Experimental determination of single solute and competitive adsorption isotherms

Hydrophobic interaction chromatography in continuous flow-through mode for product-related variant removal

Product-related impurities are challenging to remove during monoclonal antibody (mAb) purification process due to molecular similarity. Frontal chromatography on hydrophobic interaction resins has demonstrated its capability to effectively remove such impurities. However, process improvements geared towards purity level comes as a trade-off with the yield loss. In this work, we present a hydrophobic interaction chromatography process using multicolumn continuous chromatography (MCC) concept and frontal analysis to remove a high prevalence product related impurity. This design uses a two-column continuous system where the two columns are directly connected during product chase step to capture product wash loss without any in-process adjustment. This polish MCC operation resulted in a 10 % increase in yield while maintaining 99 % purity, despite the presence of 20 % product-related impurities in the feed material. One challenge associated with polish MCC design is that the accumulation of the impurities renders a non-steady state recycling. To surmount this issue and ensure a robust process, a mechanistic model was developed and validated to predict multicomponent breakthrough. This model was capable to predict multiple cycle behavior and accounts for increased impurity concentration. Assisted by the model, the optimized operation parameters and conditions can be determined to account for variation in product load quality. The simulated results demonstrate an effective doubling of productivity compared to conventional batch chromatography.

Resin screening and process optimization for erythritol mother liquor chromatographic separation

In order to improve the utilization value of the erythritol mother liquor, the separation and purification of the erythritol mother liquor was selected in this study. The selected chromatographic separation programme for erythritol crystallizing mother liquor is as follows: Firstly, erythritol is resolved from mannitol and arabitol with DTF-01Ca (Suqing Group) resin and then mannitol is resolved from arabitol with 99Ca/320 (Dowex) resin. At the same time, the chromatographic conditions of the DTF-01Ca (Suqing Group) and 99Ca/320 (Dowex) resins were optimized, resulting in an optimal separation temperature and mobile phase flow rate of 70 °C, 10 ml/min. On this basis, a single-column chromatographic model was used to calculate the TD model parameter (N ) and the mass transfer coefficient (k m   ) of the separation of erythritol mother liquor by DTF-01Ca (Suqing Group) and 99Ca/320 (Dowex) resins. The adsorption isotherms, TD model parameter (N ) and the mass transfer coefficient (k m   ) provides data references for the design and operation of the simulated moving beds (SMB) separation system for the industrial-scale separation of erythritol crystallizing mother liquor.

A parameter estimation method for chromatographic separation process based on physics-informed neural network

Chromatographic separation processes are most often modeled in the form of partial differential equations (PDEs) to describe the complex adsorption equilibria and kinetics. However, identifying parameters in such a model requires substantial computational effort. In this work, a novel parameter estimation approach using a Physics-informed Neural Network (PINN) model is developed and tested for a binary component system. Numerical accuracy of our PINN model is confirmed by validating its simulations against those of the finite element method (FEM). Furthermore, model parameters in the kinetic model are estimated by the PINN model with sufficient accuracy from the observed data at the column outlet, where parameter fitting error can be reduced by up to 35.0 % from the conventional method. In a comparison with the conventional numerical method, our approach can reduce the computational time by up to 95 %. The robustness of the PINN model has also been demonstrated by estimating model parameters from noisy artificial experimental data.

Digital Twin for Continuous Production of Virus-like Particles toward Autonomous Operation

Lentiviral vector and virus-like particle (VLP) manufacturing have been published in fed-batch upstream and batch downstream modes before. Batch downstream and continuous upstream in perfusion mode were reported as well. This study exemplifies development and validation steps for a digital twin combining a physical-chemical-based mechanistic model for all unit operations with a process analytical technology strategy in order to show the efforts and benefits of autonomous operation approaches for manufacturing scale. As the general models are available from various other biologic manufacturing studies, the main step is model calibration for the human embryo kidney cell-based VLPs with experimental quantitative validation within the Quality-by-Design (QbD) approach, including risk assessment to define design and control space. For continuous operation in perfusion mode, the main challenge is the efficient separation of large particle manifolds for VLPs and cells, including cell debris, which is of similar size. Here, innovative tangential flow filtration operations are needed to avoid fast blocking with low mechanical stress pumps. A twofold increase of productivity was achieved using simulation case studies. This increase is similar to improvements previously described for other entities like plasmid DNAs, monoclonal antibodies (mAbs), and single-chain fragments of variability (scFv) fragments. The advantages of applying a digital twin for an advanced process control strategy have proven additional productivity gains of 20% at 99.9% reliability.

