Population Pharmacokinetics of Piperacillin/Tazobactam Across the Adult Lifespan

Hemmersbach‐Miller, Marion; Balevic, Stephen J.; Winokur, Patricia; Landersdorfer, Cornelia B.; Gu, Kenan; Chan, Austin W; Cohen‐Wolkowiez, Michael; Conrad, Thomas; An, Guohua; Kirkpatrick, C. M. J.; Swamy, Geeta K.; Walter, Emmanuel B.; Schmader, Kenneth E.

doi:10.1007/s40262-022-01198-z

Cited by 2 publications

(1 citation statement)

References 39 publications

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“…Several piperacillin population pharmacokinetic models are available in the literature, predominantly with the consideration of single- or, more often, two-compartment models including creatinine clearance as a covariate [ 22 , 23 , 24 ]. Nevertheless, it has recently become apparent that the suitability of these models for making estimates of individual pharmacokinetic properties can be highly variable.…”

Section: Discussionmentioning

confidence: 99%

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

Karvaly,

Vincze,

Neely

et al. 2024

Pharmaceutics

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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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Section: Discussionmentioning

confidence: 99%