Innovative biologics, including cell therapeutics, virus-like particles, exosomes,recombinant proteins, and peptides, seem likely to substitute monoclonal antibodies as the maintherapeutic entities in manufacturing over the next decades. This molecular variety causes agrowing need for a general change of methods as well as mindset in the process development stage,as there are no platform processes available such as those for monoclonal antibodies. Moreover,market competitiveness demands hyper-intensified processes, including accelerated decisionstoward batch or continuous operation of dedicated modular plant concepts. This indicates gaps inprocess comprehension, when operation windows need to be run at the edges of optimization. Inthis editorial, the authors review and assess potential methods and begin discussing possiblesolutions throughout the workflow, from process development through piloting to manufacturingoperation from their point of view and experience. Especially, the state-of-the-art for modeling inred biotechnology is assessed, clarifying differences and applications of statistical, rigorousphysical-chemical based models as well as cost modeling. “Digital-twins” are described and effortsvs. benefits for new applications exemplified, including the regulation-demanded QbD (quality bydesign) and PAT (process analytical technology) approaches towards digitalization or industry 4.0based on advanced process control strategies. Finally, an analysis of the obstacles and possiblesolutions for any successful and efficient industrialization of innovative methods from processdevelopment, through piloting to manufacturing, results in some recommendations. A centralquestion therefore requires attention: Considering that QbD and PAT have been required byauthorities since 2004, can any biologic manufacturing process be approved by the regulatoryagencies without being modeled by a “digital-twin” as part of the filing documentation?
Continuous manufacturing opens up new operation windows with improved product quality in contrast to documented lot deviations in batch or fed-batch operations. A more sophisticated process control strategy is needed to adjust operation parameters and keep product quality constant during long-term operations. In the present study, the applicability of a combination of spectroscopic methods was evaluated to enable Advanced Process Control (APC) in continuous manufacturing by Process Analytical Technology (PAT). In upstream processing (USP) and aqueous two-phase extraction (ATPE), Raman-, Fourier-transformed infrared (FTIR), fluorescence- and ultraviolet/visible- (UV/Vis) spectroscopy have been successfully applied for titer and purity prediction. Raman spectroscopy was the most versatile and robust method in USP, ATPE, and precipitation and is therefore recommended as primary PAT. In later process stages, the combination of UV/Vis and fluorescence spectroscopy was able to overcome difficulties in titer and purity prediction induced by overlapping side component spectra. Based on the developed spectroscopic predictions, dynamic control of unit operations was demonstrated in sophisticated simulation studies. A PAT development workflow for holistic process development was proposed.
Downstream of pharmaceutical proteins, such as monoclonal antibodies, is mainly done by chromatography, where concentration determination of coeluting components presents a major problem. Inline concentration measurements (ICM) by Ultraviolet/Visible light (UV/VIS)-spectral data analysis provide a label-free and noninvasive approach to significantly speed up the analysis and process time. Here, two different approaches are presented. For a test mixture of three proteins, a fast and easily calibrated method based on the non-negative least-squares algorithm is shown, which reduces the calibration effort compared to a partial least-squares approach. The accuracy of ICM for analytical separations of three proteins on an ion exchange column is over 99%, compared to less than 85% for classical peak area evaluation. The power of the partial least squares algorithm (PLS) is shown by measuring the concentrations of Immunoglobulin G (IgG) monomer and dimer under a worst-case scenario of completely overlapping peaks. Here, the faster SIMPLS algorithm is used in comparison to the nonlinear iterative partial least squares (NIPALS) algorithm. Both approaches provide concentrations as well as purities in real-time, enabling live-pooling decisions based on product quality. This is one important step towards advanced process automation of chromatographic processes. Analysis time is less than 100 ms and only one program is used for all the necessary communications and calculations.
An experimental feasibility study on continuous bioprocessing in pilot-scale of 1 L/day cell supernatant, that is, about 150 g/year product (monoclonal antibody) based on CHO (Chinese hamster ovary) cells for model validation is performed for about six weeks including preparation, start-up, batch, and continuous steady-state operation for at least two weeks stable operation as well as final analysis of purity and yield. A mean product concentration of around 0.4 g/L at cell densities of 25 × 106 cells/mL was achieved. After perfusion cultivation with alternating tangential flow filtration (ATF), an aqueous two-phase extraction (ATPE) followed by ultra-/diafiltration (UF/DF) towards a final integrated counter-current chromatography (iCCC) purification with an ion exchange (IEX) and a hydrophobic interaction (HIC) column prior to lyophilization were successfully operated. In accordance to prior studies, continuous operation is stable and feasible. Efforts of broadly-qualified operation personal as well as the need for an appropriate measurement and process control strategy is shown evidently.
Preparative and process chromatography is a versatile unit operation for the capture, purification, and polishing of a broad variety of molecules, especially very similar and complex compounds such as sugars, isomers, enantiomers, diastereomers, plant extracts, and metal ions such as rare earth elements. Another steadily growing field of application is biochromatography, with a diversity of complex compounds such as peptides, proteins, mAbs, fragments, VLPs, and even mRNA vaccines. Aside from molecular diversity, separation mechanisms range from selective affinity ligands, hydrophobic interaction, ion exchange, and mixed modes. Biochromatography is utilized on a scale of a few kilograms to 100,000 tons annually at about 20 to 250 cm in column diameter. Hence, a versatile and fast tool is needed for process design as well as operation optimization and process control. Existing process modeling approaches have the obstacle of sophisticated laboratory scale experimental setups for model parameter determination and model validation. For a broader application in daily project work, the approach has to be faster and require less effort for non-chromatography experts. Through the extensive advances in the field of artificial intelligence, new methods have emerged to address this need. This paper proposes an artificial neural network-based approach which enables the identification of competitive Langmuir-isotherm parameters of arbitrary three-component mixtures on a previously specified column. This is realized by training an ANN with simulated chromatograms varying in isotherm parameters. In contrast to traditional parameter estimation techniques, the estimation time is reduced to milliseconds, and the need for expert or prior knowledge to obtain feasible estimates is reduced.
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