Screening, separation and identification of metal-chelating peptides for nutritional, cosmetics and pharmaceutical applications

Metal-chelating peptides, which form metal-peptide coordination complexes with various metal ions, can be used as biofunctional ingredients notably to enhance human health and prevent diseases. This review aims to discuss recent insights into food-derived metal-chelating peptides, the strategies set up for their discovery, their study, and identification. After understanding the overall properties of metal-chelating peptides, their production from food-derived protein sources and their potential applications will be discussed, particularly in nutritional, cosmetics and pharmaceutical fields. In addition, the review provides an overview of the last decades of progress in discovering food-derived metal-chelating peptides, addressing several screening, separation and identification methodologies. Furthermore, it emphasizes the methods used to assess peptide-metal interaction, allowing for better understanding of chemical and thermodynamic parameters associated with the formation of peptide-metal coordination complexes, as well as the specific amino acid residues that play important roles in the metal ion coordination.

Sequential purification of cannabidiol by two-dimensional liquid chromatography combined with modeling and simulation of elution profiles

Cannabidiol (CBD) has garnered significant attention for its neuroprotective properties, and research on its therapeutic effects has increased dramatically in recent years. However, the systematic purification of CBD through scalable processes has remained bottleneck due to the structural similarities of the cannabinoids. Although preparative chromatography is considered as a potential solution, it is usually time-consuming and expensive. Therefore, the development of scalable strategy via fast and accurate optimization approach is crucial. The present study aimed to develop a sequential process for the scalable purification of CBD through an eco-friendly ethanolic extraction using ultrasonic assisted extraction, decarboxylation of cannabidiolic acid optimized by response surface methodology, followed by the development of off-line two-dimensional semi-preparative chromatography, boosted with stacked injection overloading. In the first dimension, a column packed with macroporous resin allows to enrich the target substance and then, the behavior of resin column for scale-up procedure were predicted and optimized by developed mathematical model. A C18 column was used in the second dimension. The CBD purity and recovery obtained were 94.3 and 82.1 %, respectively. A robust and reliable method was employed for CBD enrichment/purification, which can be generalized to other bioactive compounds in complex matrices.

Unbiased Determination of Adsorption Isotherms by Inverse Method in Liquid Chromatography

The Inverse Method is a widely used technique for the determination of adsorption isotherms in liquid chromatography. In this method, isotherm is determined from the overloaded peak profile of the component by the iterative solution of the mass balance equation of liquid chromatography. Successful use of this method requires a prior assumption of equation of isotherm (Langmuir, BET etc.). In this work, we have developed an inverse method that gives results of similar accuracy to the frontal analysis without assuming the equation of the isotherm. The oversaturated peaks were calculated using a spline fitted to data points instead of the derivative of the isotherm. The distribution of the isotherm points were optimized for minimizing the difference between the measured and calculated overloaded peaks. The accuracy of the developed method was verified with synthetic benchmark peaks and by the determination of isotherm of buthyl-benzoate under real conditions. The results confirmed that the accuracy of the developed method is similar to that of Frontal Analysis.

Improved Apparatus for Dynamic Column Breakthrough Measurements Relevant to Direct Air Capture of CO2

Dynamic column breakthrough (DCB) measurements are valuable for characterizing the adsorption of gaseous species by solid sorbents and are typically used for high concentrations of adsorptives, often at elevated temperatures and pressures. However, adsorbents for the direct capture of carbon dioxide from natural air demand measurement capability at low partial pressures of CO2 at atmospherically relevant temperatures and pressures. We have developed a new apparatus focused on the measurement of DCB curves under typical tropospheric conditions. The new apparatus is described in detail and validated with breakthrough curve measurements. Adsorption capacities are reported at (233.1 to 323.1) K and (351 to 1078) hPa for low carbon dioxide concentrations on 13X zeolite samples on the order of a few hundred milligrams. Measurement uncertainties related to timing, flow, temperature, and concentrations are analyzed and the present results at 273 K, 298 K, and 323 K are compared with static measurements obtained with a manometric adsorption analyzer. In addition, experiments at a typical atmospheric CO2 concentration of 400 μL · L-1 have been performed.

