Identification of therapeutic allergen products using their Raman spectral fingerprint

Raman spectroscopy is a widely used technique for the identification of chemical substances and in the quality control of pharmaceutical products. Inelastic scattering of laser light generates unique fingerprints of chemical substances which allows for identification of products and quantification of active components. Using this non-destructive technique for biomedicines like vaccines or therapeutic allergen products introduces new challenges in terms of experimental setup, spectral processing, and their standardization. We explore experimental setups and use machine learning techniques to evaluate the potential of Raman spectroscopy to distinguish between therapeutic allergen products from different manufacturers with closely related bee and wasp venoms as Active Pharmaceutical Ingredients (APIs). A comparison of various models shows that a differentiation of products is possible based on their Raman spectra at accuracies above 95%. A deeper analysis allows to identify key regions in the spectra for differentiation. These can guide further research towards the identification and quantification of biochemical compounds of interest. In conclusion, this proof-of-concept study shows the applicability of Raman spectroscopy in the quality assurance of biomedicines and suggests directions for further in-depth analyses.