Advanced monitoring and control of pharmaceutical production processes with Pichia pastoris by using Raman spectroscopy and multivariate calibration methods. Issue 12 (25th September 2017)

• Record Type: Journal Article
• Authors: Voss, Jan‐Patrick; Mittelheuser, Nina E.; Lemke, Roman; Luttmann, Reiner
• Is Part Of: Engineering in life sciences. Volume 17:Issue 12(2017)
• Page Start: 1281
• Page End: 1294
• Publication Date: 2017-09-25
• Subjects: Bioreactor monitoring and control -- In‐line Raman spectroscopy -- Partial least squares regression -- Process analytical technology -- Support vector regression; Bioengineering -- Periodicals; 660.605
• DOI: 10.1002/elsc.201600229
• Languages: English
• ISSNs: 1618-0240

Abstract

Abstract: This contribution includes an investigation of the applicability of Raman spectroscopy as a PAT analyzer in cyclic production processes of a potential Malaria vaccine with Pichia pastoris . In a feasibility study, Partial Least Squares Regression (PLSR) models were created off‐line for cell density and concentrations of glycerol, methanol, ammonia and total secreted protein. Relative cross validation errors RMSEcvrel range from 2.87% (glycerol) to 11.0% (ammonia). In the following, on‐line bioprocess monitoring was tested for cell density and glycerol concentration. By using the nonlinear Support Vector Regression (SVR) method instead of PLSR, the error RMSEPrel for cell density was reduced from 5.01 to 2.94%. The high potential of Raman spectroscopy in combination with multivariate calibration methods was demonstrated by the implementation of a closed loop control for glycerol concentration using PLSR. The strong nonlinear behavior of exponentially increasing control disturbances was met with a feed‐forward control and adaptive correction of control parameters. In general the control procedure works very well for low cell densities. Unfortunately, PLSR models for glycerol concentration are strongly influenced by a correlation with the cell density. This leads to a failure in substrate prediction, which in turn prevents substrate control at cell densities above 16 g/L.