Artificial Neural Network for Fast and Versatile Model Parameter Adjustment utilizin PAT signals of Chromatography Processes for Process Control under Production Conditions

Preparative chromatography is a well-established operation in the chemical and biotechnology manufacturing. Chromatography achieves high separation performances but often has to deal with the yield versus purity trade-off as the optimization criterium regarding through-put. The initial trade-off is often disturbed by the well-known phenomenon of chromatogram shifts over process lifetime and has to be corrected by operators via adjustment of peak fraction cutting. Nevertheless, with regards to autonomous operation and batch to continuous processing modes, any advanced process control strategy is needed to identify and correct shifts from the optimal operation point automatically. Previous studies already presented solutions for batch-to-batch variance and process control options with the aid of rigorous physico-chemical process model-ling. These models can be implemented as distinct digital twins as well as statistical process op-eration data analysers. In order to utilize such models for advanced process control, the model parameters have to be up dated with aid of inline PAT data to describe the actual operational status. Also including any occurring operational change phenomenon and its relation to their physico-chemical root cause. Typical phenomena are fluid dynamic changes due to packing breakage, channelling or compression as well as mass transfer and phase equilibrium related separation performance decrease due to adsorbent ageing or feed and buffer composition changes. In order to track these changes an Artificial Neural Network (ANN) is trained in this work. The ANN training is in this first step based on the simulation results of a distinct and pre-viously experimentally validated process model. The model is implemented in the open source tool CasADi for python. This allows the implementation of interfaces to e.g. process control systems with relatively low effort. Therefore, PAT signals can easily incorporated for the suffi-cient adjustment of the process model for appropriate process control. Further steps would be the implementation of optimization routines based on the PAT and ANN predictions to derive optimal operation points with the model

Fast and versatile chromatography process design and operation optimization with the aid of artificial intelligence

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

Artificial neural network for fast and versatile model parameter adjustment utilizing PAT signals of chromatography processes for process control under production conditions

Preparative chromatography is a well-established operation in chemical and biotechnology manufacturing. Chromatography achieves high separation performances, but often has to deal with the yield versus purity trade-off as the optimization criterium regarding through-put. The initial trade-off is often disturbed by the well-known phenomenon of chromatogram shifts over process lifetime, and has to be corrected by operators via adjustment of peak fraction cutting. Nevertheless, with regard to autonomous operation and batch to continuous processing modes, an advanced process control strategy is needed to identify and correct shifts from the optimal operation point automatically. Previous studies have already presented solutions for batch-to-batch variance and process control options with the aid of rigorous physico-chemical process modeling. These models can be implemented as distinct digital twins as well as statistical process operation data analyzers. In order to utilize such models for advanced process control (APC), the model parameters have to be updated with the aid of inline Process Analytical Technology (PAT) data to describe the actual operational status. This updating process also includes any operational change phenomena that occur, and its relation to their physico-chemical root cause. Typical phenomena are fluid dynamic changes due to packing breakage, channelling or compression as well as mass transfer and phase equilibrium-related separation performance decrease due to adsorbent aging or feed and buffer composition changes. In order to track these changes, an Artificial Neural Network (ANN) is trained in this work. The ANN training is in this first step, based on the simulation results of a distinct and previously experimentally validated process model. The model is implemented in the open source tool CasADi for Python. This allows the implementation of interfaces to process control systems, among others, with relatively low effort. Therefore, PAT signals can easily be incorporated for sufficient adjustment of the process model for appropriate process control. Further steps would be the implementation of optimization routines based on PAT and ANN predictions to derive optimal operation points with the model

Process analytical technology as key-enabler for digital twins in continuous biomanufacturing

Over the last few years rapid progress has been made in adopting well-known process modeling techniques from chemicals to biologics manufacturing. The main challenge has been analytical methods as engineers need quantitative data for their workflow. Industrialization 4.0, Internet of Things, artificial intelligence and machine learning activities up to big data analysis have taken their share in solving fundamental problems like component- or at least group-specific evaluation of spectroscopic data. Besides, concerning inline analytics methods included in process analytical technology concepts the key technology has been the generation of decisive validated digital twins based on process models. This review aims to summarize the methodology to achieve a holistic understanding of process models, control and optimization by means of digital twins using the example of recent work published in this field

Process analytical approach towards quality controlled process automation for the downstream of protein mixtures by inline concentration measurements based on Ultraviolet/Visible Light (UV/VIS) spectral analysis

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

Towards Autonomous Operation by Advanced Process Control—Process Analytical Technology for Continuous Biologics Antibody Manufacturing

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

Accelerating Biologics Manufacturing by Modeling or: Is Approval under the QbD and PAT Approaches Demanded by Authorities Acceptable without a Digital-Twin?

Innovative biologics, including cell therapeutics, virus-like particles, exosomes, recombinant proteins, and peptides, seem likely to substitute monoclonal antibodies as the main therapeutic entities in manufacturing over the next decades. This molecular variety causes a growing 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 decisions toward batch or continuous operation of dedicated modular plant concepts. This indicates gaps in process comprehension, when operation windows need to be run at the edges of optimization. In this editorial, the authors review and assess potential methods and begin discussing possible solutions throughout the workflow, from process development through piloting to manufacturing operation from their point of view and experience. Especially, the state-of-the-art for modeling in red biotechnology is assessed, clarifying differences and applications of statistical, rigorous physical-chemical based models as well as cost modeling. “Digital-twins„ are described and efforts vs. benefits for new applications exemplified, including the regulation-demanded QbD (quality by design) and PAT (process analytical technology) approaches towards digitalization or industry 4.0 based on advanced process control strategies. Finally, an analysis of the obstacles and possible solutions for any successful and efficient industrialization of innovative methods from process development, through piloting to manufacturing, results in some recommendations. A central question therefore requires attention: Considering that QbD and PAT have been required by authorities since 2004, can any biologic manufacturing process be approved by the regulatory agencies without being modeled by a “digital-twin„ as part of the filing documentation