Big data analytics and advanced mathematical modelling to enhance chromatography performance

The increasing cost of R&D per new approved drug and the growing competition in the market is placing increasing pressure on therapeutic protein manufacturers. Companies are striving for faster, more cost-effective process development, and more productive manufacturing processes. The downstream process is reported to account for 50-80% of the production costs of a therapeutic (Somasundaram et al., 2018), and is frequently referred to as the bottleneck of the manufacturing process. As chromatography processes are the focal technology used in a standard protein purification train, they are a great focus for process development and intensification research.

Process development for chromatography systems relies on time-consuming experimental methods governed by heuristics. The Quality by Design approach aims to implement a more systematic development approach, with enhanced emphasis on process understanding and control founded on engineering and scientific principles. Mechanistic models can increase process understanding, and accelerate process development by enabling rapid investigation of alternative configurations. Therefore, the use of mechanistic modelling for preparative chromatography processes is of great interest to the biopharmaceutical industry, and so research in this area is critical.

To reduce the high operating costs associated with chromatographic bioseparations, companies and academics are also investigating process analytical technology, and its applications in process monitoring and control. Current industrial control systems typically use simple UV, pH and conductivity signals to monitor and respond to deviations in product quality. Novel spectroscopic techniques, combined with advanced multivariate data analysis techniques, can potentially provide improved estimations of product quality in-line, and so possess the potential to greatly enhance chromatography control.

Motivated by the desire to enhance the performance of industrial chromatography processes, the aim of this research is to develop and apply novel control systems and mechanistic modelling to industrial chromatography operations.

The proposed sections of work are as follows:


1. Multivariate data analysis for chromatography root-cause analysis and enhancement of process performance.
2. Mechanistic model development and application for design space interrogation.
3. Variable pathlength UV spectroscopy as a PAT for enhanced chromatography control.
4. Hybrid variable pathlength UV spectroscopy and mechanistic modelling as an advanced chromatography control scheme.

Together, novel process analytical technologies and mechanistic modelling possess qualities to greatly enhance the robustness and productivity of industrial chromatography processes. By building and validating the empirical multivariate models simultaneously with the mechanistic model using similar experimental data, model development times can be reduced. Furthermore, the weaknesses in each technology when applied for process control can be covered by their respective strengths, potentially resulting in a more productive chromatography process than if the techniques were applied separately. Thus, research into hybrid monitoring-modelling control systems is of great interest to the biopharmaceutical industry.