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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Published in:Pharmaceutics Vol. 16; no. 3; p. 358
Main Authors: Karvaly, Gellért Balázs, Vincze, István, Neely, Michael Noel, Zátroch, István, Nagy, Zsuzsanna, Kocsis, Ibolya, Kopitkó, Csaba
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 01.03.2024
MDPI
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1999-4923
1999-4923
DOI:10.3390/pharmaceutics16030358