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Published on: August 9, 2022
Determination of amorphous content in the pharmaceutical process environment
Marja Savolainen1, Kirsi Jouppila, Outi Pajamo
1Division of Pharmaceutical Technology, Faculty of Pharmacy, P.O. Box 56, FI-00014 University of Helsinki, Helsinki, Finland.
Near-infrared (NIR) and Raman spectroscopy, combined with multivariate analysis, can accurately quantify amorphous content in pharmaceutical materials. Optimizing preprocessing and sampling area enhances model performance for process monitoring.
Area of Science:
- Pharmaceutical Sciences
- Materials Science
- Analytical Chemistry
Background:
- Amorphous and crystalline states exhibit distinct chemical and physical properties.
- Amorphous regions impact drug bioavailability and material processability.
- Crystalline forms can destabilize amorphous systems by acting as nucleation sites.
Purpose of the Study:
- To quantify amorphous content in a pharmaceutical process environment.
- To evaluate near-infrared (NIR) and Raman spectroscopy for amorphous content determination.
- To assess the influence of multivariate modeling, preprocessing, and sampling on quantification accuracy.
Main Methods:
- Process-induced amorphization of alpha-lactose monohydrate via milling.
- Study of amorphous lactose and trehalose crystallization under high humidity.
- Application of NIR and Raman spectroscopy with multivariate analysis.
- Optimization of data preprocessing (e.g., Standard Normal Variate) and sampling area.
Main Results:
- Both NIR and Raman spectroscopy, coupled with multivariate methods, are suitable for quantitative analysis.
- Standard Normal Variate (SNV) transformation improved model performance.
- Increased sampling area enhanced the accuracy of NIR spectroscopy models (RMSEP of 2.7% for amorphous lactose).
- Raman spectroscopy achieved low RMSEPs (2.3% for lactose, 2.5% for trehalose).
Conclusions:
- Spectroscopic techniques and multivariate modeling offer a viable approach for determining amorphous content in pharmaceutical processes.
- Model performance is sensitive to preprocessing methods and sampling area.
- Comprehensive consideration of all physical forms and the process environment is crucial for optimal multivariate model performance.
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