/h and 142 L, respectively. The maximal dose rate (Vmax) of the nonlinear MM model was 1.92 mg/day and the average concentration at steady state at half-Vmax (Km) was 1.98 ng/mL. The nonlinear model described the data better than the linear model. Dosing regimens were proposed based on the nonlinear PK model.Conclusion Our findings demonstrate that the nonlinear MM model showed better predictive performance than the linear compartmental model, providing reliable support for optimizing TAC dosing and adjustment in children with PNS.
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