Open AccessPopulation Pharmacodynamic Modeling of Epoetin Alfa in End‐Stage Renal Disease Patients Receiving Maintenance Treatment Using Bayesian Approach
Author(s) -
Nguyen Ly Minh,
Meaney Calvin J.,
Rao Gauri G.,
Panesar Mandip,
Krzyzanski Wojciech
Publication year - 2020
Publication title -
cpt: pharmacometrics and systems pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.53
H-Index - 37
ISSN - 2163-8306
DOI - 10.1002/psp4.12556
Subject(s) - epoetin alfa , bayesian probability , medicine , pharmacodynamics , population , prior probability , end stage renal disease , intensive care medicine , computer science , anemia , disease , pharmacokinetics , artificial intelligence , environmental health
The ability to control dosage regimens of erythropoiesis‐stimulating agents (ESAs) to maintain a desired hemoglobin (HGB) target is still elusive. We utilized a Bayesian approach and informative priors to characterize HGB profiles, using simulated drug concentrations, in patients with end‐stage renal disease receiving maintenance doses of epoetin alfa. We also demonstrated an adaptive Bayesian method, applied to individual patients, to improve the accuracy of HGB predictions over time. The results showed that sparse HGB data from daily clinical practice were characterized successfully. The adaptive Bayesian method effectively improved the accuracy of HGB predictions by updating the individual model with new data accounting for within‐subject changes over time. The Bayesian approach presented leverages existing knowledge of the model parameters and has a potential utility in clinical practice to individualize dosage regimens of epoetin alfa and ESAs to achieve target HGB. Further studies are warranted to develop an application for practical use.