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Ben Hassine K, Daali Y, Gloor Y, Nava T, Théorêt Y, Krajinovic M, Bittencourt H, Satyanarayana Uppugunduri CR, Ansari M. Simulation-Based Optimization of Sampling Schedules for Model-Informed Precision Dosing of Once-Daily and 4-Times-Daily Busulfan in Pediatric Patients. Ther Drug Monit 2024:00007691-990000000-00240. [PMID: 38885146 DOI: 10.1097/ftd.0000000000001217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 03/25/2024] [Indexed: 06/20/2024]
Abstract
BACKGROUND Therapeutic drug monitoring (TDM) is crucial in optimizing the outcomes of hematopoietic stem cell transplantation by guiding busulfan (Bu) dosing. Limited sampling strategies show promise for efficiently adjusting drug doses. However, comprehensive assessments and optimization of sampling schedules for Bu TDM in pediatric patients are limited. We aimed to establish optimal sampling designs for model-informed precision dosing (MIPD) of once-daily (q24h) and 4-times-daily (q6h) Bu administration in pediatric patients. METHODS Simulated data sets were used to evaluate the population pharmacokinetic model-based Bayesian estimation of the area under the concentration-time curve (AUC) for different limited sampling strategy designs. The evaluation was based on the mean prediction error for accuracy and root mean square error for precision. These findings were validated using patient-observed data. In addition, the MIPD protocol was implemented in the Tucuxi software, and its performance was assessed. RESULTS Our Bayesian estimation approach allowed for flexible sampling times while maintaining mean prediction error within ±5% and root mean square error below 10%. Accurate and precise AUC0-24h and cumulative AUC estimations were obtained using 2-sample and single-sample schedules for q6h and q24h dosing, respectively. TDM on 2 separate days was necessary to accurately estimate cumulative exposure, especially in patients receiving q6h Bu. Validation with observed patient data confirmed the precision of the proposed limited sampling scenarios. Implementing the MIPD protocol in Tucuxi software yielded reliable AUC estimations. CONCLUSIONS Our study successfully established precise limited sampling protocols for MIPD of Bu in pediatric patients. Our findings underscore the importance of TDM on at least 2 occasions to accurately achieve desired Bu exposures. The developed MIPD protocol and its implementation in Tucuxi software provide a valuable tool for routine TDM in pediatric hematopoietic stem cell transplantation.
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Affiliation(s)
- Khalil Ben Hassine
- CANSEARCH Research Platform for Pediatric Oncology and Hematology, Department of Pediatrics, Gynecology, and Obstetrics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Youssef Daali
- Division of Clinical Pharmacology and Toxicology, University Hospital of Geneva, Geneva, Switzerland
- Faculty of Medicine & Sciences, University of Geneva, Geneva, Switzerland
| | - Yvonne Gloor
- CANSEARCH Research Platform for Pediatric Oncology and Hematology, Department of Pediatrics, Gynecology, and Obstetrics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Tiago Nava
- Charles-Bruneau Cancer Center, CHU Sainte-Justine Research Center, Montreal, Quebec, Canada
- Department of Pediatrics, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada
- Clinical Pharmacology Unit, CHU Sainte-Justine, Montreal, Quebec, Canada; and
| | - Yves Théorêt
- Charles-Bruneau Cancer Center, CHU Sainte-Justine Research Center, Montreal, Quebec, Canada
- Department of Pediatrics, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada
- Clinical Pharmacology Unit, CHU Sainte-Justine, Montreal, Quebec, Canada; and
| | - Maja Krajinovic
- Charles-Bruneau Cancer Center, CHU Sainte-Justine Research Center, Montreal, Quebec, Canada
- Department of Pediatrics, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada
- Clinical Pharmacology Unit, CHU Sainte-Justine, Montreal, Quebec, Canada; and
| | - Henrique Bittencourt
- Charles-Bruneau Cancer Center, CHU Sainte-Justine Research Center, Montreal, Quebec, Canada
- Department of Pediatrics, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada
- Clinical Pharmacology Unit, CHU Sainte-Justine, Montreal, Quebec, Canada; and
| | - Chakradhara Rao Satyanarayana Uppugunduri
- CANSEARCH Research Platform for Pediatric Oncology and Hematology, Department of Pediatrics, Gynecology, and Obstetrics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Marc Ansari
- CANSEARCH Research Platform for Pediatric Oncology and Hematology, Department of Pediatrics, Gynecology, and Obstetrics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Pediatric Oncology and Hematology, Department of Women, Child, and Adolescent, University Hospital of Geneva, Geneva, Switzerland
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Dejaco A, Dorn C, Paal M, Gruber M, Graf BM, Kees MG. Determination of glomerular filtration rate "en passant" after high doses of iohexol for computed tomography in intensive care medicine-a proof of concept. Front Pharmacol 2024; 15:1346343. [PMID: 38362152 PMCID: PMC10867190 DOI: 10.3389/fphar.2024.1346343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 01/16/2024] [Indexed: 02/17/2024] Open
