Can We Predict Individual Concentrations of Tacrolimus After Liver Transplantation? Application and Tweaking of a Published Population Pharmacokinetic Model in Clinical Practice.

BACKGROUND: Various population pharmacokinetic models have been developed to describe the pharmacokinetics of tacrolimus in adult liver transplantation. However, their extrapolated predictive performance remains unclear in clinical practice. The purpose of this study was to predict concentrations using a selected literature model and to improve these predictions by tweaking the model with a subset of the target population. METHODS: A literature review was conducted to select an adequate population pharmacokinetic model (L). Pharmacokinetic data from therapeutic drug monitoring of tacrolimus in liver-transplanted adults were retrospectively collected. A subset of these data (70%) was exploited to tweak the L-model using the $PRIOR subroutine of the NONMEM software, with 2 strategies to weight the prior information: full informative (F) and optimized (O). An external evaluation was performed on the remaining data; bias and imprecision were evaluated for predictions a priori and Bayesian forecasting. RESULTS: Seventy-nine patients (851 concentrations) were enrolled in the study. The predictive performance of L-model was insufficient for a priori predictions, whereas it was acceptable with Bayesian forecasting, from the third prediction (ie, with ≥2 previously observed concentrations), corresponding to 1 week after transplantation. Overall, the tweaked models showed a better predictive ability than the L-model. The bias of a priori predictions was -41% with the literature model versus -28.5% and -8.73% with tweaked F and O models, respectively. The imprecision was 45.4% with the literature model versus 38.0% and 39.2% with tweaked F and O models, respectively. For Bayesian predictions, whatever the forecasting state, the tweaked models tend to obtain better results. CONCLUSIONS: A pharmacokinetic model can be used, and to improve the predictive performance, tweaking the literature model with the $PRIOR approach allows to obtain better predictions.

An Alzheimer Disease Challenge Model: 24-Hour Sleep Deprivation in Healthy Volunteers, Impact on Working Memory, and Reversal Effect of Pharmacological Intervention: A Randomized, Double-Blind, Placebo-Controlled, Crossover Study.

PURPOSE/BACKGROUND: Alzheimer disease (AD) is a public health issue because of the low number of symptomatic drugs and the difficulty to diagnose it at the prodromal stage. The need to develop new treatments and to validate sensitive tests for early diagnosis could be met by developing a challenge model reproducing cognitive impairments of AD. Therefore, we implemented a 24-hour sleep deprivation (SD) design on healthy volunteers in a randomized, double-blind, placebo-controlled, crossover study on 36 healthy volunteers. METHODS/PROCEDURE: To validate the SD model, cognitive tests were chosen to assess a transient worsening of cognitive functions after SD and a restoration under modafinil as positive control (one dose of 200 mg). Then, the same evaluations were replicated after 15 days of donepezil (5 mg/d) or memantine (10 mg/d). The working memory (WM) function was assessed by the N-back task and the rapid visual processing (RVP) task. FINDINGS/RESULTS: The accuracy of the N-back task and the reaction time of the RVP revealed the alteration of the WM with SD and its restoration with modafinil (changes in score after SD compared with baseline before SD), respectively, in the placebo group and in the modafinil group (-0.2% and +1.0% of satisfactory answers, P = 0.022; +21.3 and +1.9 milliseconds of reaction time, P = 0.025). Alzheimer disease drugs also tended to reverse this deterioration: the accuracy of the N-back task was more stable through SD (compared with -3.0% in the placebo group, respectively, in the memantine group and in the donepezil group: -1.4% and -1.6%, P = 0.027 and P = 0.092) and RVP reaction time was less impacted (compared with +41.3 milliseconds in the placebo group, respectively, in the memantine group and in the donepezil group: +16.1 and +29.3 milliseconds, P = 0.034 and P = 0.459). IMPLICATIONS/CONCLUSIONS: Our SD challenge model actually led to a worsening of WM that was moderated by both modafinil and AD drugs. To use this approach, the cognitive battery, the vulnerability of the subjects to SD, and the expected drug effect should be carefully considered.

Prior information for population pharmacokinetic and pharmacokinetic/pharmacodynamic analysis: overview and guidance with a focus on the NONMEM PRIOR subroutine.

Population pharmacokinetic analysis is used to estimate pharmacokinetic parameters and their variability from concentration data. Due to data sparseness issues, available datasets often do not allow the estimation of all parameters of the suitable model. The PRIOR subroutine in NONMEM supports the estimation of some or all parameters with values from previous models, as an alternative to fixing them or adding data to the dataset. From a literature review, the best practices were compiled to provide a practical guidance for the use of the PRIOR subroutine in NONMEM. Thirty-three articles reported the use of the PRIOR subroutine in NONMEM, mostly in special populations. This approach allowed fast, stable and satisfying modelling. The guidance provides general advice on how to select the most appropriate reference model when there are several previous models available, and to implement and weight the selected parameter values in the PRIOR function. On the model built with PRIOR, the similarity of estimates with the ones of the reference model and the sensitivity of the model to the PRIOR values should be checked. Covariates could be implemented a priori (from the reference model) or a posteriori, only on parameters estimated without prior (search for new covariates).

Model-Informed Therapeutic Drug Monitoring of Meropenem in Critically Ill Patients: Improvement of the Predictive Ability of Literature Models with the PRIOR Approach.

BACKGROUND AND OBJECTIVE: To improve the predictive ability of literature models for model-informed therapeutic drug monitoring (TDM) of meropenem in intensive care units, we propose to tweak the literature models with the "prior approach" using a subset of the data. This study compares the predictive ability of both literature and tweaked models on TDM concentrations of meropenem in critically ill patients. METHODS: Blood samples were collected from patients of an intensive care unit treated with intravenous meropenem. Data were split six times into an "estimation" and a "prediction" datasets. Population pharmacokinetic (popPK) models of meropenem were selected from literature. These models were run on the "estimation" dataset with the $PRIOR subroutine in NONMEM to obtain tweaked models. The literature and tweaked models were used a priori (with covariate only) and with Bayesian fitting to predict each individual concentration from the previous concentration(s). Their respective predictive abilities were compared using median relative prediction error (MDPE%) and median absolute relative prediction error (MDAPE%). RESULTS: The total dataset was composed of 115 concentrations from 58 patients. For each of the six splits, the "estimation" and the "prediction" datasets were respectively composed of 44 and 14 patients or 45 and 13 patients. Six popPK models were selected in the literature. MDPE% and MDAPE% were globally lower for the tweaked than for the literature models, especially for a priori predictions. CONCLUSION: The "prior approach" could be a valuable tool to improve the predictive ability of literature models, especially for a priori predictions, which are important to optimize dosing in emergency situations.