Systematic Evaluation of Voriconazole Pharmacokinetic Models without Pharmacogenetic Information for Bayesian Forecasting in Critically Ill Patients
Voriconazole (VRC) is used as first line antifungal agent against invasive aspergillosis. Model-based approaches might optimize VRC therapy. This study aimed to investigate the predictive performance of pharmacokinetic models of VRC without pharmacogenetic information for their suitability for model...
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MDPI AG
2022-09-01
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| Online Access: | https://www.mdpi.com/1999-4923/14/9/1920 |
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| author | Simon Kallee Christina Scharf Lea Marie Schatz Michael Paal Michael Vogeser Michael Irlbeck Johannes Zander Michael Zoller Uwe Liebchen |
| author_facet | Simon Kallee Christina Scharf Lea Marie Schatz Michael Paal Michael Vogeser Michael Irlbeck Johannes Zander Michael Zoller Uwe Liebchen |
| author_sort | Simon Kallee |
| collection | DOAJ |
| description | Voriconazole (VRC) is used as first line antifungal agent against invasive aspergillosis. Model-based approaches might optimize VRC therapy. This study aimed to investigate the predictive performance of pharmacokinetic models of VRC without pharmacogenetic information for their suitability for model-informed precision dosing. Seven PopPK models were selected from a systematic literature review. A total of 66 measured VRC plasma concentrations from 33 critically ill patients was employed for analysis. The second measurement per patient was used to calculate relative Bias (rBias), mean error (ME), relative root mean squared error (rRMSE) and mean absolute error (MAE) (i) only based on patient characteristics and dosing history (a priori) and (ii) integrating the first measured concentration to predict the second concentration (Bayesian forecasting). The a priori rBias/ME and rRMSE/MAE varied substantially between the models, ranging from −15.4 to 124.6%/−0.70 to 8.01 mg/L and from 89.3 to 139.1%/1.45 to 8.11 mg/L, respectively. The integration of the first TDM sample improved the predictive performance of all models, with the model by Chen (85.0%) showing the best predictive performance (rRMSE: 85.0%; rBias: 4.0%). Our study revealed a certain degree of imprecision for all investigated models, so their sole use is not recommendable. Models with a higher performance would be necessary for clinical use. |
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| institution | Directory Open Access Journal |
| issn | 1999-4923 |
| language | English |
| last_indexed | 2024-03-09T22:49:04Z |
| publishDate | 2022-09-01 |
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| series | Pharmaceutics |
| spelling | doaj.art-16ad7566d2964aeb886c5e07021f2f4b2023-11-23T18:22:58ZengMDPI AGPharmaceutics1999-49232022-09-01149192010.3390/pharmaceutics14091920Systematic Evaluation of Voriconazole Pharmacokinetic Models without Pharmacogenetic Information for Bayesian Forecasting in Critically Ill PatientsSimon Kallee0Christina Scharf1Lea Marie Schatz2Michael Paal3Michael Vogeser4Michael Irlbeck5Johannes Zander6Michael Zoller7Uwe Liebchen8Department of Anesthesiology, University Hospital, LMU Munich, 81377 Munich, GermanyDepartment of Anesthesiology, University Hospital, LMU Munich, 81377 Munich, GermanyDepartment of Pharmaceutical and Medical Chemistry, Clinical Pharmacy, University of Muenster, 48149 Muenster, GermanyInstitute of Laboratory Medicine, University Hospital, LMU Munich, 81377 Munich, GermanyInstitute of Laboratory Medicine, University Hospital, LMU Munich, 81377 Munich, GermanyDepartment of Anesthesiology, University Hospital, LMU Munich, 81377 Munich, GermanyLaboratory Dr. Brunner, Luisenstr. 7e, 78464 Konstanz, GermanyDepartment of Anesthesiology, University Hospital, LMU Munich, 81377 Munich, GermanyDepartment of Anesthesiology, University Hospital, LMU Munich, 81377 Munich, GermanyVoriconazole (VRC) is used as first line antifungal agent against invasive aspergillosis. Model-based approaches might optimize VRC therapy. This study aimed to investigate the predictive performance of pharmacokinetic models of VRC without pharmacogenetic information for their suitability for model-informed precision dosing. Seven PopPK models were selected from a systematic literature review. A total of 66 measured VRC plasma concentrations from 33 critically ill patients was employed for analysis. The second measurement per patient was used to calculate relative Bias (rBias), mean error (ME), relative root mean squared error (rRMSE) and mean absolute error (MAE) (i) only based on patient characteristics and dosing history (a priori) and (ii) integrating the first measured concentration to predict the second concentration (Bayesian forecasting). The a priori rBias/ME and rRMSE/MAE varied substantially between the models, ranging from −15.4 to 124.6%/−0.70 to 8.01 mg/L and from 89.3 to 139.1%/1.45 to 8.11 mg/L, respectively. The integration of the first TDM sample improved the predictive performance of all models, with the model by Chen (85.0%) showing the best predictive performance (rRMSE: 85.0%; rBias: 4.0%). Our study revealed a certain degree of imprecision for all investigated models, so their sole use is not recommendable. Models with a higher performance would be necessary for clinical use.https://www.mdpi.com/1999-4923/14/9/1920precision medicineantifungalmodel informed precision dosingpharmacokineticstherapeutic drug monitoringvoriconazole |
| spellingShingle | Simon Kallee Christina Scharf Lea Marie Schatz Michael Paal Michael Vogeser Michael Irlbeck Johannes Zander Michael Zoller Uwe Liebchen Systematic Evaluation of Voriconazole Pharmacokinetic Models without Pharmacogenetic Information for Bayesian Forecasting in Critically Ill Patients Pharmaceutics precision medicine antifungal model informed precision dosing pharmacokinetics therapeutic drug monitoring voriconazole |
| title | Systematic Evaluation of Voriconazole Pharmacokinetic Models without Pharmacogenetic Information for Bayesian Forecasting in Critically Ill Patients |
| title_full | Systematic Evaluation of Voriconazole Pharmacokinetic Models without Pharmacogenetic Information for Bayesian Forecasting in Critically Ill Patients |
| title_fullStr | Systematic Evaluation of Voriconazole Pharmacokinetic Models without Pharmacogenetic Information for Bayesian Forecasting in Critically Ill Patients |
| title_full_unstemmed | Systematic Evaluation of Voriconazole Pharmacokinetic Models without Pharmacogenetic Information for Bayesian Forecasting in Critically Ill Patients |
| title_short | Systematic Evaluation of Voriconazole Pharmacokinetic Models without Pharmacogenetic Information for Bayesian Forecasting in Critically Ill Patients |
| title_sort | systematic evaluation of voriconazole pharmacokinetic models without pharmacogenetic information for bayesian forecasting in critically ill patients |
| topic | precision medicine antifungal model informed precision dosing pharmacokinetics therapeutic drug monitoring voriconazole |
| url | https://www.mdpi.com/1999-4923/14/9/1920 |
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