Metal-Chelating Peptides Separation Using Immobilized Metal Ion Affinity Chromatography: Experimental Methodology and Simulation

Metal-Chelating Peptides (MCPs), obtained from protein hydrolysates, present various applications in the field of nutrition, pharmacy, cosmetic etc. The separation of MCPs from hydrolysates mixture is challenging, yet, techniques based on peptide-metal ion interactions such as Immobilized Metal Ion Affinity Chromatography (IMAC) seem to be efficient. However, separation processes are time consuming and expensive, therefore separation prediction using chromatography modelling and simulation should be necessary. Meanwhile, the obtention of sorption isotherm for chromatography modelling is a crucial step. Thus, Surface Plasmon Resonance (SPR), a biosensor method efficient to screen MCPs in hydrolysates and with similarities to IMAC might be a good option to acquire sorption isotherm. This review highlights IMAC experimental methodology to separate MCPs and how, IMAC chromatography can be modelled using transport dispersive model and input data obtained from SPR for peptides separation simulation.

The use of predictive models to develop chromatography-based purification processes

Chromatography is the workhorse of biopharmaceutical downstream processing because it can selectively enrich a target product while removing impurities from complex feed streams. This is achieved by exploiting differences in molecular properties, such as size, charge and hydrophobicity (alone or in different combinations). Accordingly, many parameters must be tested during process development in order to maximize product purity and recovery, including resin and ligand types, conductivity, pH, gradient profiles, and the sequence of separation operations. The number of possible experimental conditions quickly becomes unmanageable. Although the range of suitable conditions can be narrowed based on experience, the time and cost of the work remain high even when using high-throughput laboratory automation. In contrast, chromatography modeling using inexpensive, parallelized computer hardware can provide expert knowledge, predicting conditions that achieve high purity and efficient recovery. The prediction of suitable conditions in silico reduces the number of empirical tests required and provides in-depth process understanding, which is recommended by regulatory authorities. In this article, we discuss the benefits and specific challenges of chromatography modeling. We describe the experimental characterization of chromatography devices and settings prior to modeling, such as the determination of column porosity. We also consider the challenges that must be overcome when models are set up and calibrated, including the cross-validation and verification of data-driven and hybrid (combined data-driven and mechanistic) models. This review will therefore support researchers intending to establish a chromatography modeling workflow in their laboratory.

Optimization and troubleshooting of preparative liquid chromatography using statistical design of experiments: Four small-molecule case studies

Statistical design of experiments (DoE) is used to aid in the development and execution of preparative liquid chromatography (LC) for large-scale purification of active pharmaceutical ingredients (API) and pharmaceutical intermediates. Four purification case studies were undertaken. In case study 1, a normal phase preparative silica method is developed and modeled. After initial method screening, DoE results were used to set mobile phase composition, flowrate, and sample diluent. Of the three particle sizes studied (10 µm, 20 µm, 50 µm) only 10 µm silica resin was able to produce purified API at the yield (>96%) and productivity (> 1 kg/kg-resin/day) necessitated by the project. The second case study uses DoE studies to identify critical process parameters of column load, mobile phase solvent ratio and basic modifier level for a low-resolution, preparative, chiral separation. Trade-offs between purity, yield and productivity are quantified in a tight separation which made compromising on process outcomes a necessity. The third case study troubleshoots a loss of yield experienced during operation of a process-scale reverse-phase LC purification. DoE is used to identify a critical interaction between levels of acetonitrile and phosphoric acid in the mobile phase. An operating region which increased yield from around 85% to 97% was defined and implemented. The fourth case study was initially designed as a preparative chromatography purification of API. DoE was used to screen mobile phase solubility. These experiments uncovered conditions where API is soluble, and impurities are not. The solubility model in acetonitrile/water mixtures is further defined via a response surface DoE. The resulting targeted solvent mixture allows bulk purification via dissolution of API while three less-polar impurities remain in the solid phase and are removed by filtration. These four case studies demonstrate the efficiency of DoE and response surface modeling as tools for process development and optimization.

An Experimental and Modeling Combined Approach in Preparative Hydrophobic Interaction Chromatography

Chromatography is a technique widely used in the purification of biopharmaceuticals, and generally consists of several chromatographic steps. In this work, Hydrophobic Interaction Chromatography (HIC) is investigated as a polishing step for the purification of therapeutic proteins. Adsorption 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 experimental 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.

