Population pharmacokinetic analyses of tacrolimus in non-transplant patients: a systematic review.

BACKGROUND AND OBJECTIVES: Tacrolimus (TAC) has been increasingly used in patients with non-transplant settings. Because of its large between-subject variability, several population pharmacokinetic (PPK) studies have been performed to facilitate individualized therapy. This review summarized published PPK models of TAC in non-transplant patients, aiming to clarify factors affecting PKs of TAC and identify the knowledge gap that may require further research. METHODS: The PubMed, Embase databases, and Cochrane Library, as well as related references, were searched from the time of inception of the databases to February 2023, to identify TAC population pharmacokinetic studies modeled in non-transplant patients using a non-linear mixed-effects modeling approach. RESULTS: Sixteen studies, all from Asian countries (China and Korea), were included in this study. Of these studies, eleven and four were carried out in pediatric and adult patients, respectively. One-compartment models were the commonly used structural models for TAC. The apparent clearance (CL/F) of TAC ranged from 2.05 to 30.9 L·h-1 (median of 14.9 L·h-1). Coadministered medication, genetic factors, and weight were the most common covariates affecting TAC-CL/F, and variability in the apparent volume of distribution (V/F) was largely explained by weight. Coadministration with Wuzhi capsules reduced CL/F by about 19 to 43%. For patients with CYP3A5*1*1 and *1*3 genotypes, the CL/F was 39-149% higher CL/F than patients with CYP3A5*1*1. CONCLUSION: The optimal TAC dosage should be adjusted based on the patient's co-administration, body weight, and genetic information (especially CYP3A5 genotype). Further studies are needed to assess the generalizability of the published models to other ethnic groups. Moreover, external validation should be frequently performed to improve the clinical practicality of the models.

Population pharmacokinetics of meropenem in patients undergoing automated peritoneal dialysis.

BACKGROUND: Meropenem is a second-line agent for the treatment of peritoneal dialysis-associated peritonitis (PD peritonitis), while information on pharmacokinetics (PK) of intraperitoneal (i.p.) meropenem is limited in this patient group. The objective of the present evaluation was to assess a pharmacokinetic rationale for the selection of meropenem doses in automated PD (APD) patients based on population PK modelling. METHODS: Data were available from a PK study in six patients undergoing APD who received a single 500 mg dose of meropenem intravenous or i.p. A population PK model was developed for plasma and dialysate concentrations (n = 360) using Monolix. Monte Carlo simulations were carried out to assess the probability of achieving meropenem concentrations above minimum inhibitory concentrations (MICs) of 2 and 8 mg/L, representing susceptible and less susceptible pathogens respectively, for at least 40% of the dosing interval (T >MIC ≥ 40%). RESULTS: A two-compartment model for each plasma and dialysate concentrations with one transit compartment for the transfer from plasma to dialysate fluid described the data well. An i.p. dose of 250 and 750 mg, for an MIC of 2 and 8 mg/L respectively, was sufficient to attain the pharmacokinetic/pharmacodynamic target (T >MIC ≥ 40%) in more than 90% patients in plasma and dialysate. Additionally, the model predicted that no relevant meropenem accumulation in plasma and/or peritoneal fluid would occur with prolonged treatment. CONCLUSION: Our results suggest that an i.p. dose of 750 mg daily is optimal for pathogens with an MIC 2-8 mg/L in APD patients.

Evaluation of covariate effects using variance-based global sensitivity analysis in pharmacometrics.

