Evaluation of model-integrated evidence approaches for pharmacokinetic bioequivalence studies using model averaging methods.

Publisher: Wiley Country of Publication: United States NLM ID: 101580011 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2163-8306 (Electronic) Linking ISSN: 21638306 NLM ISO Abbreviation: CPT Pharmacometrics Syst Pharmacol Subsets: MEDLINE

Publication: 2015- : Hoboken, NJ : Wiley
Original Publication: New York, NY : Nature Pub. Group

Therapeutic Equivalency* ; Models, Biological* ; Computer Simulation*; Humans ; Administration, Oral ; Pharmacokinetics ; Models, Statistical

Conventional approaches for establishing bioequivalence (BE) between test and reference formulations using non-compartmental analysis (NCA) may demonstrate low power in pharmacokinetic (PK) studies with sparse sampling. In this case, model-integrated evidence (MIE) approaches for BE assessment have been shown to increase power, but may suffer from selection bias problems if models are built on the same data used for BE assessment. This work presents model averaging methods for BE evaluation and compares the power and type I error of these methods to conventional BE approaches for simulated studies of oral and ophthalmic formulations. Two model averaging methods were examined: bootstrap model selection and weight-based model averaging with parameter uncertainty from three different sources, either from a sandwich covariance matrix, a bootstrap, or from sampling importance resampling (SIR). The proposed approaches increased power compared with conventional NCA-based BE approaches, especially for the ophthalmic formulation scenarios, and were simultaneously able to adequately control type I error. In the rich sampling scenario considered for oral formulation, the weight-based model averaging method with SIR uncertainty provided controlled type I error, that was closest to the target of 5%. In sparse-sampling designs, especially the single sample ophthalmic scenarios, the type I error was best controlled by the bootstrap model selection method.
(© 2024 The Author(s). CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

J Pharmacokinet Pharmacodyn. 2017 Dec;44(6):509-520. (PMID: 28887735)
CPT Pharmacometrics Syst Pharmacol. 2013 Jun 26;2:e50. (PMID: 23836189)
J Pharmacokinet Pharmacodyn. 2004 Aug;31(4):321-39. (PMID: 15563006)
CPT Pharmacometrics Syst Pharmacol. 2024 Oct;13(10):1748-1761. (PMID: 39205490)
Comput Methods Programs Biomed. 2012 Nov;108(2):789-805. (PMID: 22640817)
J Pharmacokinet Pharmacodyn. 2017 Dec;44(6):631-640. (PMID: 29119381)
Comput Methods Programs Biomed. 2016 Apr;127:83-93. (PMID: 27000291)
AAPS J. 2020 Oct 30;22(6):141. (PMID: 33125589)
CPT Pharmacometrics Syst Pharmacol. 2023 Jul;12(7):904-915. (PMID: 37114321)
Pharm Res. 2021 Mar;38(3):381-395. (PMID: 33723793)
AAPS J. 2018 Mar 29;20(3):56. (PMID: 29600418)
Biostatistics. 2022 Jan 13;23(1):314-327. (PMID: 32696053)
J Biopharm Stat. 2017;27(2):257-264. (PMID: 27906608)
Stat Med. 2017 Oct 30;36(24):3844-3857. (PMID: 28703360)
Stat Med. 2011 Sep 20;30(21):2582-600. (PMID: 21793036)
Dose Response. 2017 Jun 29;15(2):1559325817715314. (PMID: 28694745)
AAPS J. 2020 Jul 2;22(4):90. (PMID: 32617704)
J Pharmacokinet Pharmacodyn. 2017 Dec;44(6):581-597. (PMID: 29103208)
Pharm Res. 2010 Jan;27(1):92-104. (PMID: 19876723)

Date Created: 20240829 Date Completed: 20241022 Latest Revision: 20241024

20241024

PMC11494900

10.1002/psp4.13217

39205490