Digital Twin for HIV-Gag VLP Production in HEK293 Cells

The development and adoption of digital twins (DT) for Quality-by-Design (QbD)-based processes with flexible operating points within a proven acceptable range (PAR) and automation through Advanced Process Control (APC) with Process Analytical Technology (PAT) instead of conventional process execution based on offline analytics and inflexible process set points is one of the great challenges in modern biotechnology. Virus-like particles (VLPs) are part of a line of innovative drug substances (DS). VLPs, especially those based on human immunodeficiency virus (HIV), HIV-1 Gag VLPs, have very high potential as a versatile vaccination platform, allowing for pseudotyping with heterologous envelope proteins, e.g., the S protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As enveloped VLPs, optimal process control with minimal hold times is essential. This study demonstrates, for the first time, the use of a digital twin for the overall production process of HIV-1 Gag VLPs from cultivation, clarification, and purification to lyophilization. The accuracy of the digital twins is in the range of 0.8 to 1.4% in depth filtration (DF) and 4.6 to 5.2% in ultrafiltration/diafiltration (UFDF). The uncertainty due to variability in the model parameter determination is less than 4.5% (DF) and less than 3.8% (UFDF). In the DF, a prediction of the final filter capacity was demonstrated from as low as 5.8% (9mbar) of the final transmembrane pressure (TMP). The scale-up based on DT in chromatography shows optimization potential in productivity up to a factor of 2. The schedule based on DT and PAT for APC has been compared to conventional process control, and hold-time and process duration reductions by a factor of 2 have been achieved. This work lays the foundation for the short-term validation of the DT and PAT for APC in an automated S7 process environment and the conversion from batch to continuous production.

Digital Twins for scFv Production in Escherichia coli

Quality-by-Design (QbD) is demanded by regulatory authorities in biopharmaceutical production. Within the QbD frame advanced process control (APC), facilitated through process analytical technology (PAT) and digital twins (DT), plays an increasingly important role as it can help to assure to stay within the predefined proven acceptable range (PAR).This ensures high product quality, minimizes failure and is an important step towards a real-time-release testing (RTRT) that could help to accelerate time-to-market of drug substances, which is becoming even more important in light of dynamical pandemic situations. The approach is exemplified on scFv manufacturing in Escherichia coli. Simulation results from digital twins are compared to experimental data and found to be accurate and precise. Harvest is achieved by tangential flow filtration followed by product release through high pressure homogenization and subsequent clarification by tangential flow filtration. Digital twins of the membrane processes show that shear rate and transmembrane pressure are significant process parameters, which is in line with experimental data. Optimized settings were applied to 0.3 bar and a shear rate of 11,000 s−1. Productivity of chromatography steps were 5.3 g/L/d (Protein L) and 2167 g/L/d (CEX) and the final product concentration was 8 g/L. Based on digital twin results, an optimized process schedule was developed that decreased purification time to one working day, which is a factor-two reduction compared to the conventional process schedule. This work presents the basis for future studies on advanced process control and automation for biologics production in microbials in regulated industries.

Need for a Next Generation of Chromatography Models—Academic Demands for Thermodynamic Consistency and Industrial Requirements in Everyday Project Work

Process chromatography modelling for process development, design, and optimization as well as process control has been under development for decades. Still, the discussion of scientific potential and industrial applications needs is open to innovation. The discussion of next-generation modelling approaches starting from Langmuirian to steric mass action and multilayer or thermodynamic consistent real and ideal adsorption theory or colloidal particle adsorption approaches is continued.

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.

Continuous countercurrent chromatographic twin-column purification of oligonucleotides: the role of the displacement effect

Oligonucleotides (ONs) are breaking through in the biopharmaceutical industry as a promising class of biotherapeutics. The main success of these molecules is due to their peculiar way of acting in the cellular process, regulating the gene expression and hence influencing the protein synthesis at a pre-translational level. Although the Food and Drug Administration (FDA) already approved a few ON-based therapeutics, their production cost strongly limits large scale manufacturing: a situation that can be alleviated through process intensification. In this work, we address this problem by developing an efficient and continuous chromatographic purification process for ONs. In particular, we considered the chromatographic purification of a ON crude prepared by chemical synthesis using anion exchange resins. We demonstrate that in this system the competitive adsorption of the various species on the same sites of the resin leads to the displacement of the more weakly adsorbing species by the more strongly adsorbing ones. This phenomenon affects the behavior of the chromatographic units and it has been investigated in detail. Then, we developed a continuous countercurrent solvent gradient purification (MCSGP) process, which can significantly improve the productivity and buffer consumption compared to a classical single-column, batch chromatographic process. This article is protected by copyright. All rights reserved.

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.

