A systematic evaluation of population pharmacokinetic models for polymyxin B in patients with liver and/or kidney dysfunction
© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature..
Polymyxin B (PMB) is considered a last-line treatment for multidrug-resistant (MDR) gram-negative bacterial infections. Model-informed precision dosing with population pharmacokinetics (PopPK) models could help to individualize PMB dosing regimens and improve therapy. However, the external prediction ability of the established PopPK models has not been fully elaborated. This study aimed to systemically evaluate eleven PMB PopPK models from ten published literature based on a new independent population, which was divided into four different populations, patients with liver dysfunction, kidney dysfunction, liver and kidney dysfunction, and normal liver and kidney function. The whole data set consisted of 146 patients with 391 PMB concentrations. The prediction- and simulation-based diagnostics and Bayesian forecasting were conducted to evaluate model predictability. In the overall evaluation process, none of the models exhibited satisfactory predictive ability in both prediction- and simulation-based diagnostic simultaneously. However, the evaluation of the models in the subgroup of patients with normal liver and kidney function revealed improved predictive performance compared to those with liver and/or kidney dysfunction. Bayesian forecasting demonstrated enhanced predictability with the incorporation of two to three prior observations. The external evaluation highlighted a lack of consistency between the prediction results of published models and the external validation dataset. Nonetheless, Bayesian forecasting holds promise in improving the predictive performance of the models, and feedback from therapeutic drug monitoring is crucial in optimizing individual dosing regimens.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
Journal of pharmacokinetics and pharmacodynamics - (2024) vom: 16. Apr. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Li, Xueyong [VerfasserIn] |
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| Links: |
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| Themen: |
External evaluation |
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| Anmerkungen: |
Date Revised 16.04.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1007/s10928-024-09916-9 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM371146143 |
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| 520 | |a © 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. | ||
| 520 | |a Polymyxin B (PMB) is considered a last-line treatment for multidrug-resistant (MDR) gram-negative bacterial infections. Model-informed precision dosing with population pharmacokinetics (PopPK) models could help to individualize PMB dosing regimens and improve therapy. However, the external prediction ability of the established PopPK models has not been fully elaborated. This study aimed to systemically evaluate eleven PMB PopPK models from ten published literature based on a new independent population, which was divided into four different populations, patients with liver dysfunction, kidney dysfunction, liver and kidney dysfunction, and normal liver and kidney function. The whole data set consisted of 146 patients with 391 PMB concentrations. The prediction- and simulation-based diagnostics and Bayesian forecasting were conducted to evaluate model predictability. In the overall evaluation process, none of the models exhibited satisfactory predictive ability in both prediction- and simulation-based diagnostic simultaneously. However, the evaluation of the models in the subgroup of patients with normal liver and kidney function revealed improved predictive performance compared to those with liver and/or kidney dysfunction. Bayesian forecasting demonstrated enhanced predictability with the incorporation of two to three prior observations. The external evaluation highlighted a lack of consistency between the prediction results of published models and the external validation dataset. Nonetheless, Bayesian forecasting holds promise in improving the predictive performance of the models, and feedback from therapeutic drug monitoring is crucial in optimizing individual dosing regimens | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a External evaluation | |
| 650 | 4 | |a Kidney dysfunction | |
| 650 | 4 | |a Liver dysfunction | |
| 650 | 4 | |a Polymyxin B | |
| 650 | 4 | |a Population pharmacokinetics | |
| 700 | 1 | |a Cheng, Yu |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Bingqing |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Bo |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yiying |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Yingbing |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Hailing |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Lili |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Hui |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Maobai |e verfasserin |4 aut | |
| 700 | 1 | |a Que, Wancai |e verfasserin |4 aut | |
| 700 | 1 | |a Qiu, Hongqiang |e verfasserin |4 aut | |
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