Increasing process understanding by analyzing complex interactions in experimental data

Research output: Contribution to journalJournal articleResearchpeer-review

Standard

Increasing process understanding by analyzing complex interactions in experimental data. / Naelapaa, Kaisa; Allesø, Morten; Kristensen, Henning Gjelstrup; Bro, Rasmus; Rantanen, Jukka Tapio; Bertelsen, Poul.

In: Journal of Pharmaceutical Sciences, Vol. 98, No. 5, 2009, p. 1852-1861.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Naelapaa, K, Allesø, M, Kristensen, HG, Bro, R, Rantanen, JT & Bertelsen, P 2009, 'Increasing process understanding by analyzing complex interactions in experimental data', Journal of Pharmaceutical Sciences, vol. 98, no. 5, pp. 1852-1861. https://doi.org/10.1002/jps.21565

APA

Naelapaa, K., Allesø, M., Kristensen, H. G., Bro, R., Rantanen, J. T., & Bertelsen, P. (2009). Increasing process understanding by analyzing complex interactions in experimental data. Journal of Pharmaceutical Sciences, 98(5), 1852-1861. https://doi.org/10.1002/jps.21565

Vancouver

Naelapaa K, Allesø M, Kristensen HG, Bro R, Rantanen JT, Bertelsen P. Increasing process understanding by analyzing complex interactions in experimental data. Journal of Pharmaceutical Sciences. 2009;98(5):1852-1861. https://doi.org/10.1002/jps.21565

Author

Naelapaa, Kaisa ; Allesø, Morten ; Kristensen, Henning Gjelstrup ; Bro, Rasmus ; Rantanen, Jukka Tapio ; Bertelsen, Poul. / Increasing process understanding by analyzing complex interactions in experimental data. In: Journal of Pharmaceutical Sciences. 2009 ; Vol. 98, No. 5. pp. 1852-1861.

Bibtex

@article{825dead056a111de87b8000ea68e967b,
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.",
keywords = "Former Faculty of Pharmaceutical Sciences",
author = "Kaisa Naelapaa and Morten Alles{\o} and Kristensen, {Henning Gjelstrup} and Rasmus Bro and Rantanen, {Jukka Tapio} and Poul Bertelsen",
year = "2009",
doi = "10.1002/jps.21565",
language = "English",
volume = "98",
pages = "1852--1861",
journal = "Journal of Pharmaceutical Sciences",
issn = "0022-3549",
publisher = "Elsevier",
number = "5",

}

RIS

TY - JOUR

T1 - Increasing process understanding by analyzing complex interactions in experimental data

AU - Naelapaa, Kaisa

AU - Allesø, Morten

AU - Kristensen, Henning Gjelstrup

AU - Bro, Rasmus

AU - Rantanen, Jukka Tapio

AU - Bertelsen, Poul

PY - 2009

Y1 - 2009

N2 - 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.

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.

KW - Former Faculty of Pharmaceutical Sciences

U2 - 10.1002/jps.21565

DO - 10.1002/jps.21565

M3 - Journal article

C2 - 18781630

VL - 98

SP - 1852

EP - 1861

JO - Journal of Pharmaceutical Sciences

JF - Journal of Pharmaceutical Sciences

SN - 0022-3549

IS - 5

ER -

ID: 12627146