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

2015 ◽  
Vol 59 (8) ◽  
pp. 4907-4913 ◽  
Author(s):  
Marieke G. G. Sturkenboom ◽  
Leonie W. Mulder ◽  
Arthur de Jager ◽  
Richard van Altena ◽  
Rob E. Aarnoutse ◽  
...  
Keyword(s):  
Population Pharmacokinetic Model ◽  
Pharmacokinetic Model ◽  
Mean Squared Error ◽  
Geometric Mean ◽  
Plasma Concentrations ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Optimal Sampling ◽  
Sampling Strategies ◽  
Wide Range

ABSTRACTRifampin, 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 (AUC0–24) 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 AUC0–24values. 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 AUC0–24value was 41.5 (range, 13.5 to 117) mg · h/liter. The median time to maximum concentration of drug in serum (Tmax) was 2.2 h, ranging from 0.4 to 5.7 h. This wide range indicates that obtaining a concentration level at 2 h (C2) 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 anr2value 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 AUC0–24in 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.

scholarly journals Mining Small Routine Clinical Data: A Population Pharmacokinetic Model and Optimal Sampling Times of Capecitabine and its Metabolites

2019 ◽  
Vol 22 ◽  
pp. 112-121 ◽  
Author(s):  
Esther Oyaga-Iriarte ◽  
Asier Insausti ◽  
Lorea Bueno ◽  
Onintza Sayar ◽  
Azucena Aldaz
Keyword(s):  
Clinical Practice ◽  
Clinical Data ◽  
Population Pharmacokinetic Model ◽  
Pharmacokinetic Model ◽  
Visual Predictive Check ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Optimal Sampling ◽  
Multicompartmental Model ◽  
Sampling Times

Purpose: The present study was performed to demonstrate that small amounts of routine clinical data allow to generate valuable knowledge. Concretely, the aims of this research were to build a joint population pharmacokinetic model for capecitabine and three of its metabolites (5-DFUR, 5-FU and 5-FUH2) and to determine optimal sampling times for therapeutic drug monitoring. Methods: We used data of 7 treatment cycles of capecitabine in patients with metastatic colorectal cancer. The population pharmacokinetic model was built as a multicompartmental model using NONMEM and was internally validated by visual predictive check. Optimal sampling times were estimated using PFIM 4.0 following D-optimality criterion. Results: The final model was a multicompartmental model which represented the sequential transformations from capecitabine to its metabolites 5-DFUR, 5-FU and 5-FUH2 and was correctly validated. The optimal sampling times were 0.546, 0.892, 1.562, 4.736 and 8 hours after the administration of the drug. For its correct implementation in clinical practice, the values were rounded to 0.5, 1, 1.5, 5 and 8 hours after the administration of the drug. Conclusions: Capecitabine, 5-DFUR, 5-FU and 5-FUH2 can be correctly described by the joint multicompartmental model presented in this work. The aforementioned times are optimal to maximize the information of samples. Useful knowledge can be obtained for clinical practice from small databases.

scholarly journals Optimal sampling strategies for darunavir and external validation of the underlying population pharmacokinetic model

2020 ◽  
Author(s):  
Gabriel Stillemans ◽  
Leila Belkhir ◽  
Bernard Vandercam ◽  
Anne Vincent ◽  
Vincent Haufroid ◽  
...  
Keyword(s):  
Population Pharmacokinetic Model ◽  
Pharmacokinetic Model ◽  
External Validation ◽  
Diagnostic Methods ◽  
Validation Dataset ◽  
Pharmacokinetic Parameters ◽  
Population Pharmacokinetic ◽  
Optimal Sampling ◽  
Sampling Strategies ◽  
Validation Set

Abstract Purpose A 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. Methods A 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. Results External 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. Conclusions Optimal sampling strategies derived from this model could be used in clinical practice to enhance therapeutic drug monitoring or to conduct additional pharmacokinetic studies.

Population Pharmacokinetic Model of Carbamazepine Derived from Routine Therapeutic Drug Monitoring Data

2007 ◽  
Vol 29 (6) ◽  
pp. 781-788 ◽  
Author(s):  
Katarina Vučićević ◽  
Branislava Miljković ◽  
Ružica Veličković ◽  
Milena Pokrajac ◽  
Aleš Mrhar ◽  
...  
Keyword(s):  
Therapeutic Drug Monitoring ◽  
Drug Monitoring ◽  
Population Pharmacokinetic Model ◽  
Pharmacokinetic Model ◽  
Monitoring Data ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Routine Therapeutic Drug Monitoring

scholarly journals Using Population Pharmacokinetics To Determine Gentamicin Dosing during Extended Daily Diafiltration in Critically Ill Patients with Acute Kidney Injury

2010 ◽  
Vol 54 (9) ◽  
pp. 3635-3640 ◽  
Author(s):  
Jason A. Roberts ◽  
Jonathan Field ◽  
Adam Visser ◽  
Rosemary Whitbread ◽  
Mandy Tallot ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Population Pharmacokinetic Model ◽  
Pharmacokinetic Model ◽  
Kidney Injury ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic

ABSTRACT The objective of the present prospective pharmacokinetic study was to describe the variability of plasma gentamicin concentrations in critically ill patients with acute kidney injury (AKI) necessitating extended daily diafiltration (EDD-f) using a population pharmacokinetic model and to subsequently perform Monte Carlo dosing simulations to determine which dose regimen achieves the pharmacodynamic targets the most consistently. We collected data from 28 gentamicin doses in 14 critically ill adult patients with AKI requiring EDD-f and therapeutic gentamicin. Serial plasma samples were collected. A population pharmacokinetic model was used to describe the pharmacokinetics of gentamicin and perform Monte Carlo simulations with doses of between 3 mg/kg of body weight and 7 mg/kg and at various time points before commencement of EDD-f to evaluate the optimal dosing regimen for achieving pharmacodynamic targets. A two-compartment pharmacokinetic model adequately described the gentamicin clearance while patients were on and off EDD-f. The plasma half-life of gentamicin during EDD-f was 13.8 h, whereas it was 153.4 h without EDD-f. Monte Carlo simulations suggest that dosing with 6 mg/kg every 48 h either 30 min or 1 h before the commencement of EDD-f results in 100% attainment of the target maximum concentration drug in plasma (<10 mg/liter) and sufficient attainment of the target area under the concentration-time curve from 0 to 24 h (AUC0-24; 70 to 120 mg·h/liter). None of the simulated dosing regimens satisfactorily achieved the targets of the minimum concentrations of drug in plasma (<1.0 mg/liter) at 24 h. In conclusion, dosing of gentamicin 30 min to 1 h before the commencement of an EDD-f treatment enables attainment of target peak concentrations for maximal therapeutic effect while enhancing drug clearance to minimize toxicity. Redosing in many patients should occur after 48 h, and we recommend the use of therapeutic drug monitoring to guide dosing to optimize achievement of the AUC0-24 targets.

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