The application of Raman spectroscopy for monitoring product quality attributes in perfusion cell culture

Abstract

In-situ Raman spectroscopy provides enhanced capabilities to monitor and control the mammalian cell culture process in real-time, which conforms to the concepts Process Analytical Technology (PAT) and Quality by Design (QbD) raised by U.S. Food and Drug Administration (FDA), and help us to overcome the challenges encountered in the Pharmaceutical R&D. Product quality control was addressed in FDA’s guidance to encourage continuous manufacturing, few work was done to maintain a steady output of drug product in the continuous mammalian cell culture process with the aid of Raman spectroscopy. Here, the state-of-the-art intensified perfusion cell culture - Raman integrated system was set up where product quality attributes on-line monitoring was archived, which could enhance our product quality attributes (PQA) control capabilities as well as ensuring product quality stability in the cell culture process.In this study, an intensified perfusion cell culture system integrated with real-time Raman analyzer was built-up at 3 L bench-top scale for product quality attributes monitoring. Real-time Raman spectrums were correlated with the PQA data of one-step Protein A purified sample after the alternating tangential flow (ATF) column via partial least square (PLS) modeling, results showed that the Raman spectrum could reflect the PQA changes as a function of elapsed time. Thus the real-time monitoring of 3 representative analytical items for cell culture, i.e., Non-reduced Microchip CE-SDS (SDS_Caliper-NR), size-exclusion chromatography (SEC) and N-linked glycan liquid chromatography (N-glycan LC) were evaluated through Raman analyzer. For different analytical items, around 6∼8 kinds of PLS models with different pre-processing methods were evaluated, which considered the effects of Raman shift range, spectrum pre-processing (1st order derivate or 2nd order derivate), utilization of variable importance in projection (V.I.P) on Raman shift range on the model performance. Then the trained models were tested in another intensified perfusion batch with changed process.As for SDS_Caliper-NR main peak, SEC monomer, high molecule weight (HMW) species, Mannose 5 (Man5) percentage, our optimized models could predict these items accurately, with relatively low root mean square error of cross validation (RMSECV) of 0.37 %, 0.44 %, 0.24 % and 0.51 %, respectively. Adapting these models in another 3 L intensified perfusion culture bioreactor with different processes, these models still have predictability, yielded root mean square error of prediction (RMSEP) of 1.88 %, 1.74 %, 0.90 %, 2.79 %, suggesting that Raman spectrums could capture the PQA profiling trends and these models were quite robust against some process changes like pH strategy, perfusion rate, additional feeding. We also found that the 1st order derivate with standard normal variate transformation (SNV), Raman wavenumber from 800 to 1800 cm−1 and V.I.P > 0.8 usually obtained the best performance in the training set, suggesting a golden point for Raman PQA monitoring of cell culture process.This work demonstrated that real-time Raman spectroscopy is an effective PAT tool for on-line product quality attributes monitoring in the cell culture process, especially for continuous perfusion cell culture, allowing us to explore the possibility of PQA on-line tuning in the continuous manufacturing.