A machine-learning approach to calibrate generic Raman models for real-time monitoring of cell culture processes
Tulsyan, Schorner, Khodabandehlou, Wang, Coufal, Undey (2019) A machine-learning approach to calibrate generic Raman models for real-time monitoring of cell culture processes Biotechnol Bioeng (IF: 3.8) 116(10) 2575-2586Abstract
The manufacture of biotherapeutic proteins consists of complex upstream unit operations requiring multiple raw materials, analytical techniques, and control strategies to produce safe and consistent products for patients. Raman spectroscopy is a ubiquitous multipurpose analytical technique in biopharmaceutical manufacturing for real-time predictions of critical parameters in cell culture processes. The accuracy of Raman spectroscopy relies on chemometric models that need to be carefully calibrated. The existing calibration procedure is nontrivial to implement as it necessitates executing multiple carefully designed experiments for generating relevant calibration sets. Further, existing procedure yields calibration models that are reliable only in operating conditions they were calibrated in. This creates a unique challenge in clinical manufacturing where products have limited production history. In this paper, a novel machine-learning procedure based on just-in-time learning (JITL) is proposed to calibrate Raman models. Unlike traditional techniques, JITL-based generic Raman models can be reliably used for different modalities, cell lines, culture media, and operating conditions. The accuracy of JITL-based generic models is demonstrated on several validation studies involving real-time predictions of critical cell culture performance parameters, such as glucose, glutamate, glutamine, ammonium, lactate, sodium, calcium, viability, and viable cell density. The proposed JITL framework introduces a paradigm shift in the way industrial Raman models are calibrated, which to the best of authors' knowledge have not been done before.© 2019 Wiley Periodicals, Inc.
Links
http://www.ncbi.nlm.nih.gov/pubmed/31231792http://dx.doi.org/10.1002/bit.27100
