At‐line raman spectroscopy and design of experiments for robust monitoring and control of miniature bioreactor cultures. (10th December 2018)

• Record Type: Journal Article
• Authors: Rowland‐Jones, Ruth C.; Jaques, Colin
• Is Part Of: Biotechnology progress. Volume 35:Number 2(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-12-10
• Subjects: Raman spectroscopy -- process analytical technology (PAT) -- design of experiment -- Chinese hamster ovary cultures (CHO cultures) -- control; Biotechnology -- Periodicals; Food industry and trade -- Periodicals; Bioengineering -- Periodicals; 660.6
• DOI: 10.1002/btpr.2740
• Languages: English
• ISSNs: 8756-7938

Abstract

Abstract : The biopharmaceutical industry is moving toward a more quality by design (QbD) approach that seeks to increase product and process understanding and process control. Miniature bioreactor systems offer a high‐throughput method enabling the assessment of numerous process variables in a controlled environment. However, the number of off/at‐line samples that can be taken is restricted due to the small working volume of each vessel. This limitation may be resolved through the use of Raman spectroscopy due to its ability to obtain multianalyte data from small sample volumes fast. It can, however, be challenging to implement this technique for this application due to the complexity of the sample matrix and that analytes are often present in low concentration. Here, we present a design of experiments (DOE) approach to generate samples for calibrating robust multivariate predictive models measuring glucose, lactate, ammonium, viable cell concentration (VCC) and product concentration, for unclarified cell culture that improves the daily monitoring of each miniature bioreactor vessel. Furthermore, we demonstrate how the output of the glucose and VCC models can be used to control the glucose and main nutrient feed rate within miniature bioreactor cultures to within qualified critical limits set for larger scale vessels. The DOE approach used to generate the calibration sample set is shown to result in models more robust to process changes than by simply using samples taken from the "typical" process. © 2018 American Institute of Chemical Engineers Biotechnol. Prog ., 35: e2740, 2019.