Fast and Flexible mRNA Vaccine Manufacturing as a Solution to Pandemic Situations by Adopting Chemical Engineering Good Practice—Continuous Autonomous Operation in Stainless Steel Equipment Concepts

SARS-COVID-19 vaccine supply for the total worldwide population has a bottleneck in manufacturing capacity. Assessment of existing messenger ribonucleic acid (mRNA) vaccine processing shows a need for digital twins enabled by process analytical technology approaches in order to improve process transfer for manufacturing capacity multiplication, a reduction in out-of-specification batch failures, qualified personal training for faster validation and efficient operation, optimal utilization of scarce buffers and chemicals and speed-up of product release by continuous manufacturing. In this work, three manufacturing concepts for mRNA-based vaccines are evaluated: Batch, full-continuous and semi-continuous. Technical transfer from batch single-use to semi-continuous stainless-steel, i.e., plasmid deoxyribonucleic acid (pDNA) in batch and mRNA in continuous operation mode, is recommended, in order to gain: faster plant commissioning and start-up times of about 8–12 months and a rise in dose number by a factor of about 30 per year, with almost identical efforts in capital expenditures (CAPEX) and personnel resources, which are the dominant bottlenecks at the moment, at about 25% lower operating expenses (OPEX). Consumables are also reduceable by a factor of 6 as outcome of this study. Further optimization potential is seen at consequent digital twin and PAT (Process Analytical Technology) concept integration as key-enabling technologies towards autonomous operation including real-time release-testing.

Selectivity limits of and opportunities for ion pair chromatographic separation of oligonucleotides

Here it was investigated how oligonucleotide retention and selectivity factors are affected by electrostatic and non-electrostatic interactions in ion pair chromatography. A framework was derived describing how selectivity depends on the electrostatic potential generated by the ion-pair reagent concentration, co-solvent volume fraction, charge difference between the analytes, and temperature. Isocratic experiments verified that, in separation problems concerning oligonucleotides of different charges, selectivity increases with increasing surface potential and analyte charge difference and with decreasing co-solvent volume fraction and temperature. For analytes of the same charge, for example, diastereomers of phosphorothioated oligonucleotides, selectivity can be increased by decreasing the co-solvent volume fraction or the temperature and has only a minor dependency on the ion-pairing reagent concentration. An important observation is that oligonucleotide retention is driven predominantly by electrostatic interaction generated by the adsorption of the ion-pairing reagent. We therefore compared classical gradient elution in which the co-solvent volume fraction increases over time versus gradient elution with a constant co-solvent volume fraction but with decreasing ion-pair reagent concentration over time. Both modes decrease the electrostatic potential. Oligonucleotide selectivity was found to increase with decreasing ion-pairing reagent concentration. The two elution modes were finally applied to two different model antisense oligonucleotide separation problems, and it was shown that the ion-pair reagent gradient increases the selectivity of non-charge-based separation problems while maintaining charge-difference-based selectivity.

Digital Twin of mRNA-Based SARS-COVID-19 Vaccine Manufacturing towards Autonomous Operation for Improvements in Speed, Scale, Robustness, Flexibility and Real-Time Release Testing

Supplying SARS-COVID-19 vaccines in quantities to meet global demand has a bottleneck in manufacturing capacity. Assessment of existing mRNA (messenger ribonucleic acid) vaccine processing shows the need for digital twins enabled by process analytical technology approaches to improve process transfers for manufacturing capacity multiplication, reduction of out-of-specification batch failures, qualified personnel training for faster validation and efficient operation, optimal utilization of scarce buffers and chemicals, and faster product release. A digital twin of the total pDNA (plasmid deoxyribonucleic acid) to mRNA process is proposed. In addition, a first feasibility of multisensory process analytical technology (PAT) is shown. Process performance characteristics are derived as results and evaluated regarding manufacturing technology bottlenecks. Potential improvements could be pointed out such as dilution reduction in lysis, and potential reduction of necessary chromatography steps. 1 g pDNA may lead to about 30 g mRNA. This shifts the bottleneck towards the mRNA processing step, which points out co-transcriptional capping as a preferred option to reduce the number of purification steps. Purity demands are fulfilled by a combination of mixed-mode and reversed-phase chromatography as established unit operations on a higher industrial readiness level than e.g., precipitation and ethanol-chloroform extraction. As a final step, lyophilization was chosen for stability, storage and transportation logistics. Alternative process units like UF/DF (ultra-/diafiltration) integration would allow the adjustment of final concentration and buffer composition before lipid-nano particle (LNP) formulation. The complete digital twin is proposed for further validation in manufacturing scale and utilization in process optimization and manufacturing operations. The first PAT results should be followed by detailed investigation of different batches and processing steps in order to implement this strategy for process control and reliable, efficient operation.

