Dataset Information

Population Pharmacokinetic Model and Limited Sampling Strategies for Personalized Dosing of Levofloxacin in Tuberculosis Patients.

ABSTRACT: Levofloxacin is an antituberculosis drug with substantial interindividual pharmacokinetic variability; therapeutic drug monitoring (TDM) could therefore be helpful to improve treatment results. TDM would be more feasible with limited sampling strategies (LSSs), a method to estimate the area under the concentration curve for the 24-h dosing interval (AUC0-24) by using a limited number of samples. This study aimed to develop a population pharmacokinetic (popPK) model of levofloxacin in tuberculosis patients, along with LSSs using a Bayesian and multiple linear regression approach. The popPK model and Bayesian LSS were developed using data from 30 patients and externally validated with 20 patients. The LSS based on multiple linear regression was internally validated using jackknife analysis. Only clinically suitable LSSs (maximum time span, 8?h; minimum interval, 1?h; 1 to 3 samples) were tested. Performance criteria were root-mean-square error (RMSE) of <15%, mean prediction error (MPE) of <5%, and r 2 value of >0.95. A one-compartment model with lag time best described the data while only slightly underestimating the AUC0-24 (mean, -7.9%; standard error [SE], 1.7%). The Bayesian LSS using 0- and 5-h postdose samples (RMSE, 8.8%; MPE, 0.42%; r 2 = 0.957) adequately estimated the AUC0-24, with a mean underestimation of -4.4% (SE, 2.7%). The multiple linear regression LSS using 0- and 4-h postdose samples (RMSE, 7.0%; MPE, 5.5%; r 2 = 0.977) was internally validated, with a mean underestimation of -0.46% (SE, 2.0%). In this study, we successfully developed a popPK model and two LSSs that could be implemented in clinical practice to assist TDM of levofloxacin. (This study has been registered at ClinicalTrials.gov under identifier NCT01918397.).

SUBMITTER: van den Elsen SHJ

PROVIDER: S-EPMC6256746 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Population Pharmacokinetic Model and Limited Sampling Strategies for Personalized Dosing of Levofloxacin in Tuberculosis Patients.

van den Elsen Simone H J SHJ Sturkenboom Marieke G G MGG Van't Boveneind-Vrubleuskaya Natasha N Skrahina Alena A van der Werf Tjip S TS Heysell Scott K SK Mpagama Stellah S Migliori Giovanni B GB Peloquin Charles A CA Touw Daan J DJ Alffenaar Jan-Willem C JC

Antimicrobial agents and chemotherapy 20181126 12

Levofloxacin is an antituberculosis drug with substantial interindividual pharmacokinetic variability; therapeutic drug monitoring (TDM) could therefore be helpful to improve treatment results. TDM would be more feasible with limited sampling strategies (LSSs), a method to estimate the area under the concentration curve for the 24-h dosing interval (AUC<sub>0-24</sub>) by using a limited number of samples. This study aimed to develop a population pharmacokinetic (popPK) model of levofloxacin in ...[more]

PMID: 30373800

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Project description:Purpose: The aim of this study is i) to establish a strategy to estimate the area under the curve of the dosing interval (AUC0-12h) of mycophenolic acid (MPA) in the heart transplant recipients and ii) to find the covariates that significantly affect the pharmacokinetics of MPA exposure. Methods: This single-center, prospective, open-label, observational study was conducted in 91 adult heart transplant recipients orally taking mycophenolate mofetil dispersible tablets. Samples collected intensively and sparsely were analyzed by the enzyme-multiplied immunoassay technique, and all the data were used in PPK modeling. Potential covariates were tested stepwise. The goodness-of-fit plots, the normalized prediction distribution error, and prediction-corrected visual predictive check were used for model evaluation. Optimal sampling times by ED-optimal strategy and multilinear regression (MLR) were analyzed based on the simulated data by the final PPK model. Moreover, using intensive data from 14 patients, the accuracy of AUC0-12h estimation was evaluated by Passing-Bablok regression analysis and Bland-Alman plots for both the PPK model and MLR equation. Results: A two-compartment model with first-order absorption and elimination with a lag time was chosen as the structure model. Co-medication of proton pump inhibitors (PPIs), estimated glomerular filtration rate (eGFR), and albumin (ALB) were found to significantly affect bioavailability (F), clearance of central compartment (CL/F), and the distribution volume of the central compartment (V2/F), respectively. Co-medication of PPIs decreased F by 27.6%. When eGFR decreased by 30 ml/min/1.73 m2, CL/F decreased by 23.7%. However, the impact of ALB on V2/F was limited to MPA exposure. The final model showed an adequate fitness of the data. The optimal sampling design was pre-dose and 1 and 4 h post-dose for pharmacokinetic estimation. The best-fit linear equation was finally established as follows: AUC0-12h = 3.539 × C0 + 0.288 × C0.5 + 1.349 × C1 + 6.773 × C4.5. Conclusion: A PPK model was established with three covariates in heart transplant patients. Co-medication of PPIs and eGFR had a remarkable impact on AUC0-12h of MPA. A linear equation was also concluded with four time points as an alternative way to estimate AUC0-12h for MPA.

Project description:PurposeA variety of diagnostic methods are available to validate the performance of population pharmacokinetic models. Internal validation, which applies these methods to the model building dataset and to additional data generated through Monte Carlo simulations, is often sufficient, but external validation, which requires a new dataset, is considered a more rigorous approach, especially if the model is to be used for predictive purposes. Our first objective was to validate a previously published population pharmacokinetic model of darunavir, an HIV protease inhibitor boosted with ritonavir or cobicistat. Our second objective was to use this model to derive optimal sampling strategies that maximize the amount of information collected with as few pharmacokinetic samples as possible.MethodsA validation dataset comprising 164 sparsely sampled individuals using ritonavir-boosted darunavir was used for validation. Standard plots of predictions and residuals, NPDE, visual predictive check, and bootstrapping were applied to both the validation set and the combined learning/validation set in NONMEM to assess model performance. D-optimal designs for darunavir were then calculated in PopED and further evaluated in NONMEM through simulations.ResultsExternal validation confirmed model robustness and accuracy in most scenarios but also highlighted several limitations. The best one-, two-, and three-point sampling strategies were determined to be pre-dose (0 h); 0 and 4 h; and 1, 4, and 19 h, respectively. A combination of samples at 0, 1, and 4 h was comparable to the optimal three-point strategy. These could be used to reliably estimate individual pharmacokinetic parameters, although with fewer samples, precision decreased and the number of outliers increased significantly.ConclusionsOptimal sampling strategies derived from this model could be used in clinical practice to enhance therapeutic drug monitoring or to conduct additional pharmacokinetic studies.

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Dataset Information

Population Pharmacokinetic Model and Limited Sampling Strategies for Personalized Dosing of Levofloxacin in Tuberculosis Patients.

Publications

Population Pharmacokinetic Model and Limited Sampling Strategies for Personalized Dosing of Levofloxacin in Tuberculosis Patients.

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