Abstract
Accurate assessment of renal function is of great clinical and scientific importance, as it is an important pharmacokinetic covariate of pivotal drugs. The iohexol clearance is nearly identical to the glomerular filtration rate, but its determination usually requires an intravenous injection and therefore bears intrinsic risks. This motivates to showcase an "en passant" approach to quantification of renal function without additional risk or blood sampling beyond routine care using real-world data. We enrolled 37 intensive care patients who received high doses of iohexol for computed tomography imaging, and quantified series of iohexol plasma concentrations by high-performance liquid chromatography (HPLC-UV). Iohexol clearance was derived by both log-linear regression and nonlinear least squares fitting and compared to glomerular filtration rate estimated by the CKD-EPI-2021 formulas. Nonlinear fitting not only turned out to be more accurate but also more robust in handling the irregularly timed data points. Concordance of iohexol clearance against estimations based on both creatinine and cystatin C showed a slightly higher bias (-3.44 mL/min/1.73 m2) compared to estimations based on creatinine alone (-0.76 mL/min/1.73 m2), but considerably narrower limits of agreement (±42.8 vs. 56 mL/min/1.73 m2) and higher Lin's correlation (0.84 vs. 0.72). In summary, we have demonstrated the feasibility and performance of the "en passant" variant of the iohexol method in intensive care medicine and described a working protocol for its application in clinical practice and pharmacologic studies.
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Affiliation(s)
- Alexander Dejaco
- Department of Anesthesia, University Hospital Regensburg, Regensburg, Germany
| | - Christoph Dorn
- Institute of Pharmacy, University of Regensburg, Regensburg, Germany
| | - Michael Paal
- Institute for Laboratory Medicine, Hospital of the University of Munich (LMU), Munich, Germany
| | - Michael Gruber
- Department of Anesthesia, University Hospital Regensburg, Regensburg, Germany
| | - Bernhard M. Graf
- Department of Anesthesia, University Hospital Regensburg, Regensburg, Germany
| | - Martin G. Kees
- Department of Anesthesia, University Hospital Regensburg, Regensburg, Germany
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Hovd M, Robertsen I, Woillard JB, Åsberg A. A Method for Evaluating Robustness of Limited Sampling Strategies—Exemplified by Serum Iohexol Clearance for Determination of Measured Glomerular Filtration Rate. Pharmaceutics 2023; 15:pharmaceutics15041073. [PMID: 37111559 PMCID: PMC10143161 DOI: 10.3390/pharmaceutics15041073] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/22/2023] [Accepted: 03/25/2023] [Indexed: 03/29/2023] Open
Abstract
In combination with Bayesian estimates based on a population pharmacokinetic model, limited sampling strategies (LSS) may reduce the number of samples required for individual pharmacokinetic parameter estimations. Such strategies reduce the burden when assessing the area under the concentration versus time curves (AUC) in therapeutic drug monitoring. However, it is not uncommon for the actual sample time to deviate from the optimal one. In this work, we evaluate the robustness of parameter estimations to such deviations in an LSS. A previously developed 4-point LSS for estimation of serum iohexol clearance (i.e., dose/AUC) was used to exemplify the effect of sample time deviations. Two parallel strategies were used: (a) shifting the exact sampling time by an empirical amount of time for each of the four individual sample points, and (b) introducing a random error across all sample points. The investigated iohexol LSS appeared robust to deviations from optimal sample times, both across individual and multiple sample points. The proportion of individuals with a relative error greater than 15% (P15) was 5.3% in the reference run with optimally timed sampling, which increased to a maximum of 8.3% following the introduction of random error in sample time across all four time points. We propose to apply the present method for the validation of LSS developed for clinical use.
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Affiliation(s)
- Markus Hovd
- Section for Pharmacology and Pharmaceutical Biosciences, Department of Pharmacy, University of Oslo, P.O. Box 1068 Blindern, 0316 Oslo, Norway; (I.R.); (A.Å.)
- Correspondence:
| | - Ida Robertsen
- Section for Pharmacology and Pharmaceutical Biosciences, Department of Pharmacy, University of Oslo, P.O. Box 1068 Blindern, 0316 Oslo, Norway; (I.R.); (A.Å.)
| | - Jean-Baptiste Woillard
- Inserm, Univ. Limoges, CHU Limoges, Pharmacology & Toxicology, U 1248, F-87000 Limoges, France;
| | - Anders Åsberg
- Section for Pharmacology and Pharmaceutical Biosciences, Department of Pharmacy, University of Oslo, P.O. Box 1068 Blindern, 0316 Oslo, Norway; (I.R.); (A.Å.)
