Advances in monitoring and control of refolding kinetics combining PAT and modeling

Abstract

Overexpression of recombinant proteins in Escherichia coli results in misfolded and non-active protein aggregates in the cytoplasm, so-called inclusion bodies (IB). In recent years, a change in the mindset regarding IBs could be observed: IBs are no longer considered an unwanted waste product, but a valid alternative to produce a product with high yield, purity, and stability in short process times. However, solubilization of IBs and subsequent refolding is necessary to obtain a correctly folded and active product. This protein refolding process is a crucial downstream unit operation—commonly done as a dilution in batch or fed-batch mode. Drawbacks of the state-of-the-art include the following: the large volume of buffers and capacities of refolding tanks, issues with uniform mixing, challenging analytics at low protein concentrations, reaction kinetics in non-usable aggregates, and generally low re-folding yields. There is no generic platform procedure available and a lack of robust control strategies. The introduction of Quality by Design (QbD) is the method-of-choice to provide a controlled and reproducible refolding environment. However, reliable online monitoring techniques to describe the refolding kinetics in real-time are scarce. In our view, only monitoring and control of re-folding kinetics can ensure a productive, scalable, and versatile platform technology for re-folding processes. For this review, we screened the current literature for a combination of online process analytical technology (PAT) and modeling techniques to ensure a controlled refolding process. Based on our research, we propose an integrated approach based on the idea that all aspects that cannot be monitored directly are estimated via digital twins and used in real-time for process control. Key points • Monitoring and a thorough understanding of refolding kinetics are essential for model-based control of refolding processes. • The introduction of Quality by Design combining Process Analytical Technology and modeling ensures a robust platform for inclusion body refolding.

Quality by Design in Downstream Process Development of Romiplostim

“…J. 26 (6): 414-425 selected as process parameters in HIC were optimized by Box-Behnken method. Urea concentration was determined as CPP.…”

“…J. 26 (6): [414][415][416][417][418][419][420][421][422][423][424][425] Quantity One software (Bio-Rad). DTT concentration, incubation time, and urea concentration were selected as parameters for DoE, based on literature and previous experiences [13] .…”

“…The QbD utilizes risk analysis, statistical methods, and experimental design to gain an in-depth understanding of the effects of process parameters on each other and product quality [ 2 , 3 ] . QbD approaches are used in the product development processes of different proteins expressed in E. coli and have successfully been utilized to optimize the upstream process [ 4 ] and refolding [ 5 , 6 ] . QbD is also used to create a model that identifies CPPs for HIC [ 7 ] .…”

The role of Raman spectroscopy in biopharmaceuticals from development to manufacturing

“…Raman and UV-resonance Raman spectroscopy are now established biophysical tools that complement other analyses in the PAT toolbox to understand protein higher-order structure [ 113 ]. They are starting to be used in structural analysis of therapeutic proteins because they can measure in the solution phase, are compatible with isotope exchange, are non-destructive, and provide a direct measurement of protein structure.…”

Process optimization using machine learning enhanced design of experiments (DOE): ranibizumab refolding as a case study

“…However, despite significant developments in refolding methodologies over the past two decades, issues persist and low refolding yields continue to be a concern for the manufacturers. 16,17 Hence, optimal design and operation are critical to achieve desirable yields and product quality. In this regard, machine learning (ML) is being increasingly explored as a key enabler.…”