The use of GA‐RxODE (Genetics Algorithms and Running simulations from Ordinary Differential Equations‐based model) method to optimize bioequivalence studies

Abstract Bioequivalence (BE) studies are prerequisite in generic products approval. Normally, they are quite simple in design and expensive in execution, and sometimes suffer ethical questioning. Genetics Algorithms and Running simulations from Ordinary Differential Equations‐based model (GA‐RxODE)...

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Autores principales:	Ezequiel Omar Nuske, Mikhail Morozov, Héctor Alejandro Serra
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	bioequivalence studies generic drugs genetics algorithms NLME models pharmacokinetic Therapeutics. Pharmacology RM1-950
Acceso en línea:	https://doaj.org/article/0adb05e812fe4f52af49d0fafadfe106
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spelling	oai:doaj.org-article:0adb05e812fe4f52af49d0fafadfe1062021-11-16T13:45:54ZThe use of GA‐RxODE (Genetics Algorithms and Running simulations from Ordinary Differential Equations‐based model) method to optimize bioequivalence studies2052-170710.1002/prp2.824https://doaj.org/article/0adb05e812fe4f52af49d0fafadfe1062021-10-01T00:00:00Zhttps://doi.org/10.1002/prp2.824https://doaj.org/toc/2052-1707Abstract Bioequivalence (BE) studies are prerequisite in generic products approval. Normally, they are quite simple in design and expensive in execution, and sometimes suffer ethical questioning. Genetics Algorithms and Running simulations from Ordinary Differential Equations‐based model (GA‐RxODE) is a multipurpose method used in pharmacokinetic (PK) optimization. It can be used to complete concentration–time (C–T) missing data. In this investigation, GA‐RxODE was applied in BE field. For this purpose, three BE studies were selected as a source data comprising formulations of metformin, alprazolam and clonazepam. From them, five blood samples values per volunteer‐round from specific preset times were chosen as if BE study was carried out with five instead of the classic 10–20 samples. With the five values of each volunteer a complete C–T curve was simulated by GA‐RxODE and certain PK estimation parameters (as maximum concentration, Cmax, and area under C–T curve from zero to infinite, AUCinf) were elicited. Finally, with these modeled parameters, a BE analysis was performed according to certain regulatory agencies guidances. Some results, expressed as geometric mean ratios of compared formulations and their 90% confidence intervals (CI90), were as follows: Metformin Cmax = 0.954 (0.878–1.035), AUCinf = 0.949 (0.881–1.022); Alprazolam Cmax = 1.063 (0.924–1.222), AUCinf = 1.036 (0.857–1.249), Clonazepam Cmax = 0.927 (0.831–1.034), and AUCinf = 1.021 (0.931–1.119). All CI90 were inside the 0.8–1.25 BE range. In summary, the simulated data were bioequivalent and non‐significantly different from original studies’ data. This raises the opportunity to perform more economic BE studies to build reliable PK estimation parameters from a few samples per volunteer.Ezequiel Omar NuskeMikhail MorozovHéctor Alejandro SerraWileyarticlebioequivalence studiesgeneric drugsgenetics algorithmsNLME modelspharmacokineticTherapeutics. PharmacologyRM1-950ENPharmacology Research & Perspectives, Vol 9, Iss 5, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	bioequivalence studies generic drugs genetics algorithms NLME models pharmacokinetic Therapeutics. Pharmacology RM1-950
spellingShingle	bioequivalence studies generic drugs genetics algorithms NLME models pharmacokinetic Therapeutics. Pharmacology RM1-950 Ezequiel Omar Nuske Mikhail Morozov Héctor Alejandro Serra The use of GA‐RxODE (Genetics Algorithms and Running simulations from Ordinary Differential Equations‐based model) method to optimize bioequivalence studies
description	Abstract Bioequivalence (BE) studies are prerequisite in generic products approval. Normally, they are quite simple in design and expensive in execution, and sometimes suffer ethical questioning. Genetics Algorithms and Running simulations from Ordinary Differential Equations‐based model (GA‐RxODE) is a multipurpose method used in pharmacokinetic (PK) optimization. It can be used to complete concentration–time (C–T) missing data. In this investigation, GA‐RxODE was applied in BE field. For this purpose, three BE studies were selected as a source data comprising formulations of metformin, alprazolam and clonazepam. From them, five blood samples values per volunteer‐round from specific preset times were chosen as if BE study was carried out with five instead of the classic 10–20 samples. With the five values of each volunteer a complete C–T curve was simulated by GA‐RxODE and certain PK estimation parameters (as maximum concentration, Cmax, and area under C–T curve from zero to infinite, AUCinf) were elicited. Finally, with these modeled parameters, a BE analysis was performed according to certain regulatory agencies guidances. Some results, expressed as geometric mean ratios of compared formulations and their 90% confidence intervals (CI90), were as follows: Metformin Cmax = 0.954 (0.878–1.035), AUCinf = 0.949 (0.881–1.022); Alprazolam Cmax = 1.063 (0.924–1.222), AUCinf = 1.036 (0.857–1.249), Clonazepam Cmax = 0.927 (0.831–1.034), and AUCinf = 1.021 (0.931–1.119). All CI90 were inside the 0.8–1.25 BE range. In summary, the simulated data were bioequivalent and non‐significantly different from original studies’ data. This raises the opportunity to perform more economic BE studies to build reliable PK estimation parameters from a few samples per volunteer.
format	article
author	Ezequiel Omar Nuske Mikhail Morozov Héctor Alejandro Serra
author_facet	Ezequiel Omar Nuske Mikhail Morozov Héctor Alejandro Serra
author_sort	Ezequiel Omar Nuske
title	The use of GA‐RxODE (Genetics Algorithms and Running simulations from Ordinary Differential Equations‐based model) method to optimize bioequivalence studies
title_short	The use of GA‐RxODE (Genetics Algorithms and Running simulations from Ordinary Differential Equations‐based model) method to optimize bioequivalence studies
title_full	The use of GA‐RxODE (Genetics Algorithms and Running simulations from Ordinary Differential Equations‐based model) method to optimize bioequivalence studies
title_fullStr	The use of GA‐RxODE (Genetics Algorithms and Running simulations from Ordinary Differential Equations‐based model) method to optimize bioequivalence studies
title_full_unstemmed	The use of GA‐RxODE (Genetics Algorithms and Running simulations from Ordinary Differential Equations‐based model) method to optimize bioequivalence studies
title_sort	use of ga‐rxode (genetics algorithms and running simulations from ordinary differential equations‐based model) method to optimize bioequivalence studies
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/0adb05e812fe4f52af49d0fafadfe106
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The use of GA‐RxODE (Genetics Algorithms and Running simulations from Ordinary Differential Equations‐based model) method to optimize bioequivalence studies

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