Variable Selection in Multivariate Modeling of Drug Product Formula and Process

Source: AIChE
  • Type:
    Conference Presentation
  • Conference Type:
    AIChE Annual Meeting
  • Presentation Date:
    October 30, 2012
  • Duration:
    30 minutes
  • Skill Level:
  • PDHs:

Share This Post:

Multivariate data analysis methods such as partial least square (PLS) modeling have been increasingly applied to product development, particularly in the quality-by design paradigm. This study applied the PLS modeling to analyze a product development dataset combining a design of experiment study and historical batch data. Attention was paid in particular to the assessment of the importance of predictor variables, and subsequently the variable selection in the PLS modeling. The results indicate that in conducting PLS modeling irrelevant and collinear predictors could be extensively present in the initial model. Therefore, variable selection is an important step in the model optimization. The VIP and coefficient values can be employed to rank the importance of predictors and to help remove irrelevant predictors. To reduce collinear predictors, on the other hand, multiple rounds of PLS modeling on different combinations of predictors may be necessary. To this end, stepwise reduction of predictors based on their VIP/coefficient ranking was introduced and appeared to be an effective approach to identify and remove redundant collinear predictors.
Once the content has been viewed and you have attested to it, you will be able to download and print a certificate for PDH credits. If you have already viewed this content, please click here to login.



Do you already own this?



AIChE Member Credits 0.5
AIChE Members $15.00
AIChE Undergraduate Student Members Free
AIChE Graduate Student Members Free
Non-Members $25.00