Modeling Pharmacokinetics in Individual Patients Using Therapeutic Drug Monitoring and Artificial Population Quasi-Models: A Study with Piperacillin

Population pharmacokinetic (pop-PK) models constructed for model-informed precision dosing often have limited utility due to the low number of patients recruited. To augment such models, an approach is presented for generating fully artificial quasi-models which can be employed to make individual es...

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Vydané v:	Pharmaceutics Ročník 16; číslo 3; s. 358
Hlavní autori:	Karvaly, Gellért Balázs, Vincze, István, Neely, Michael Noel, Zátroch, István, Nagy, Zsuzsanna, Kocsis, Ibolya, Kopitkó, Csaba
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 01.03.2024 MDPI
Predmet:	Algorithms Analysis Anesthesiology Antibiotics Bayesian models Drug stores Estimates Ethylenediaminetetraacetic acid Health aspects Human subjects Intensive care Laboratories Medical research Medicine, Experimental nonparametric adaptive grid Patient monitoring equipment Patients Pharmacokinetics piperacillin Pneumonia Reagents Tazobactam Teaching hospitals therapeutic drug monitoring Hungary Bayesian models intensive care model-informed precision dosing tazobactam pharmacokinetics piperacillin nonparametric adaptive grid therapeutic drug monitoring
ISSN:	1999-4923, 1999-4923
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Shrnutí:	Population pharmacokinetic (pop-PK) models constructed for model-informed precision dosing often have limited utility due to the low number of patients recruited. To augment such models, an approach is presented for generating fully artificial quasi-models which can be employed to make individual estimates of pharmacokinetic parameters. Based on 72 concentrations obtained in 12 patients, one- and two-compartment pop-PK models with or without creatinine clearance as a covariate were generated for piperacillin using the nonparametric adaptive grid algorithm. Thirty quasi-models were subsequently generated for each model type, and nonparametric maximum a posteriori probability Bayesian estimates were established for each patient. A significant difference in performance was found between one- and two-compartment models. Acceptable agreement was found between predicted and observed piperacillin concentrations, and between the estimates of the random-effect pharmacokinetic variables obtained using the so-called support points of the pop-PK models or the quasi-models as priors. The mean squared errors of the predictions made using the quasi-models were similar to, or even considerably lower than those obtained when employing the pop-PK models. Conclusion: fully artificial nonparametric quasi-models can efficiently augment pop-PK models containing few support points, to make individual pharmacokinetic estimates in the clinical setting.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1999-4923 1999-4923
DOI:	10.3390/pharmaceutics16030358