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
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Abstract	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.
AbstractList	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.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. 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.
Audience	Academic
Author	Karvaly, Gellért Balázs Neely, Michael Noel Nagy, Zsuzsanna Kocsis, Ibolya Kopitkó, Csaba Vincze, István Zátroch, István
AuthorAffiliation	1 Department of Laboratory Medicine, Semmelweis University, 1089 Budapest, Hungary; vincze.istvan@pharma.semmelweis-univ.hu (I.V.); kocsis.ibolya@med.semmelweis-univ.hu (I.K.) 3 Central Department of Anaesthesiology and Intensive Care, Uzsoki Teaching Hospital, 1145 Budapest, Hungary; zatroch.istvan@uzsoki.hu (I.Z.); kopitko.csaba@uzsoki.hu (C.K.) 4 Central Laboratory, Uzsoki Teaching Hospital, 1145 Budapest, Hungary; nagy.zsuzsanna@uzsoki.hu 2 Laboratory of Applied Pharmacokinetics and Bioinformatics, The Saban Research Institute, University of Southern California, Los Angeles, CA 90027, USA; mneely@chla.usc.edu
AuthorAffiliation_xml	– name: 2 Laboratory of Applied Pharmacokinetics and Bioinformatics, The Saban Research Institute, University of Southern California, Los Angeles, CA 90027, USA; mneely@chla.usc.edu – name: 1 Department of Laboratory Medicine, Semmelweis University, 1089 Budapest, Hungary; vincze.istvan@pharma.semmelweis-univ.hu (I.V.); kocsis.ibolya@med.semmelweis-univ.hu (I.K.) – name: 4 Central Laboratory, Uzsoki Teaching Hospital, 1145 Budapest, Hungary; nagy.zsuzsanna@uzsoki.hu – name: 3 Central Department of Anaesthesiology and Intensive Care, Uzsoki Teaching Hospital, 1145 Budapest, Hungary; zatroch.istvan@uzsoki.hu (I.Z.); kopitko.csaba@uzsoki.hu (C.K.)
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SubjectTerms	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
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Title	Modeling Pharmacokinetics in Individual Patients Using Therapeutic Drug Monitoring and Artificial Population Quasi-Models: A Study with Piperacillin
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