Fast-track to a solid dispersion formulation using multi-way analysis of complex interactions

Research output: Contribution to journalJournal articleResearchpeer-review

Standard

Fast-track to a solid dispersion formulation using multi-way analysis of complex interactions. / Wu, Jian-Xiong; Den Berg, Frans Van; Søgaard, Søren Vinter; Rantanen, Jukka.

In: Journal of Pharmaceutical Sciences, Vol. 102, No. 3, 03.2013, p. 904-14.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Wu, J-X, Den Berg, FV, Søgaard, SV & Rantanen, J 2013, 'Fast-track to a solid dispersion formulation using multi-way analysis of complex interactions', Journal of Pharmaceutical Sciences, vol. 102, no. 3, pp. 904-14. https://doi.org/10.1002/jps.23409

APA

Wu, J-X., Den Berg, F. V., Søgaard, S. V., & Rantanen, J. (2013). Fast-track to a solid dispersion formulation using multi-way analysis of complex interactions. Journal of Pharmaceutical Sciences, 102(3), 904-14. https://doi.org/10.1002/jps.23409

Vancouver

Wu J-X, Den Berg FV, Søgaard SV, Rantanen J. Fast-track to a solid dispersion formulation using multi-way analysis of complex interactions. Journal of Pharmaceutical Sciences. 2013 Mar;102(3):904-14. https://doi.org/10.1002/jps.23409

Author

Wu, Jian-Xiong ; Den Berg, Frans Van ; Søgaard, Søren Vinter ; Rantanen, Jukka. / Fast-track to a solid dispersion formulation using multi-way analysis of complex interactions. In: Journal of Pharmaceutical Sciences. 2013 ; Vol. 102, No. 3. pp. 904-14.

Bibtex

@article{c9e03685fa3645dba931e94b38e2cd5d,
title = "Fast-track to a solid dispersion formulation using multi-way analysis of complex interactions",
abstract = "Several factors with complex interactions influence the physical stability of solid dispersions, thus highlighting the need for efficient experimental design together with robust and simple multivariate model. Design of Experiments together with ANalysis Of VAriance (ANOVA) model is one of the central tools when establishing a design space according to the Quality by Design (QbD) approach. However, higher order interaction terms are often significant in these ANOVA models, making the final model difficult to interpret and understand. As this is ordinarily the purpose of applying ANOVA, it poses an obvious problem. In the current study, the GEneralized Multiplicative ANOVA (GEMANOVA) model is proposed as an alternative for the ANOVA model. Two complex multivariate data sets obtained by monitoring the physical stability of a solid dispersion with image analysis and X-ray powder diffraction (XRPD) as responses were subjected to GEMANOVA analysis. The results showed that the obtained GEMANOVA model was easier to interpret and understand than the additive ANOVA model. Furthermore, the GEMANOVA model has additional advantages such as the possibility of readily including multivariate responses (e.g., an entire spectral data set), model uniqueness, and curve resolution abilities. {\circledC} 2012 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 102:904-914, 2013.",
author = "Jian-Xiong Wu and {Den Berg}, {Frans Van} and S{\o}gaard, {S{\o}ren Vinter} and Jukka Rantanen",
note = "Copyright {\circledC} 2012 Wiley Periodicals, Inc.",
year = "2013",
month = "3",
doi = "10.1002/jps.23409",
language = "English",
volume = "102",
pages = "904--14",
journal = "Journal of Pharmaceutical Sciences",
issn = "0022-3549",
publisher = "Elsevier",
number = "3",

}

RIS

TY - JOUR

T1 - Fast-track to a solid dispersion formulation using multi-way analysis of complex interactions

AU - Wu, Jian-Xiong

AU - Den Berg, Frans Van

AU - Søgaard, Søren Vinter

AU - Rantanen, Jukka

N1 - Copyright © 2012 Wiley Periodicals, Inc.

PY - 2013/3

Y1 - 2013/3

N2 - Several factors with complex interactions influence the physical stability of solid dispersions, thus highlighting the need for efficient experimental design together with robust and simple multivariate model. Design of Experiments together with ANalysis Of VAriance (ANOVA) model is one of the central tools when establishing a design space according to the Quality by Design (QbD) approach. However, higher order interaction terms are often significant in these ANOVA models, making the final model difficult to interpret and understand. As this is ordinarily the purpose of applying ANOVA, it poses an obvious problem. In the current study, the GEneralized Multiplicative ANOVA (GEMANOVA) model is proposed as an alternative for the ANOVA model. Two complex multivariate data sets obtained by monitoring the physical stability of a solid dispersion with image analysis and X-ray powder diffraction (XRPD) as responses were subjected to GEMANOVA analysis. The results showed that the obtained GEMANOVA model was easier to interpret and understand than the additive ANOVA model. Furthermore, the GEMANOVA model has additional advantages such as the possibility of readily including multivariate responses (e.g., an entire spectral data set), model uniqueness, and curve resolution abilities. © 2012 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 102:904-914, 2013.

AB - Several factors with complex interactions influence the physical stability of solid dispersions, thus highlighting the need for efficient experimental design together with robust and simple multivariate model. Design of Experiments together with ANalysis Of VAriance (ANOVA) model is one of the central tools when establishing a design space according to the Quality by Design (QbD) approach. However, higher order interaction terms are often significant in these ANOVA models, making the final model difficult to interpret and understand. As this is ordinarily the purpose of applying ANOVA, it poses an obvious problem. In the current study, the GEneralized Multiplicative ANOVA (GEMANOVA) model is proposed as an alternative for the ANOVA model. Two complex multivariate data sets obtained by monitoring the physical stability of a solid dispersion with image analysis and X-ray powder diffraction (XRPD) as responses were subjected to GEMANOVA analysis. The results showed that the obtained GEMANOVA model was easier to interpret and understand than the additive ANOVA model. Furthermore, the GEMANOVA model has additional advantages such as the possibility of readily including multivariate responses (e.g., an entire spectral data set), model uniqueness, and curve resolution abilities. © 2012 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 102:904-914, 2013.

U2 - 10.1002/jps.23409

DO - 10.1002/jps.23409

M3 - Journal article

VL - 102

SP - 904

EP - 914

JO - Journal of Pharmaceutical Sciences

JF - Journal of Pharmaceutical Sciences

SN - 0022-3549

IS - 3

ER -

ID: 44548063