A ternary eluent strategy to tune the peak shape of steviol glycosides in reversed-phase liquid chromatography

In this paper, an effective strategy of using acetonitrile-methanol-water as mobile phase was developed to achieve acceptable peak shape of steviol glycosides in reversed-phase liquid chromatography (RPLC). The change of elution profiles of rebaudioside A (RA) was systematically investigated. Two classical distributions, namely, tailing and fronting peaks resulting from injections of RA solution in range of 0.5-5 mg were both observed in a ternary eluent of acetonitrile-methanol-water (21:43:36, v/v). Next, a three-phase diagram of tailing factor (Tf) was illustrated, showing high dependence of elution profile of RA on the ternary composition. The peak shape of RA can be adjusted mainly based on the additive effect, that is, acetonitrile is more strongly adsorbed to the stationary phase than RA in the pure weak solvent (H2O). Therefore, with the increase of acetonitrile concentration in the ternary eluent, the RA band profiles went from being tailing to fronting shapes. At the same time, due to the large RA-RA interactions, there was anti-Langmuir adsorption isotherm in acetonitrile-water mobile phase, which is the reason for the fronting peaks of RA. It could be concluded that the way of using the ternary eluent of acetonitrile-methanol-water does control and tune the peak shape of steviol glycosides in RPLC separation.

PAT for Continuous Chromatography Integrated into Continuous Manufacturing of Biologics towards Autonomous Operation

This study proposes a reliable inline PAT concept for the simultaneous monitoring of different product components after chromatography. The feed for purification consisted of four main components, IgG monomer, dimer, and two lower molecular weight components of 4.4 kDa and 1 kDa molecular weight. The proposed measurement setup consists of a UV–VIS diode-array detector and a fluorescence detector. Applying this system, a R2 of 0.93 for the target component, a R2 of 0.67 for the dimer, a R2 of 0.91 for the first side component and a R2 of 0.93 for the second side component is achieved. Root mean square error for IgG monomer was 0.027 g/L, for dimer 0.0047 g/L, for side component 1 0.016 g/L and for the side component 2 0.014 g/L. The proposed measurement concept tracked component concentration reliably down to 0.05 g/L. Zero-point fluctuations were kept within a standard deviation of 0.018 g/L for samples with no IgG concentration but with side components present, allowing a reliable detection of the target component. The main reason inline concentration measurements have not been established yet, is the false-positive measurement of target components when side components are present. This problem was eliminated using the combination of fluorescence and UV–VIS data for the test system. The use of this measurement system is simulated for the test system, allowing an automatic fraction cut at 0.05 g/L. In this simulation a consistent yield of >99% was achieved. Process disturbances for processed feed volume, feed purity and feed IgG concentration can be compensated with this setup. Compared to a timed process control, yield can be increased by up to 12.5%, if unexpected process disturbances occur.

Nonlinear behavior in preparative liquid chromatography: A method-development case study for hydroxytyrosol purification

Preparative liquid chromatography has become an important purification method owing to its advantages of high separation efficiency, good reproducibility, and low solvent consumption. Because overloading in preparative liquid chromatography must be performed to increase the throughput in a cycle, nonlinear chromatographic behavior is observed. Therefore, it is crucial to carefully study nonlinear chromatography for the purification of a given product, which facilitates the efficient optimization of the purification parameters. In this work, a method for the development of a purification method using preparative liquid chromatography based on nonlinear chromatography is proposed. Hydroxytyrosol was selected as the subject for method demonstration. Using methanol and ethanol as organic modifiers, the optimum flow rate was determined on three commercial columns entitled C₈ TDE, C₁₈ ME, and C₁₈ TDE, respectively. The curves were fitted with the van Deemter equation, with thorough analysis of the A, B, and C terms. Adsorption isotherms were subsequently studied to explore the distribution of solutes between the stationary and mobile phases at equilibrium. C₁₈ TDE, 5 vol% ethanol-water, and 0.2 mL/min were selected as the optimal separation material, elution solvent, and flow rate, respectively. Purification of hydroxytyrosol was tentatively confirmed on a C₁₈ TDE column with 1.6% sample loading, 90.98% recovery, and 98.01% purity.

Digital Twins in Biomanufacturing

In recent years process modelling has become an established method which generates digital twins of manufacturing plant operation with the aid of numerically solved process models. This article discusses the benefits of establishing process modelling, in-house or by cooperation, in order to support the workflow from process development, piloting and engineering up to manufacturing. The examples are chosen from the variety of botanicals and biologics manufacturing thus proving the broad applicability from variable feedstock of natural plant extracts of secondary metabolites to fermentation of complex molecules like mAbs, fragments, proteins and peptides.Consistent models and methods to simulate whole processes are available. To determine the physical properties used as model parameters, efficient laboratory-scale experiments are implemented. These parameters are case specific since there is no database for complex molecules of biologics and botanicals in pharmaceutical industry, yet.Moreover, Quality-by-Design approaches, demanded by regulatory authorities, are integrated within those predictive modelling procedures. The models could be proven to be valid and predictive under regulatory aspects. Process modelling does earn its money from the first day of application. Process modelling is a key-enabling tool towards cost-efficient digitalization in chemical-pharmaceutical industries.

