In-line Raman spectroscopy real-time glucose prediction method for commercial pneumococcal vaccine drug substance fermentation manufacturing process control.

Traditional off-line analysis methods to quantify residual metabolite concentrations in a drug substance fermentation process consume valuable time, require costly resources, and demand potentially hazardous manual operations when working with pathogenic biological organisms. This case study focuses on the successful method development and validation of Raman spectroscopy as an in-line process analytical technology (PAT) to replace the existing off-line assay for real-time glucose quantification. The value and feasibility of Raman spectroscopy as an in situ PAT method are recognized and demonstrated as an enabling technology suited to mitigate challenges fundamental to lab-scale process development and commercial manufacturing. Herein, application of chemometric techniques and multivariate analysis of Raman spectral data is described to develop a partial least squares model capable of predicting real-time and accurate glucose concentrations with a root mean square error of prediction of 1.2 g/L. Through this work, the predictive Raman PAT glucose quantification method was validated to make process decisions in a commercially licensed Pneumococcal vaccine drug substance manufacturing facility for the first time at our company. The method was integral to the process control strategy and applied to accurately and robustly indicate the glucose concentration target ± 2 g/L for triggering subsequent process operations. Several advantages were realized through this work including advanced process control, reduced operating costs, and improved safety posture.