Performance of an Iterative Two-Stage Bayesian Technique for Population Pharmacokinetic Analysis of Rich Data Sets

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f Rifampin, together with isoniazid, has been the backbone of the current first-line treatment of tuberculosis (TB). The ratio of the area under the concentration-time curve from 0 to 24 h (AUC 0 -24 ) to the MIC is the best predictive pharmacokinetic-pharmacodynamic parameter for determinations of efficacy. The objective of this study was to develop an optimal sampling procedure based on population pharmacokinetics to predict AUC 0 -24 values. Patients received rifampin orally once daily as part of their anti-TB treatment. A one-compartmental pharmacokinetic population model with first-order absorption and lag time was developed using observed rifampin plasma concentrations from 55 patients. The population pharmacokinetic model was developed using an iterative two-stage Bayesian procedure and was cross-validated. Optimal sampling strategies were calculated using Monte Carlo simulation (n ‫؍‬ 1,000). The geometric mean AUC 0 -24 value was 41.5 (range, 13.5 to 117) mg · h/liter. The median time to maximum concentration of drug in serum (T max ) was 2.2 h, ranging from 0.4 to 5.7 h. This wide range indicates that obtaining a concentration level at 2 h (C 2 ) would not capture the peak concentration in a large proportion of the population. Optimal sampling using concentrations at 1, 3, and 8 h postdosing was considered clinically suitable with an r 2 value of 0.96, a root mean squared error value of 13.2%, and a prediction bias value of ؊0.4%. This study showed that the rifampin AUC 0 -24 in TB patients can be predicted with acceptable accuracy and precision using the developed population pharmacokinetic model with optimal sampling at time points 1, 3, and 8 h.

Section: Methodsmentioning

confidence: 99%

“…The final one-compartmental model was selected based on the Akaike information criterion (33). Geometric mean pharmacokinetic parameters of the final population model (n ϭ 55) are shown in Table 3.…”

Section: Study Populationmentioning

confidence: 99%

Pharmacokinetic Modeling and Optimal Sampling Strategies for Therapeutic Drug Monitoring of Rifampin in Patients with Tuberculosis

Sturkenboom

Mulder

Jager

et al. 2015

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“…(i) Development of pharmacokinetic model with data for volunteers. The selection of the two-compartmental model was based on Akaike information criterion (AIC) values for one-compartment (AIC ϭ 1,280) and twocompartment (AIC ϭ Ϫ1,073) models (22). The final population pharmacokinetic model parameters developed with data for healthy volunteers (n ϭ 42) are shown in Table 2.…”

Section: Resultsmentioning

confidence: 99%

“…The final model was selected on the basis of the AIC (27). The plasma drug concentrations for the 42 healthy volunteers were used to develop a two-compartment population pharmacokinetic model using an iterative two-stage Bayesian (ITSB) procedure (the KinPop module of the MW/Pharm software package) (22). Clearance (CL) was calculated using the equation (CL m · BSA)/(1.85 ϩ f r · CL CR ), where CL m is metabolic clearance (in liters per hour per 1.85 m 2 ), BSA is the body surface area (in square meters), f r is the drug clearance/creatinine clearance ratio, and CL CR is creatinine clearance (in liters per hour) (23).…”

Section: Methodsmentioning

confidence: 99%

Pharmacokinetic Modeling and Limited Sampling Strategies Based on Healthy Volunteers for Monitoring of Ertapenem in Patients with Multidrug-Resistant Tuberculosis

Rijn

Zuur

Altena

et al. 2017

Ertapenem is a broad-spectrum carbapenem antibiotic whose activity against Mycobacterium tuberculosis is being explored. Carbapenems have antibacterial activity when the plasma concentration exceeds the MIC at least 40% of the time (40% T MIC ). To assess the 40% T MIC in multidrug-resistant tuberculosis (MDR-TB) patients, a limited sampling strategy was developed using a population pharmacokinetic model based on data for healthy volunteers. A two-compartment population pharmacokinetic model was developed with data for 42 healthy volunteers using an iterative two-stage Bayesian method. External validation was performed by Bayesian fitting of the model developed with data for volunteers to the data for individual MDR-TB patients (in which the fitted values of the area under the concentrationtime curve from 0 to 24 h [AUC 0 -24, fit values] were used) using the population model developed for volunteers as a prior. A Monte Carlo simulation (n ϭ 1,000) was used to evaluate limited sampling strategies. Additionally, the 40% T MIC with the free fraction (f 40% T MIC ) of ertapenem in MDR-TB patients was estimated with the population pharmacokinetic model. The population pharmacokinetic model that was developed was shown to overestimate the area under the concentration-time curve from 0 to 24 h (AUC 0 -24 ) in MDR-TB patients by 6.8% (range, Ϫ17.2 to 30.7%). The best-performing limited sampling strategy, which had a time restriction of 0 to 6 h, was found to be sampling at 1 and 5 h (r 2 ϭ 0.78, mean prediction error ϭ Ϫ0.33%, root mean square error ϭ 5.5%). Drug exposure was overestimated by a mean percentage of 4.2% (range, Ϫ15.2 to 23.6%). When a free fraction of 5% was considered and the MIC was set at 0.5 mg/liter, the minimum f 40% T MIC would have been exceeded in 9 out of 12 patients. A population pharmacokinetic model and limited sampling strategy, developed using data from healthy volunteers, were shown to be adequate to predict ertapenem exposure in MDR-TB patients.

“…The total body water was incorporated in the model by basing the model on fat-free mass (FFM), but it was unfortunately not possible to include the bilirubin concentration. A two-compartment model based on the observed anidulafungin concentrations was created using an iterative 2-stage Bayesian procedure (6) (7). Interindividual variability of the pharmacokinetic parameters was assumed to be log-normally distributed.…”

Section: Methodsmentioning

confidence: 99%

Limited-Sampling Strategies for Anidulafungin in Critically Ill Patients

Wanrooy

Proost

Rodgers

et al. 2015

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e Efficacy of anidulafungin is driven by the area under the concentration-time curve (AUC)/MIC ratio. Determination of the anidulafungin AUC along with MIC values can therefore be useful. Since obtaining a full concentration-time curve to determine an AUC is not always feasible or appropriate, limited-sampling strategies may be useful in adequately estimating exposure. The objective of this study was to develop a model to predict the individual anidulafungin exposure in critically ill patients using limited-sampling strategies. Pharmacokinetic data were derived from 20 critically ill patients with invasive candidiasis treated with anidulafungin. These data were used to develop a two-compartment model in MW‫گ‬Pharm using an iterative 2-stage Bayesian procedure. Limited-sampling strategies were subsequently investigated using two methods, a Bayesian analysis and a linear regression analysis. The best possible strategies for these two methods were evaluated by a Bland-Altman analysis for correlation of the predicted and observed AUC from 0 to 24 h (AUC 0 -24 ) values. Anidulafungin exposure can be adequately estimated with the concentration from a single sample drawn 12 h after the start of the infusion either by linear regression (R 2 ‫؍‬ 0.99; bias, 0.05%; root mean square error [RMSE], 3%) or using a population pharmacokinetic model (R 2 ‫؍‬ 0.89; bias, ؊0.1%; RMSE, 9%) in critically ill patients and also in less severely ill patients, as reflected by healthy volunteers. Limited sampling can be advantageous for future studies evaluating the pharmacokinetics and pharmacodynamics of anidulafungin and for therapeutic drug monitoring in selected patients. (This study has been registered at ClinicalTrials.gov under registration no. NCT01047267.)