Increasing process understanding by analyzing complex interactions in experimental data
Research output: Contribution to journal › Journal article › Research › peer-review
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Increasing process understanding by analyzing complex interactions in experimental data. / Naelapaa, Kaisa; Alleso, Morten; Kristensen, Henning G.; Bro, Rasmus; Rantanen, Jukka; Bertelsen, Poul.
In: Journal of Pharmaceutical Sciences, Vol. 98, No. 5, 2009, p. 1852-1861.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Increasing process understanding by analyzing complex interactions in experimental data
AU - Naelapaa, Kaisa
AU - Alleso, Morten
AU - Kristensen, Henning G.
AU - Bro, Rasmus
AU - Rantanen, Jukka
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 ANOVA model 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 ANOVA model 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