In pharmacometrics, understanding a covariate effect on an interested outcome is essential for assessing the importance of the covariate. Variance-based global sensitivity analysis (GSA) can simultaneously quantify contribution of each covariate effect to the variability for the interested outcome considering with random effects. The aim of this study was to apply GSA to pharmacometric models to assess covariate effects. Simulations were conducted with pharmacokinetic models to characterize the GSA for assessment of covariate effects and with an example of quantitative systems pharmacology (QSP) models to apply the GSA to a complex model. In the simulations, covariate and random variables were generated to simulate the outcomes using the models. Ratios of variance explained by each factor (each covariate and random effect) over the overall variance of the outcome were used as sensitivity indices. The sensitivity indices were consistent with the effect size of covariate. The sensitivity indices identified the importance of creatinine clearance on the pharmacokinetic exposure for a renally-excreted drug. These sensitivity indices could be applied to plasma concentrations over time (repeated measurable outcomes over time) as interested outcomes. Using the GSA, each contribution of all of the covariate effects could be efficiently identified even in the complex QSP model. Variance-based GSA can provide insight when considering the importance of covariate effects by simultaneously and quantitatively assessing all covariate and random effects on interested outcomes in pharmacometrics.

Quantitative Analysis of Effects of a Single 60Co Gamma Ray Point Exposure on Time-Dependent Change in Locomotor Activity in Rats.

Investigating initial behavioral changes caused by irradiation of animals might provide important information to aid understanding of early health effects of radiation exposure and clinical features of radiation injury. Although previous studies in rodents suggested that radiation exposure leads to reduced activity, detailed properties of the effects were unrevealed due to a lack of proper statistical analysis, which is needed to better elucidate details of changes in locomotor activity. Ten-week-old male Wistar rats were subjected to single point external whole-body irradiation with 60Co gamma rays at 0, 2.0, 3.5, and 5.0 Gy (four rats per group). Infrared sensors were used to continuously record the locomotor activity of each rat. The cumulative number of movements during the night was defined as "activity" for each day. A non-linear mixed effects model accounting for individual differences and daily fluctuation of activity was applied to analyze the rats' longitudinal locomotor data. Our statistical method revealed characteristics of the changes in locomotor activity after radiation exposure, showing that (1) reduction in activity occurred immediately-and in a dose-dependent manner-after irradiation and (2) recovery to pre-irradiation levels required almost one week, with the same recovery rate in each dose group.

Pharmacokinetics of Vancomycin in Patients with Different Renal Function Levels.

There are many determinants of vancomycin clearance, but these have not been analyzed separately in populations with different levels of renal function, which could be why some important factors have been missed. The aim of our study was to compare the pharmacokinetic parameters and factors that may affect vancomycin pharmacokinetics in groups of patients with normal renal function and in those with chronic kidney failure. The study used a population pharmacokinetic modeling approach, based on plasma vancomycin concentrations and other data from 78 patients with chronic kidney failure and 32 patients with normal renal function. The model was developed using NONMEM software and validated by bootstrapping. The final model for patients with impaired kidney function was described by the following equation: CL (L/h) = 0.284 + 0.000596 x DD + 0.00194 x AST, and that for the patients with normal kidney function by: CL (L/h) = 0.0727 + 0.205 x FIB. If our results are confirmed by new studies on two similar populations, these factors could be considered when dosing vancomycin in patients with chronically damaged kidneys, as well as in patients with normal kidneys who frequently require high doses of vancomycin.

Population Pharmacokinetics of Isoniazid in Chinese Tuberculosis Patients / 中国药学杂志

OBJECTIVE: To characterize the population pharmacokinetics of isoniazid in Chinese tuberculosis patients. METHODS: A total of 321 serum samples were obtained from 201 patients receiving oral doses of isoniazid. The effects of 16 covariates including demographics and blood tests to isoniazid's pharmacokinetics were evaluated. Data analysis was performed using non-linear mixed effects modeling (NONMEM). Prediction-corrected visual prediction check was performed for model evaluation. RESULTS: A two-compartment model with first-order absorption and linear elimination can well fit the isoniazid concentration-time data. A "MIXTURE" model was used to separate the subpopulation of 'subgroup A' and 'subgroup B'. Typical clearance of the two subpopulations were 82.7 and 19.3 L·h-1, respectively. CONCLUSION: Model validation shows the final model is reliable, which could be used for individualized treatment.

Preconditioning of Nonlinear Mixed Effects Models for Stabilisation of Variance-Covariance Matrix Computations.

