The biopharmaceutical industry is still running in batch mode, mostly because it is a highly regulated industry sector. In the past, sensors were not readily available and in-process control was mainly executed off-line. The most important product parameters are quantity, purity and potency besides adventitious agents and bioburden. There is increasing economic pressure on time-to-market and also on the environmental sustainability of biopharmaceutical manufacturing. New concepts for manufacturing using disposable single-use technologies and integrated bioprocessing will dominate the future of bioprocessing. In order to ensure the quality of pharmaceuticals initiatives such as Process Analytical Technologies, Quality by Design and Continuous Integrated Manufacturing have been established. The vision must be that these initiatives together with technology development pave the way for process automation and autonomous bioprocessing without any human intervention. Then a real-time release would be realized leading to a highly predictive and robust biomanufacturing system. The steps toward such automated and autonomous bioprocessing are reviewed in context of monitoring and control. Starting from statistical treatment of single and multiple sensors, establishing soft sensors with predictive chemometrics and hybrid models. A scenario is described how to integrate soft sensors and predictive chemometrics into modern process control. This will be exemplified by selective downstream processing steps such as chromatography and membrane filtration, the most common unit operations for separation of biopharmaceuticals.

Model-based real-time monitoring of biopharmaceutical production is a major step towards Quality-by-Design in this field and the fundament for model predictive control. Data-driven models have been proven a viable option to model bioprocesses. In the high stakes setting of biopharmaceutical manufacturing it is essential to ensure high model accuracy, robustness and explainability. That is only possible when (i) the data used for modeling is of high quality, (ii) state-of-the-art modeling algorithms are employed and (iii) the input-output mapping of the model has been characterized. In this study we evaluate the accuracy of multiple data-driven models in predicting the monoclonal antibody concentration, dsDNA concentration, host cell protein concentration, and high molecular weight impurity content during elution from a protein A chromatography capture step. We demonstrate how permutation/occlusion-based methods can be used to gain understanding on dependencies learned by of one of the most complex data-driven models; convolutional neural network ensembles. Finally, we present a workflow to test the model behavior in case of simulated sensor fouling and failure. This study represents a major step towards improved viability of data-driven models in biopharmaceutical manufacturing.