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

Optimal design and operation of compact simulated moving bed processes for enantioseparations

Simulated moving bed (SMB) chromatography is attracting more and more attention since it is a powerful technique for complex separation tasks. Nowadays, more than 60% of preparative SMB units are installed in the pharmaceutical and in the food in- dustry [SDI, Preparative and Process Liquid Chromatography: The Future of Process Separations, International Strategic Directions, Los Angeles, USA, 2002. http://www. strategicdirections.com]. Chromatography is the method of choice in these ¯elds, be- cause often pharmaceuticals and ¯ne-chemicals have physico-chemical properties which di®er little from those of the by-products, and they may be thermally instable. In these cases, standard separation techniques as distillation and extraction are not applicable. The noteworthiness of preparative chromatography, particulary SMB process, as a sep- aration and puri¯cation process in the above mentioned industries has been increasing, due to its °exibility, energy e±ciency and higher product purity performance. Consequently, a new SMB paradigm is requested by the large number of potential small- scale applications of the SMB technology, which exploits the °exibility and versatility of the technology. In this new SMB paradigm, a number of possibilities for improving SMB performance through variation of parameters during a switching interval, are pushing the trend toward the use of units with smaller number of columns because less stationary phase is used and the setup is more economical. This is especially important for the phar- maceutical industry, where SMBs are seen as multipurpose units that can be applied to di®erent separations in all stages of the drug-development cycle. In order to reduce the experimental e®ort and accordingly the coast associated with the development of separation processes, simulation models are intensively used. One impor- tant aspect in this context refers to the determination of the adsorption isotherms in SMB chromatography, where separations are usually carried out under strongly nonlinear conditions in order to achieve higher productivities. The accurate determination of the competitive adsorption equilibrium of the enantiomeric species is thus of fundamental importance to allow computer-assisted optimization or process scale-up. Two major SMB operating problems are apparent at production scale: the assessment of product quality and the maintenance of long-term stable and controlled operation. Constraints regarding product purity, dictated by pharmaceutical and food regulatory organizations, have drastically increased the demand for product quality control. The strict imposed regulations are increasing the need for developing optically pure drugs.(...

An experimental and modeling combined approach in preparative Hydrophobic Interaction Chromatography

Chromatography is a technique widely used in the purification of biopharmaceuticals, and gener-ally consists of several chromatographic steps. In this work, Hydrophobic Interaction Chroma-tography (HIC) is investigated as a polishing step for the purification of therapeutic proteins. Ad-sorption mechanisms in hydrophobic interaction chromatography are still not completely clear and a limited amount of published data is available. In addition to new data on adsorption isotherms for some proteins (obtained both by high-throughput and frontal analysis method), and a comparison of different models proposed in the literature, two different approaches are compared in this work to investigate HIC. The predictive approach exploits an in-house code that simulates the behavior of the component in the column using the model parameters found from the fitting of experimental data. The estimation approach, on the other hand, exploits commercial software in which the model parameters are found by the fitting of a few experimental chromatograms. The two approaches are validated on some bind-elute runs: the predictive approach is very informative, but the experi-mental effort needed is high; the estimation approach is more effective, but the knowledge gained is lower. The second approach is also applied to an in-development industrial purification process and successfully resulted in predicting the behavior of the system, allowing for optimization with a reduction in the time and amount of sample needed

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

High-throughput and modeling technologies for process development in antibody purification

This cumulative thesis evaluates opportunities, to integrate high-throughput and model-based technologies in biopharmaceutical protein purification

Sorption of sulfadiazine on Brazilian soils

AbstractAntimicrobials, among them sulfonamides are widely used in veterinary medicine and can contaminate the environment. The degree to which antimicrobials adsorb onto soil particles varies widely, as does the mobility of these drugs. Sulfadiazine (SDZ) was used to study the adsorption–desorption in Brazilian soil–water systems, using batch equilibrium experiments. Sorption of SDZ was carried out using four types of soils. Adsorption and desorption data were well fitted with Freundlich isotherms in log form (r>0.999) and (0.984<r<0.999), respectively. An adsorption–desorption hysteresis phenomenon was apparent in all soils ranging from 0.517 to 0.827. The experimental results indicate that the Freundlich sorption coefficient (KF) values for SDZ ranged from 0.45 to 2.6μg1−1/n(cm3)1/ng−1

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

Multivariate parameter determination of multi-component isotherms for chromatography digital twins

Many fundamental decisions in the process design of a separation task are conducted in an early stage where, unfortunately, process simulation does not have the highest priority. Subsequently, during the setup of the digital twin, dedicated experiments are carried out in the design space that was established earlier. These experiments are most often too complicated to conduct directly. This paper addresses the idea of a combined approach. The early-stage buffer screening and optimization experiments were planned with the Design of Experiments, carried out and then analyzed statistically to extract not only the best buffer composition but also the crucial model parameters, in this case the isotherm dependency on the buffer composition. This allowed the digital twin to predict the best buffer composition, and if the model-predicted control was applied to keep the process at the optimal productivity at a predetermined purity. The methodology was tested with an industrial peptide purification step