Analysis of chemometric models applied to Raman spectroscopy for monitoring key metabolites of cell culture.

The Food and Drug Administration (FDA) initiative of Process Analytical Technology (PAT) encourages the monitoring of biopharmaceutical manufacturing processes by innovative solutions. Raman spectroscopy and the chemometric modeling tool Partial Least Squares (PLS) has been applied to this aim for monitoring cell culture process variables. This study compares the chemometric modeling methods of Support Vector Machine radial (SVMr), Random Forests (RF) and Cubist to the commonly used linear PLS model for predicting cell culture components - glucose, lactate and ammonia. This research is performed to assess if the use of PLS as standard practice is justified for chemometric modeling of Raman spectroscopy and cell culture data. Model development data from 5 small scale bioreactors (2 x 1 L & 3 x 5 L) using two Chinese Hamster Ovary (CHO) cell lines were used to predict against a manufacturing scale bioreactor (2000 L). Analysis demonstrated that Cubist predictive models were better for average performance over PLS, SVMr and RF for glucose, lactate and ammonia. The Root Mean Square Error of Prediction (RMSEP) of Cubist modeling was acceptable for the process concentration ranges of glucose (1.437 mM), lactate (2.0 mM) and ammonia (0.819 mM). Interpretation of Variable Importance (VI) results theorize the potential advantages of Cubist modeling in avoiding interference of Raman spectral peaks. Predictors/Raman wavenumbers (cm-1 ) of interest for individual variables are; X1139 - X1141 for glucose, X846 - X849 for lactate, and X2941 - X2943 for ammonia. These results demonstrate that other beneficial chemometric models are available for use in monitoring cell culture with Raman spectroscopy. This article is protected by copyright. All rights reserved.