Assessing the accuracy of two Bayesian forecasting programs in estimating vancomycin drug exposure

2020 ◽  
Vol 75 (11) ◽  
pp. 3293-3302 ◽  
Author(s):  
Rashmi V Shingde ◽  
Stephanie E Reuter ◽  
Garry G Graham ◽  
Jane E Carland ◽  
Kenneth M Williams ◽  
...  
Keyword(s):  
Drug Exposure ◽  
Predictive Performance ◽  
Biased Sampling ◽  
Optimal Sampling ◽  
Bayesian Forecasting ◽  
Sample Collection ◽  
Vancomycin Concentration ◽  
Population Pk ◽  
Post Infusion ◽  
Sampling Times

Abstract Background Current guidelines for intravenous vancomycin identify drug exposure (as indicated by the AUC) as the best pharmacokinetic (PK) indicator of therapeutic outcome. Objectives To assess the accuracy of two Bayesian forecasting programs in estimating vancomycin AUC0–∞ in adults with limited blood concentration sampling. Methods The application of seven vancomycin population PK models in two Bayesian forecasting programs was examined in non-obese adults (n = 22) with stable renal function. Patients were intensively sampled following a single (1000 mg or 15 mg/kg) dose. For each patient, AUC was calculated by fitting all vancomycin concentrations to a two-compartment model (defined as AUCTRUE). AUCTRUE was then compared with the Bayesian-estimated AUC0–∞ values using a single vancomycin concentration sampled at various times post-infusion. Results Optimal sampling times varied across different models. AUCTRUE was generally overestimated at earlier sampling times and underestimated at sampling times after 4 h post-infusion. The models by Goti et al. (Ther Drug Monit 2018; 40 212–21) and Thomson et al. (J Antimicrob Chemother 2009; 63 1050–7) had precise and unbiased sampling times (defined as mean imprecision <25% and <38 mg·h/L, with 95% CI for mean bias containing zero) between 1.5 and 6 h and between 0.75 and 2 h post-infusion, respectively. Precise but biased sampling times for Thomson et al. were between 4 and 6 h post-infusion. Conclusions When using a single vancomycin concentration for Bayesian estimation of vancomycin drug exposure (AUC), the predictive performance was generally most accurate with sample collection between 1.5 and 6 h after infusion, though optimal sampling times varied across different population PK models.

scholarly journals Optimal design for population pk/pd models

2012 ◽  
Vol 51 (1) ◽  
pp. 115-130
Author(s):  
Sergei Leonov ◽  
Alexander Aliev
Keyword(s):  
Optimal Design ◽  
Fisher Information ◽  
Fisher Information Matrix ◽  
Information Matrix ◽  
Optimal Sampling ◽  
Design Algorithm ◽  
Sampling Schemes ◽  
Population Pk ◽  
Different Types

ABSTRACT We provide some details of the implementation of optimal design algorithm in the PkStaMp library which is intended for constructing optimal sampling schemes for pharmacokinetic (PK) and pharmacodynamic (PD) studies. We discuss different types of approximation of individual Fisher information matrix and describe a user-defined option of the library.

scholarly journals External validation of the predictive performance of population pharmacokinetic models for phenobarbital in pediatric patients

2020 ◽  
Author(s):  
Sunae Ryu ◽  
Woo Jin Jung ◽  
Zheng Jiao ◽  
Jung Woo Chae ◽  
Hwi-yeol Yun
Keyword(s):  
External Validation ◽  
Predictive Performance ◽  
Validation Dataset ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Pharmacokinetic Models ◽  
Bayesian Forecasting ◽  
Predictive Capability ◽  
Young Infants ◽  
Simulation Based

Aim: Several studies have reported population pharmacokinetic models for phenobarbital (PB), but the predictive performance of these models has not been well documented. This study aims to do external validation of the predictive performance in published pharmacokinetic models. Methods: Therapeutic drug monitoring data collected in neonates and young infants treated with PB for seizure control, was used for external validation. A literature review was conducted through PubMed to identify population pharmacokinetic models. Prediction- and simulation-based diagnostics, and Bayesian forecasting were performed for external validation. The incorporation of size or maturity functions into the published models was also tested for prediction improvement. Results: A total of 79 serum concentrations from 28 subjects were included in the external validation dataset. Seven population pharmacokinetic studies of PB were selected for evaluation. The model by Voller et al. [27] showed the best performance concerning prediction-based evaluation. In simulation-based analyses, the normalized prediction distribution error of two models (those of Shellhaas et al. [24] and Marsot et al. [25]) obeyed a normal distribution. Bayesian forecasting with more than one observation improved predictive capability. Incorporation of both allometric size scaling and maturation function generally enhanced the predictive performance, but with marked improvement for the adult pharmacokinetic model. Conclusion: The predictive performance of published pharmacokinetic models of PB was diverse, and validation may be necessary to extrapolate to different clinical settings. Our findings suggest that Bayesian forecasting improves the predictive capability of individual concentrations for pediatrics.