As the importance of pharmacometric analysis increases, more and more complex mathematical models are introduced and computational error resulting from computational instability starts to become a bottleneck in the analysis. We propose a preconditioning method for non-linear mixed effects models used in pharmacometric analyses to stabilise the computation of the variance-covariance matrix. Roughly speaking, the method reparameterises the model with a linear combination of the original model parameters so that the Hessian matrix of the likelihood of the reparameterised model becomes close to an identity matrix. This approach will reduce the influence of computational error, for example rounding error, to the final computational result. We present numerical experiments demonstrating that the stabilisation of the computation using the proposed method can recover failed variance-covariance matrix computations, and reveal non-identifiability of the model parameters.

Influence assessment in censored mixed-effects models using the multivariate Student's-t distribution.

In biomedical studies on HIV RNA dynamics, viral loads generate repeated measures that are often subjected to upper and lower detection limits, and hence these responses are either left- or right-censored. Linear and non-linear mixed-effects censored (LMEC/NLMEC) models are routinely used to analyse these longitudinal data, with normality assumptions for the random effects and residual errors. However, the derived inference may not be robust when these underlying normality assumptions are questionable, especially the presence of outliers and thick-tails. Motivated by this, Matos et al. (2013b) recently proposed an exact EM-type algorithm for LMEC/NLMEC models using a multivariate Student's-t distribution, with closed-form expressions at the E-step. In this paper, we develop influence diagnostics for LMEC/NLMEC models using the multivariate Student's-t density, based on the conditional expectation of the complete data log-likelihood. This partially eliminates the complexity associated with the approach of Cook (1977, 1986) for censored mixed-effects models. The new methodology is illustrated via an application to a longitudinal HIV dataset. In addition, a simulation study explores the accuracy of the proposed measures in detecting possible influential observations for heavy-tailed censored data under different perturbation and censoring schemes.

NIMROD: a program for inference via a normal approximation of the posterior in models with random effects based on ordinary differential equations.

Models based on ordinary differential equations (ODE) are widespread tools for describing dynamical systems. In biomedical sciences, data from each subject can be sparse making difficult to precisely estimate individual parameters by standard non-linear regression but information can often be gained from between-subjects variability. This makes natural the use of mixed-effects models to estimate population parameters. Although the maximum likelihood approach is a valuable option, identifiability issues favour Bayesian approaches which can incorporate prior knowledge in a flexible way. However, the combination of difficulties coming from the ODE system and from the presence of random effects raises a major numerical challenge. Computations can be simplified by making a normal approximation of the posterior to find the maximum of the posterior distribution (MAP). Here we present the NIMROD program (normal approximation inference in models with random effects based on ordinary differential equations) devoted to the MAP estimation in ODE models. We describe the specific implemented features such as convergence criteria and an approximation of the leave-one-out cross-validation to assess the model quality of fit. In pharmacokinetics models, first, we evaluate the properties of this algorithm and compare it with FOCE and MCMC algorithms in simulations. Then, we illustrate NIMROD use on Amprenavir pharmacokinetics data from the PUZZLE clinical trial in HIV infected patients.

Testing the product of slopes in related regressions.

A study was conducted of the relationships among neuroprotective factors and cytokines in brain tissue of mice at different ages that were examined on the effect of dietary restriction on protection after experimentally induced brain stroke. It was of interest to assess whether the cross-product of the slopes of pairs of variables vs. age was positive or negative. To accomplish this, the product of the slopes was estimated and tested to determine if it is significantly different from zero. Since the measurements are taken on the same animals, the models used must account for the non-independence of the measurements within animals. A number of approaches are illustrated. First a multivariate multiple regression model is employed. Since we are interested in a nonlinear function of the parameters (the product) the delta method is used to obtain the standard error of the estimate of the product. Second, a linear mixed-effects model is fit that allows for the specification of an appropriate correlation structure among repeated measurements. The delta method is again used to obtain the standard error. Finally, a non-linear mixed-effects approach is taken to fit the linear-mixed-effects model and conduct the test. A simulation study investigates the properties of the procedure.