Enhanced nitrate adsorption by using cetyltrimethylammonium chloride pre-loaded activated carbon

This paper used cetyltrimethylammonium chloride (CTAC) pre-loaded activated carbon (AC) to research nitrate adsorption. Effects of various parameters such as AC types, AC dosage as well as initial pH were studied. The results indicated that the ACs modified by CTAC can get higher nitrate removal. Even pH is neutral and basic, an accepted removal about 2.5 mg/g can be observed. The more CTAC pre-loaded on the AC surface, the higher nitrate adsorption capacity can be obtained. pH is regarded as a key factor affecting interactions between adsorbent and adsorbate, and the results confirmed that the nitrate adsorption on modified AC decreases gradually with the growth of initial pH. Besides, the acidic pH condition is much favoured for adsorption while the results gained a nitrate adsorption about 4.28 mg/g at pH = 3 condition. Sorption mechanism of nitrate on CTAC modified AC was investigated through two kinetic modellings including pseudo-second-order and Weber and Morris intra-particle diffusion model. The results imply that the generalized kinetic models tally well with experimental data. Additionally, interference of co-existing anions is examined, and the results showed that higher co-anions concentration would bring a heavier depression of the nitrate uptake due to its competing for adsorption sites.

Chromatography bioseparation technologies and in-silico modelings for continuous production of biotherapeutics

The potential of continuous bioprocessing is hindered by the bottlenecks of chromatography processing, which continues to be executed in batch mode. Highlighting the critical drawbacks of batch chromatography, this review underscores the transition that the industry has made by implementing continuous upstream process without devising a working model for downstream chromatography operations. Even though multitude of process development initiatives have commenced, the review emphasizes the first principle models of chromatography on which these initiatives are built. Various models of continuous chromatography, which are essential, but not limited to multi-column systems, employed to congeal a unified process are reviewed. Advancements made by several mechanistic models and simulations to maximize productivity and performance are described, in an attempt to provide the integral tools. The modeling tools can be used for development of a strong model based control strategy and can be embedded into the continuous chromatography framework. The review addresses the limitations and challenges of the current modeling methods for development of robust mechanistic modeling and efficient unit operation platform in continuous chromatography.

Adsorption mechanism of triterpenoid saponins in reversed-phase liquid chromatography and hydrophilic interaction liquid chromatography: Mogroside V as test substance

In this paper, adsorption mechanism of triterpenoid saponins in reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) was proposed based on the study of the retention behavior of mogroside V as test substance. The change of peak shape of mogroside V and its influencing factors was first investigated. As the increase of sample loading, a tailing peak of mogroside V was observed in MeOHH₂O of both two modes. It was the fronting peak in ACNH₂O of HILIC while there was a transition from fronting peak to tailing peak in ACNH₂O of RPLC that was largely affected by column temperature and ACN concentration. The adsorption isotherm of mogroside V in ACNH₂O of RPLC was fitted by Moreau model, where a monolayer adsorption with large inter-molecular interaction was formed on the C18 surface. While in ACNH₂O of HILIC, the adsorption of mogroside V was in accordance with BET model, showing multilayer adsorption behavior. In MeOHH₂O of both HILIC and RPLC, there was always monolayer adsorption, which was fitted by Langmuir model. At last, by choosing the suitable chromatographic mode, controlling the key factors such as the solvent concentration and column temperature, and predicting the broadening trend of peak, three methods were screened out, namely, C18 column with 22% ACN (30 °C), Click XIon column with 90% MeOH or 70% ACN, to get mogroside V of purity greater than 98% from Siraitia grosvenorii extract. Among them, the RPLC method of 22% ACN that showed the highest loading sample per hour (1.92%) and the lowest solvent consumption emerged as the best approach.