scholarly journals Population pharmacokinetic evaluation and optimization of amikacin dosage regimens for the management of mycobacterial infections

2020 ◽  
Vol 75 (10) ◽  
pp. 2933-2940
Author(s):  
Hinke Siebinga ◽  
Fiona Robb ◽  
Alison H Thomson
Keyword(s):  
Dose Adjustment ◽  
Population Pharmacokinetic ◽  
Limited Information ◽  
Once Daily ◽  
Mycobacterial Infections ◽  
Dosing Interval ◽  
Dosage Regimens ◽  
Population Pk ◽  
Post Infusion ◽  
Dosage Guidelines

Abstract Background There is limited information on amikacin pharmacokinetics (PK) and dose requirements in patients with mycobacterial infections. Objectives To conduct a population PK analysis of amikacin data from patients with mycobacterial infections and compare predicted concentrations from standard and modified dosage guidelines with recommended target ranges. Methods A population PK model was developed using NONMEM. Cmax, Cmin, concentration 1 h post-infusion (C1h) and AUC0–24 using 15 mg/kg daily (once daily), the WHO table, 25 mg/kg three times weekly (TTW) and modified guidelines were compared using Monte Carlo simulations of 1000 patients. Results Data were available from 124 patients (684 concentrations) aged 16–92 years. CL was 4.64 L/h per 100 mL/min CLCR; V was 0.344 L/kg. With once-daily regimens, Cmax was 35–45 mg/L in 30%–35% of patients and 35–50 mg/L in 46%–48%; C1h was 25–40 mg/L in 53%–59%. The WHO table produced high Cmax values in patients <60 kg and low in patients >75 kg. With TTW dosing, around 30% of Cmax values were 65–80 mg/L, 40% were 60–80 mg/L, and 48% of C1h were 45–65 mg/L. Increasing the dosage interval for patients with CLCR <50 mL/min reduced Cmin values >2 mg/L from 34% to 25% for once-daily dosing and from 18% to 13% for TTW. In patients whose Cmin was <2 mg/L, 82% of AUC0–24 values were 100–300 mg.h/L. Conclusions Standard amikacin dosing guidelines achieve low percentages of target concentrations for mycobacterial infections. Extending the dosing interval in renal impairment and widening target ranges would reduce the need for dose adjustment.

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.

An Interim Evaluation of Systemic F-Ara-a Exposure in Pediatric Allogeneic Hematopoietic Cell Transplant (alloHCT) Recipients Using An Optimal Sampling Design

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 1962-1962
Author(s):  
Janel R Long-Boyle ◽  
Shirley Yan ◽  
Christopher C. Dvorak ◽  
Biljana N. Horn ◽  
Morton Jerome Cowan ◽  
...  
Keyword(s):  
Interim Analysis ◽  
Sampling Strategy ◽  
Volume Of Distribution ◽  
Exploratory Analysis ◽  
Accurate Estimation ◽  
Cell Transplant ◽  
Optimal Sampling ◽  
Exposure Response ◽  
Relative Standard ◽  
Population Pk

