Increasing process understanding by analyzing complex interactions in experimental data.
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.
Naelapää
Kaisa
K
Department of Pharmaceutics and Analytical Chemistry, Faculty of Pharmaceutical Sciences, University of Copenhagen, Universitetsparken 2, DK-2100, Copenhagen, Denmark. kn@farma.ku.dk
Allesø
Morten
M
Kristensen
Henning G
HG
Bro
Rasmus
R
Rantanen
Jukka
J
Bertelsen
Poul
P
D000465:Algorithms; D000704:Analysis of Variance; D002626:Chemistry, Pharmaceutical / Q000706:statistics & numerical data*; D003460:Crystallization; D003627:Data Interpretation, Statistical; D004339:Drug Compounding; D007202:Indicators and Reagents; D007700:Kinetics; D012107:Research Design; D012995:Solubility; D013696:Temperature
D007202:Indicators and Reagents