Modified Dual-Site Langmuir Adsorption Equilibrium Models from A GCMC Molecular Simulation

In the modern industrial separation process, the pressure swing adsorption technology is widely used to separate and purify gases due to its low energy consumption, low cost, convenience, reliability, and environmental benignity. The basic elements of the design and application of the pressure swing adsorption process are adsorption isotherms at different temperatures for adsorbents. The dual-site Langmuir (DSL) adsorption equilibrium model is the mostly used model; however, this model is based on the assumption that the adsorption energy on the surface of an adsorbent is uniform and remains unchanged. Here, a grand canonical Monte Carlo (GCMC) molecular simulation was used to calculate the CO2 adsorption equilibrium on MIL-101 (Cr) at 298 K. MIL-101 (Cr) was chosen, as it has more a general pore structure with three different pores. The calculation results showed that the adsorption energies with different adsorption pressures fitted a normal distribution and the relationship of the average adsorption energies, E with pressures had a linear form described as: E = aP + c. With this relationship, the parameter b = k·exp(E/RT) in the DSL model was modified to b = k·exp((aP + c)/RT), and the modified DSL model (M-DSL) was used to correlate the adsorption equilibrium data on CO2-MIL-101 (Cr), C2H4-HHPAC, CH4-BPL, and CO2-H-Mordenite, showing better correlations than those of the DSL model. We also extended the parameter qm in the M-DSL model with the equation qm = k1 + k2T to adsorption equilibrium data for different temperatures. The obtained model (M-TDSL) was checked with the abovementioned adsorption equilibrium systems. The fitting results also indicated that the M-TDSL model could be used to improve the correlation of adsorption equilibrium data for different temperatures. The linear relationship between the average adsorption energy and adsorption pressure could be further tested in other adsorption equilibrium models to determine its universality.

Distinct and Quantitative Validation Method for Predictive Process Modelling in Preparative Chromatography of Synthetic and Bio-Based Feed Mixtures Following a Quality-by-Design (QbD) Approach

Process development, especially in regulated industries, where quality-by-design approaches have become a prerequisite, is cost intensive and time consuming. A main factor is the large number of experiments needed. Process modelling can reduce this number significantly by replacing experiments with simulations. However, this requires a validated model. In this paper, a process and model development workflow is presented, which focuses on implementing, parameterizing, and validating the model in four steps. The presented methods are laid out to gain, create, or generate the maximum information and process knowledge needed for successful process development. This includes design of experiments and statistical evaluations showing process robustness, sensitivity of target values to process parameters, and correlations between process and target values. Two case studies are presented. An ion exchange capture step for monoclonal antibodies focusing on high accuracy and low feed consumption; and one case study for small molecules focusing on rapid process development, emphasizing speed of parameter determination.

Experimental Evaluation of the Impact of Intrinsic Process Parameters on the Performance of a Continuous Chromatographic Polishing Unit (MCSGP)

The semicontinuous twin-column multicolumn countercurrent solvent gradient purification (MCSGP) process improves the trade-off between purity and yield encountered in traditional batch chromatography, while its complexity, in terms of hardware requirements and process design, is reduced in comparison to process variants using more columns. In this study, the MCSGP process is experimentally characterized, specifically with respect to its unique degrees of freedom, i.e., the four switching times, which alternate the columns between interconnected and batch states. By means of isolation of the main charge isoform of an antibody, it is shown that purity is determined by the selection of the product collection window with negligible influence from the recycle phases. In addition, the amount of weak and strong impurities can be specifically attributed to the start and end of the collection, respectively. Due to higher abundance of weakly adsorbing impurities, the start of product collection influences productivity and yield more than the other switching times. Furthermore, most of the encountered tendencies scale between different loadings. The found trends can be rationalized from the corresponding batch chromatogram and therefore used during process design to obtain desirable process performances without extensive trial-and-error experimentation or complete model development and calibration.

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 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?

Separation of tartronic and glyceric acids by simulated moving bed chromatography

The SMB unit developed by the Laboratory of Separation and Reaction Engineering (FlexSMB-LSRE^®) was used to perform tartronic acid (TTA) and glyceric acid (GCA) separation and to validate the mathematical model in order to determine the optimum operating parameters of an industrial unit. The purity of the raffinate and extract streams in the experiments performed were 80% and 100%, respectively. The TTA and GCA productivities were 79 and 115 kg per liter of adsorbent per day, respectively and only 0.50 cubic meters of desorbent were required per kilogram of products. Under the optimum operating conditions, which were determined through an extensive simulation study based on the mathematical model developed to predict the performance of a real SMB unit, it was possible to achieve a productivity of 86 kg of TTA and 176 kg of GCA per cubic meter of adsorbent per day (considering the typical commercial purity value of 97% for both compounds) with an eluent consumption of 0.30 cubic meters per kilogram of products.