Raman mapping-based non-destructive dissolution prediction of sustained-release tablets.

Area of Science:

• Pharmaceutical Sciences
• Analytical Chemistry
• Materials Science

Background:

• Sustained-release tablets require precise control over drug dissolution profiles for efficacy.
• Non-destructive methods for predicting dissolution are crucial for pharmaceutical quality control.
• Raman chemical imaging offers rich spatial information about tablet composition and structure.

Purpose of the Study:

• To demonstrate the applicability of Raman chemical imaging for non-destructive prediction of in vitro dissolution profiles of sustained-release tablets.
• To establish a methodology linking Raman spectral data to tablet properties and dissolution behavior.
• To explore the use of machine learning models for dissolution prediction based on Raman imaging data.

Main Methods:

• Sustained-release tablets with varying hydroxypropyl methylcellulose (HPMC) concentration and particle size were prepared.
• Raman chemical maps of HPMC were acquired and analyzed using histogram-based and wavelet techniques.
• Principal Component Analysis (PCA) was employed to extract key features (HPMC content, particle size) from spectral data.
• Artificial Neural Networks (ANNs) were trained using PCA scores to predict tablet dissolution profiles.

Main Results:

• PCA successfully identified HPMC content and particle size as key predictors from Raman spectral data.
• ANN models accurately predicted the in vitro dissolution profiles of test tablets.
• The average f2 similarity value exceeded 59, indicating high prediction accuracy with both histogram and wavelet methods.

Conclusions:

• Raman chemical imaging, coupled with PCA and ANNs, provides a robust non-destructive method for predicting sustained-release tablet dissolution.
• The developed methodology enables the conversion of complex chemical map data into actionable numerical inputs for predictive modeling.
• This approach lays the groundwork for analyzing larger datasets using advanced Raman imaging technologies for pharmaceutical quality assurance.