- Department of Transplantation Medicine, Oslo University Hospital, P.O. Box 4950 Nydalen, 0424 Oslo, Norway
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Evaluation and Validation of the Limited Sampling Strategy of Polymyxin B in Patients with Multidrug-Resistant Gram-Negative Infection. Pharmaceutics 2022; 14:pharmaceutics14112323. [PMID: 36365141 PMCID: PMC9698835 DOI: 10.3390/pharmaceutics14112323] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 10/18/2022] [Accepted: 10/24/2022] [Indexed: 11/30/2022] Open
Abstract
Polymyxin B (PMB) is the final option for treating multidrug-resistant Gram-negative bacterial infections. The acceptable pharmacokinetic/pharmacodynamic target is an area under the concentration–time curve across 24 h at a steady state (AUCss,24h) of 50–100 mg·h/L. The limited sampling strategy (LSS) is useful for predicting AUC values. However, establishing an LSS is a time-consuming process requiring a relatively dense sampling of patients. Further, given the variability among different centers, the predictability of LSSs is frequently questioned when it is extrapolated to other clinical centers. Currently, limited data are available on a reliable PMB LSS for estimating AUCss,24h. This study assessed and validated the practicability of LSSs established in the literature based on data from our center to provide reliable and ready-made PMB LSSs for laboratories performing therapeutic drug monitoring (TDM) of PMB. The influence of infusion and sampling time errors on predictability was also explored to obtain the optimal time points for routine PMB TDM. Using multiple regression analysis, PMB LSSs were generated from a model group of 20 patients. A validation group (10 patients) was used to validate the established LSSs. PMB LSSs from two published studies were validated using a dataset of 30 patients from our center. A population pharmacokinetic model was established to simulate the individual plasma concentration profiles for each infusion and sampling time error regimen. Pharmacokinetic data obtained from the 30 patients were fitted to a two-compartment model. Infusion and sampling time errors observed in real-world clinical practice could considerably affect the predictability of PMB LSSs. Moreover, we identified specific LSSs to be superior in predicting PMB AUCss,24h based on different infusion times. We also discovered that sampling time error should be controlled within −10 to 15 min to obtain better predictability. The present study provides validated PMB LSSs that can more accurately predict PMB AUCss,24h in routine clinical practice, facilitating PMB TDM in other laboratories and pharmacokinetics/pharmacodynamics-based clinical studies in the future.
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Gao Y, Hennig S, Barras M. Monitoring of Tobramycin Exposure: What is the Best Estimation Method and Sampling Time for Clinical Practice? Clin Pharmacokinet 2020; 58:389-399. [PMID: 30140975 DOI: 10.1007/s40262-018-0707-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVES The objective of this article is to investigate the influence of blood sampling times on tobramycin exposure estimation and clinical decisions and to determine the best sampling times for two estimation methods used for therapeutic drug monitoring. METHODS Adult patients with cystic fibrosis, treated with once-daily intravenous tobramycin, were intensively sampled over one 24-h dosing interval to determine true exposure (AUC0-24). The AUC0-24s were then estimated using both log-linear regression and Bayesian forecasting methods for 21 different sampling time combinations. These were compared to true exposure using relative prediction errors. The differences in subsequent dose recommendations were calculated. RESULTS Twelve patients, with a median (range) age of 25 years (18-36) and weight of 66.5 kg (50.6-76.4) contributed 96 tobramycin concentrations. Five hundred and eighty-eight estimated AUC0-24s were compared to 12 measured true AUC0-24 values. Median relative prediction errors ranged from - 34.7 to 45.5% for the log-linear regression method and from - 14.46 to 11.23% for the Bayesian forecasting method across the 21 sampling combinations. The most unbiased exposure estimation was provided from concentrations sampled at 100/640 min after the start of the infusion using log-linear regression and at 70/160 min using Bayesian forecasting. Subsequent dosing recommendations varied greatly depending on the estimation method and the sampling times used. CONCLUSION Sampling times markedly influence bias in AUC0-24 estimation, leading to greatly varied dose adjustments. The impact of blood sampling times on dosing decisions is reduced when using Bayesian forecasting.
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Affiliation(s)
- Yanhua Gao
- School of Pharmacy, Pharmacy Australia Centre of Excellence, University of Queensland, 20 Cornwall Street, Woolloongabba, Brisbane, QLD, 4102, Australia
| | - Stefanie Hennig
- School of Pharmacy, Pharmacy Australia Centre of Excellence, University of Queensland, 20 Cornwall Street, Woolloongabba, Brisbane, QLD, 4102, Australia.
| | - Michael Barras
- School of Pharmacy, Pharmacy Australia Centre of Excellence, University of Queensland, 20 Cornwall Street, Woolloongabba, Brisbane, QLD, 4102, Australia
- Princess Alexandra Hospital, Brisbane, QLD, Australia
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