Raman model development for the protein conformational state classification in different freeze-dried formulations

Area of Science:

• Analytical Chemistry
• Pharmaceutical Sciences
• Biophysics

Background:

• Protein conformational states are critical for drug efficacy.
• Freeze-dried formulations present analytical challenges due to variability.
• Raman spectroscopy offers molecular insights but is sensitive to interferences.

Purpose of the Study:

• To develop a robust multivariate calibration (MVC) model using Raman spectra.
• To predict protein conformational states (native-like vs. non-native) in freeze-dried pharmaceutical formulations.
• To ensure model performance despite variations in formulation type and batch.

Main Methods:

• Variance analysis of Raman spectral data to identify sources of variation.
• Investigating factors like raw material batch variability and Maillard reactions.
• Applying exhaustive calibration and external parameter orthogonalization (EPO) pre-processing techniques.

Main Results:

• Significant spectral background variations were identified across different formulations and batches.
• Raw material batch variability and Maillard reactions were key sources of spectral interference.
• Both exhaustive calibration and EPO successfully enhanced model robustness against spectral interferences.

Conclusions:

• A robust MVC model for predicting protein conformational states from Raman spectra is achievable.
• Pre-processing methods like exhaustive calibration and EPO are effective in managing spectral variations.
• This approach supports better formulation and process development for freeze-dried protein pharmaceuticals.