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Application of partial least-squares (PLS) modeling in quantifying drug crystallinity in amorphous solid dispersions
Alfred C F Rumondor1, Lynne S Taylor
1Department of Industrial and Physical Pharmacy, College of Pharmacy, Purdue University, West Lafayette, IN 47907, USA.
International Journal of Pharmaceutics
|August 4, 2010
Summary
Powder X-ray diffractometry (PXRD) can quantify drug crystallinity in amorphous solid dispersions. Partial Least-Squares (PLS) analysis offers superior accuracy compared to traditional area methods for this quantification.
Area of Science:
- Pharmaceutical Sciences
- Materials Science
- Analytical Chemistry
Background:
- Quantifying drug crystallinity in amorphous solid dispersions is crucial for drug development.
- Powder X-ray diffractometry (PXRD) is a primary technique for this analysis.
- Accurate quantification requires robust calibration and mathematical models, especially for multiphase systems.
Purpose of the Study:
- To compare two methods for quantifying crystalline drug content in polymer-based amorphous solid dispersions.
- To evaluate the accuracy and predictive power of an area-based model versus a Partial Least-Squares (PLS) multivariate regression model.
Main Methods:
- Development and application of an area-based calculation method using Bragg peak to total area ratios.
- Development and application of a Partial Least-Squares (PLS) multivariate regression model.
- Validation using amorphous solid dispersions with varying drug and polymer ratios.
Main Results:
- The PLS method demonstrated significantly higher accuracy in predicting % drug crystallinity.
- Achieved root-mean-squared error of estimation (RMSEE) values of 2.2%, 1.9%, and 4.7% for PLS across different polymer concentrations.
- The area model yielded substantially higher RMSEE values (11.2%, 17.0%, and 23.6%).
Conclusions:
- Partial Least-Squares (PLS) multivariate regression is a superior method for quantifying drug crystallinity in amorphous solid dispersions.
- PLS models offer additional benefits, including outlier detection, non-linearity assessment, and factor importance analysis.
- This advanced analytical approach enhances the reliability of PXRD data for solid dispersion characterization.
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