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Register a new userPopulation Pharmacokinetic Study of Tacrolimus in Pediatric Patients with Primary Nephrotic Syndrome: A Comparison of Linear and Nonlinear Michaelis Menten Pharmacokinetic Model
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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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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.
Population pharmacokinetic (PK) modeling methods can be statistically classified as either parametric or nonparametric (NP). Each classification can be divided into maximum likelihood (ML) or Bayesian (B) approaches. In this paper we discuss the nonparametric case using both maximum likelihood and Bayesian approaches. We present two nonparametric methods for estimating the unknown joint population distribution of model parameter values in a pharmacokinetic/pharmacodynamic (PK/PD) dataset. The first method is the NP Adaptive Grid (NPAG). The second is the NP Bayesian (NPB) algorithm with a stick-breaking process to construct a Dirichlet prior. Our objective is to compare the performance of these two methods using a simulated PK/PD dataset. Our results showed excellent performance of NPAG and NPB in a realistically simulated PK study. This simulation allowed us to have benchmarks in the form of the true population parameters to compare with the estimates produced by the two methods, while incorporating challenges like unbalanced sample times and sample numbers as well as the ability to include the covariate of patient weight. We conclude that both NPML and NPB can be used in realistic PK/PD population analysis problems. The advantages of one versus the other are discussed in the paper. NPAG and NPB are implemented in R and freely available for download within the Pmetrics package from www.lapk.org.
In the past one hundred years, deterministic rate equations have been successfully used to infer enzyme-catalysed reaction mechanisms and to estimate rate constants from reaction kinetics experiments conducted in vitro. In recent years, sophisticated experimental techniques have been developed that allow the measurement of enzyme- catalysed and other biopolymer-mediated reactions inside single cells at the single molecule level. Time course data obtained by these methods are considerably noisy because molecule numbers within cells are typically quite small. As a consequence, the interpretation and analysis of single cell data requires stochastic methods, rather than deterministic rate equations. Here we concisely review both experimental and theoretical techniques which enable single molecule analysis with particular emphasis on the major developments in the field of theoretical stochastic enzyme kinetics, from its inception in the mid-twentieth century to its modern day status. We discuss the differences between stochastic and deterministic rate equation models, how these depend on enzyme molecule numbers and substrate inflow into the reaction compartment and how estimation of rate constants from single cell data is possible using recently developed stochastic approaches.
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.
Non linear mixed effect models are classical tools to analyze non linear longitudinal data in many fields such as population Pharmacokinetic. Groups of observations are usually compared by introducing the group affiliations as binary covariates with a reference group that is stated among the groups. This approach is relatively limited as it allows only the comparison of the reference group to the others. In this work, we propose to compare the groups using a penalized likelihood approach. Groups are described by the same structural model but with parameters that are group specific. The likelihood is penalized with a fused lasso penalty that induces sparsity on the differences between groups for both fixed effects and variances of random effects. A penalized Stochastic Approximation EM algorithm is proposed that is coupled to Alternating Direction Method Multipliers to solve the maximization step. An extensive simulation study illustrates the performance of this algorithm when comparing more than two groups. Then the approach is applied to real data from two pharmacokinetic drug-drug interaction trials.
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