Abstract Abstract 1962 Background Fludarabine is a purine analogue used in the preparative regimens of pediatric alloHCT to enhance stem cell engraftment. Administered intravenously as a prodrug, fludarabine (f-ara-AMP) undergoes rapid dephosphorylation in the plasma to the systemically circulating compound, f-ara-a. Despite widespread use, there are no published pharmacokinetic-pharmacodynamic (PK-PD) studies of fludarabine in children undergoing alloHCT. Using an optimal sampling strategy (OSS), we designed a prospective study to evaluate the PK-PD of fludarabine in pediatric alloHCT recipients. We report the year-1 interim PK analysis of this 3-year exposure-response study. Methods Utilizing prior f-ara-a PK data available in adults and D-optimal sampling methods (PFIM software), we designed an OSS for f-ara-a in children. Based on the OSS, the relative standard errors (RSE), representing the precision of estimated PK parameters, were predicted to be less than 20% in a total of 45 children. An interim analysis was planned after year 1 to ensure the sample collection times selected by the OSS were sufficiently informative. Patients were eligible to participate in PK sampling if they were between 0 to 17 years of age, met protocol specific criteria for alloHCT, and would be receiving fludarabine as part of their preparative regimen. All patients underwent PK sampling with dose 1 of fludarabine. Fludarabine was infused per protocol over 30–60 minutes and 1 mL of whole blood was obtained at 2, 4, 8, and 24 h after the start of infusion. PK sampling was repeated following a subsequent dose of fludarabine (dose 2, 3, 4 or 5) at 2 and 24 h. Plasma samples were analyzed by LC-tandem MS and the assay was linear in the range of 5–500 ng/mL. PK model development using f-ara-a concentration-time data was carried out using standard population PK methodologies (NONMEM 7.2 software). Further development of a 2-compartment open model was based on exploratory analysis, diagnostic plots and changes in objective function value (OFV). The addition of allometric scaling, with weight built into the base model scaled to a reference patient having the median weight of the population, resulted in a significant drop in the OFV. No other covariates were tested based on exploratory analysis and plots. The model was parameterized in terms of clearance (CL), volume of distribution-central compartment (Vc), volume of distribution-peripheral compartment (Vp), and inter-compartmental clearance (Q). Residual unexplained variability was modeled as being proportional to the predicted concentrations. Area-under-the-curve (AUC) of f-ara-a was derived from the empirical Bayes estimates of individual CL. Results A total of 94 quantifiable concentrations from 16 subjects (10 male, 6 female) were available for interim PK modeling. Most patients received fludarabine 30–40mg/m2 daily over 3 to 5 days (n=13). In the 3 smallest children (<10kg), fludarabine was dosed at 1.33mg/kg/day for 3 to 4 days. Median age and weight of subjects was 6.5 years (range, 0.3–17) and 23.4kg (6.8-82.3), respectively. Markers for renal function were within normal age limits for all subjects. A 2-compartment model with linear elimination well described the PK of f-ara-a. The population PK estimates for CL, Vc, Vp, Q, and their RSE (%) were 9.0 (6.3%), 30 (8.9%), 34 (6.4%), and 7.7 (11%), respectively. The final model of this interim analysis estimated f-ara-a CL (L/h) = 9.0 * (WT/23.4)0.67. This model predicts f-ara-a CL (%CV) to be lower for children < 10kg (n=3), 3.8 L/h (11.3%) compared to those >10kg, 12.4 L/h (42%). Correspondingly, dose-normalized AUC was predicted to be approximately 2.8 times higher in patients < 10kg. Between-patient variability of CL was estimated to be 23% and the residual variability of concentrations 25%. Conclusion The optimal sampling strategy based on adult prior data allows for accurate estimation of f-ara-a population PK parameters in our study of 16 pediatric alloHCT recipients. These interim results suggest body weight may be used to predict f-ara-a clearance, as well as suggest the need for close evaluation of weight-based dosing to prevent over-exposure in very small children. Over the next 2 years we will continue to enroll children in this PK-PD study to confirm the interim PK results and identify exposure-response relationships to inform optimal dosing of fludarabine in pediatric alloHCT. Disclosures: Off Label Use: Fludarabine (Fludara) has no offical FDA indication for use in children.

scholarly journals Optimal Sampling Strategies to Assess Inulin Clearance in Children by the Inulin Single-Injection Method

2003 ◽  
Vol 49 (7) ◽  
pp. 1170-1179 ◽  
Author(s):  
Lyonne K van Rossum ◽  
Ron A A Mathot ◽  
Karlien Cransberg ◽  
Arnold G Vulto
Keyword(s):  
Pediatric Patients ◽  
Bolus Injection ◽  
Single Injection ◽  
Inulin Clearance ◽  
Optimal Sampling ◽  
Sampling Strategies ◽  
Injection Method ◽  
Validation Set ◽  
Sampling Times ◽  
Single Bolus Injection

Abstract Background: Glomerular filtration rate in patients can be determined by estimating the plasma clearance of inulin with the single-injection method. In this method, a single bolus injection of inulin is administered and several blood samples are collected. For practical and convenient application of this method in children, it is important that a minimal number of samples are drawn. The aim of this study was to develop and validate sampling strategies with fewer samples for reliable prediction of inulin clearance in pediatric patients by the inulin single-bolus-injection method. Methods: Complete inulin plasma concentration-time curves of 154 patients were divided into an index (n = 100) and a validation set (n = 54). A population pharmacokinetic model was developed for the index set. Optimal sampling times were selected based on D-optimality theory. For the validation set, Bayesian estimates of clearance were generated using the derived population parameters and concentrations at two to four sampling times. Bayesian estimates of clearance were compared with the individual reference values of clearance. Results: The strategies with samples taken at 10/30/90/240 min, 10/30/240 min, 10/90/240 min, 30/90/240 min, and 90/240 min allowed accurate prediction of inulin clearance (bias &lt;3% and not significantly different from 0; imprecision &lt;15%). Conclusions: Strategies involving two to four samples, including a sample at 240 min after administration of inulin, in the inulin single-injection method allow accurate prediction of inulin clearance in pediatric patients. Even one blood sample at 240 min showed acceptable performance. The proposed strategies are practical and convenient to children, and reduce repetitive blood sampling without compromising accuracy.

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