2012 AIChE Annual Meeting
(189g) Mixture Component Prediction Using Iterative Optimization Technology
Authors
Kyle R. Leeman, Pfizer Worldwide Research and Development
Yong Zhou, Pfizer Worldwide Research and Development
Yang A. Liu, Pfizer Worldwide Research and Development
Brent Maranzano, Pfizer Worldwide Research and Development
Paul R. Gerst, Pfizer Worldwide Research and Development
Howard W. Ward, Pfizer Worldwide Research and Development
Andreas AM. Mühlenfeld, Pfizer GMS
Matthias Danner, Pfizer GMS
Spectroscopy techniques such as near infrared (NIR), mid Infrared (MIR) and ultraviolet (UV) are commonly used to monitor various process operations such as blending, mixing and chemical reactions in pharmaceutical development. The calibration required for a quantitative measurement can be a labor-intensive and time-consuming to implement. The objective of this study is to predict/monitor/estimate the mixture component ratio without making calibration standards in advance (e.g., minimum effort) while keeping similar prediction ability as with calibration standards. Reference methods such as HPLC/GC are used just for the validation purpose.
Iterative Optimization Technology (IOT) involves the use of an optimization computation using only pure component spectra collected prior to a batch and mixture component spectra collected during a batch. The proposed approach has been successfully applied to many practical projects (solvent mixtures, chemical reactions, feed-frame process, blending process). It is expected that the use of IOT will enhance the adoption of PAT as a routine analytical tool in project labs, especially for process understanding and early development activities.
See more of this Session: Application of Multivariate Analysis in Supporting QbD
See more of this Group/Topical: Topical I: Comprehensive Quality by Design in Pharmaceutical Development and Manufacture
See more of this Group/Topical: Topical I: Comprehensive Quality by Design in Pharmaceutical Development and Manufacture