Increasing process understanding by analyzing complex interactions in experimental data

Kaisa Naelapaa; Morten Allesø; Henning Gjelstrup Kristensen; Rasmus Bro; Jukka Tapio Rantanen; Poul Bertelsen

doi:10.1002/jps.21565

Increasing process understanding by analyzing complex interactions in experimental data

Kaisa Naelapaa, Morten Allesø, Henning Gjelstrup Kristensen, Rasmus Bro, Jukka Tapio Rantanen, Poul Bertelsen

17 Citationer (Scopus)

Abstract

There is a recognized need for new approaches to understand unit operations with pharmaceutical relevance. A method for analyzing complex interactions in experimental data is introduced. Higher-order interactions do exist between process parameters, which complicate the interpretation of experimental results. In this study, experiments based on mixed factorial design of coating process were performed. Drug release was analyzed by traditional analysis of variance (ANOVA) and generalized multiplicative ANOVA (GEMANOVA). GEMANOVA modeling is introduced in this study as a new tool for increased understanding of a coating process. It was possible to model the response, that is, the amount of drug released, using both mentioned techniques. However, the ANOVAmodel was difficult to interpret as several interactions between process parameters existed. In contrast to ANOVA, GEMANOVA is especially suited for modeling complex interactions and making easily understandable models of these. GEMANOVA modeling allowed a simple visualization of the entire experimental space. Furthermore, information was obtained on how relative changes in the settings of process parameters influence the film quality and thereby drug release.

Originalsprog	Engelsk
Tidsskrift	Journal of Pharmaceutical Sciences
Vol/bind	98
Udgave nummer	5
Sider (fra-til)	1852-1861
Antal sider	10
ISSN	0022-3549
DOI	https://doi.org/10.1002/jps.21565
Status	Udgivet - 2009

Emneord

Det tidligere Farmaceutiske Fakultet

Adgang til dokumentet

10.1002/jps.21565

Citationsformater

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title = "Increasing process understanding by analyzing complex interactions in experimental data",

abstract = "There is a recognized need for new approaches to understand unit operations with pharmaceutical relevance. A method for analyzing complex interactions in experimental data is introduced. Higher-order interactions do exist between process parameters, which complicate the interpretation of experimental results. In this study, experiments based on mixed factorial design of coating process were performed. Drug release was analyzed by traditional analysis of variance (ANOVA) and generalized multiplicative ANOVA (GEMANOVA). GEMANOVA modeling is introduced in this study as a new tool for increased understanding of a coating process. It was possible to model the response, that is, the amount of drug released, using both mentioned techniques. However, the ANOVAmodel was difficult to interpret as several interactions between process parameters existed. In contrast to ANOVA, GEMANOVA is especially suited for modeling complex interactions and making easily understandable models of these. GEMANOVA modeling allowed a simple visualization of the entire experimental space. Furthermore, information was obtained on how relative changes in the settings of process parameters influence the film quality and thereby drug release.",

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AU - Naelapaa, Kaisa

AU - Allesø, Morten

AU - Kristensen, Henning Gjelstrup

AU - Bro, Rasmus

AU - Rantanen, Jukka Tapio

AU - Bertelsen, Poul

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AB - There is a recognized need for new approaches to understand unit operations with pharmaceutical relevance. A method for analyzing complex interactions in experimental data is introduced. Higher-order interactions do exist between process parameters, which complicate the interpretation of experimental results. In this study, experiments based on mixed factorial design of coating process were performed. Drug release was analyzed by traditional analysis of variance (ANOVA) and generalized multiplicative ANOVA (GEMANOVA). GEMANOVA modeling is introduced in this study as a new tool for increased understanding of a coating process. It was possible to model the response, that is, the amount of drug released, using both mentioned techniques. However, the ANOVAmodel was difficult to interpret as several interactions between process parameters existed. In contrast to ANOVA, GEMANOVA is especially suited for modeling complex interactions and making easily understandable models of these. GEMANOVA modeling allowed a simple visualization of the entire experimental space. Furthermore, information was obtained on how relative changes in the settings of process parameters influence the film quality and thereby drug release.

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