Downstream bioprocessing can benefit significantly from using on-line monitoring methods for surveillance, control and optimisation. Timely information on critical operational and product quality parameters provided by on-line monitoring may contribute to high product quality, more efficient process operation and better production economy. Here, recent advances in analytical techniques and tools are critically reviewed and assessed based on their capability to meet typical needs and requirements in the biotechnology industry. Soft sensors, which merge the signals generated from online monitoring devices into mathematical models, are highlighted for accessing critical information in downstream processing.

The quality of upstream processes and their products strongly depends on the control of all influencing parameters. However, several relevant parameters are not measured in standard bioreactor systems. Near-infrared spectroscopy (NIRS) is one promising technology capable of becoming the missing link in sensor technology. This review gives an overview of the technological principles and the technological progress. A broad range of possible applications is presented, forming in its entirety a valuable toolbox for process risk mitigation. Recent applications of NIRS in upstream bioprocesses are discussed. Moreover, the review includes regulatory aspects in implementation, calibration and validation of NIRS instrumentation and models. Key termProcess trajectory: Road of process evolution that displays whether an actual batch process runs similar to good historical batches.

Downstream processing in the manufacturing biopharmaceutical industry is a multistep process separating the desired product from process‐ and product‐related impurities. However, removing product‐related impurities, such as product variants, without compromising the product yield or prolonging the process time due to extensive quality control analytics, remains a major challenge. Here, we show how mechanistic model‐based monitoring, based on analytical quality control data, can predict product variants by modeling their chromatographic separation during product polishing with reversed phase chromatography. The system was described by a kinetic dispersive model with a modified Langmuir isotherm. Solely quality control analytical data on product and product variant concentrations were used to calibrate the model. This model‐based monitoring approach was developed for an insulin purification process. Industrial materials were used in the separation of insulin and two insulin variants, one eluting at the product peak front and one eluting at the product peak tail. The model, fitted to analytical data, used one component to simulate each protein, or two components when a peak displayed a shoulder. This monitoring approach allowed the prediction of the elution patterns of insulin and both insulin variants. The results indicate the potential of using model‐based monitoring in downstream polishing at industrial scale to take pooling decisions.

Artistic illustration of chromatographic column's inside with an antibody and B chain of insulin (not to scale). During the course of the research underlying this thesis, Patricia Roch was enrolled in Forum Scientium, a multidisciplinary doctoral programme at Linköping University, Sweden.