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‪henrik Bjugård Nyberg‬ ‪google Scholar‬

Leo Migdal
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‪henrik bjugård nyberg‬ ‪google scholar‬

Correspondence , Andrew C. Hooker, Department of Pharmacy, Uppsala University, Uppsala, Sweden. Email: andrew.hooker@farmaci.uu.se Revised 2024 Jun 26; Received 2023 Dec 18; Accepted 2024 Jul 19; Collection date 2024 Oct. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no... 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... 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.

WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC? We are happy to have Henrik Bjugård Nyberg joining Pharmetheus as an Associate System Developer in our Uppsala office. He is experienced in R and Shiny development, as well as in pharmacometric method development where he has worked on the saddle-reset algorithm, FREM, and model-based bioequivalence. Welcome to Pharmetheus, Henrik! Find Henrik's bio here: https://lnkd.in/dBNP_4Xg

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Correspondence , Andrew C. Hooker, Department Of Pharmacy, Uppsala University,

Correspondence , Andrew C. Hooker, Department of Pharmacy, Uppsala University, Uppsala, Sweden. Email: andrew.hooker@farmaci.uu.se Revised 2024 Jun 26; Received 2023 Dec 18; Accepted 2024 Jul 19; Collection date 2024 Oct. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the or...

In This Case, Model‐integrated Evidence (MIE) Approaches For BE Assessment

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... 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 f...

WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC? We Are

WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC? We are happy to have Henrik Bjugård Nyberg joining Pharmetheus as an Associate System Developer in our Uppsala office. He is experienced in R and Shiny development, as well as in pharmacometric method development where he has worked on the saddle-reset algorithm, FREM, and model-based bioequivalence. Welcome to Pharmetheus, Henrik! Find Henrik's bio here...