Choice of Model for Estimation of Adsorption Isotherm Parameters in Gradient Elution Preparative Liquid Chromatography

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.

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.

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.

Impact of changes in physicochemical parameters of the mobile phase along the column on the retention time in gradient liquid chromatography. Part A - temperature gradient

Gradient chromatography has been widely applied in analytical and preparative chromatography since it provides better peak shapes and the ability to elute analytes in a shorter time frame. Apart from changes in the composition of a mobile phase also alteration of process temperature can be applied during separation procedures to improve efficiency. However, proper mathematical modeling of the gradient chromatography and further correct prediction of solutes' retention behavior have become a serious challenge as it involves the need to develop computational procedures that accurately account for the time and spatial gradients of crucial parameters. In this work, a computational procedure including the equilibrium-dispersive two-dimensional mass transfer model, the two-dimensional (2D) heat transfer model together with Darcy's law and the continuity equation have been proposed. Additionally, the calculation procedure was simplified by replacing the 2D model with the one-dimensional (1D) mass transfer model in order to speed up the computations. Both proposed solutions were validated employing external experimental data of temperature gradient HPLC [1] as well as with predictions based on the linear elution strength (LES) model available therein. The proposed procedures made it possible to efficiently predict the concentration profiles with average relative errors of calculated retention times not exceeding 3.22%. Moreover, the effect of the axial dispersion coefficient determination method on the obtained peak shapes was examined involving the Gunn, the Wen-Fan, and the Chung-Wen correlations, indicating that the latter produces the most accurate results. Finally, the proposed mathematical procedures were tested under UHPLC conditions, and due to a significant difference in retention times found the 2D model is strongly advised.

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.

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.

Impact of stationary-phase pore size on chromatographic performance using oligonucleotide separation as a model

A combined experimental and theoretical study was performed to understand how the pore size of packing materials with pores 60-300 Å in size affects the separation of 5-50-mer oligonucleotides. For this purpose, we developed a model in which the solutes were described as thin rods to estimate the accessible surface area of the solute as a function of the pore size and solute size. First, an analytical investigation was conducted in which we found that the selectivity increased by a factor of 2.5 when separating 5- and 15-mer oligonucleotides using packing with 300 Å rather than 100 Å pores. We complemented the analytical investigation by theoretically demonstrating how the selectivity is dependent on the column's accessible surface area as a function of solute size. In the preparative investigation, we determined adsorption isotherms for oligonucleotides using the inverse method for separations of a 9- and a 10-mer. We found that preparative columns with a 60 Å-pore-size packing material provided a 10% increase in productivity as compared with a 300 Å packing material, although the surface area of the 60 Å packing is as much as five time larger.

Modeling the nonlinear behavior of a bioactive peptide in reversed-phase gradient elution chromatography

The thermodynamic behavior of octreotide, a cyclic octapeptide with important pharmaceutical functions, has been simulated under reversed-phase gradient elution conditions. To this end, adsorption behavior was firstly investigated in isocratic conditions, under a variety of water/acetonitrile + 0.02% (v/v) trifluoroacetic acid (TFA) mixtures as mobile phase by using a Langmuir isotherm. Organic modifier was varied in the range between 23 and 28% (v/v). Adsorption isotherms were determined by means of the so-called Inverse Method (IM) with a minimum amount of peptide. The linear solvent strength (LSS) model was used to find the correlation between isotherm parameters and mobile phase composition. This study contributes to enlarge our knowledge on the chromatographic behavior under nonlinear gradient conditions of peptides. In particular, it focuses on a cyclic octapeptide.

Estimation of Nonlinear Adsorption Isotherms in Gradient Elution RP-LC of Peptides in the Presence of an Adsorbing Additive

ABSTRACT

In electrostatic repulsive interaction chromatography, using a charged surface hybrid sorbent carrying positive charges can improve the peak shape of peptides in reversed-phase liquid chromatography (RP-LC), especially in overloaded conditions, compared with standard C18 sorbents. However, the positive surface charges can interact with anionic additives commonly used in peptide separations, e.g., trifluoroacetic acid (TFA), complicating adsorption isotherm estimation. We investigated how the competition for available adsorption sites between TFA and two peptides influenced the adsorption isotherm in gradient elution. A model accounting for the competition with TFA was compared with a model neglecting TFA adsorption. We found that the two models predicted elution profiles with the same accuracy. We also found that the adsorption isotherms were extremely similar in shape, leading to the conclusion that neglecting the competition with TFA is a valid approximation enabling faster and more robust adsorption isotherm estimation for the studied type of sorbent.

GRAPHICAL ABSTRACT

The importance of ion-pairing in peptide purification by reversed-phase liquid chromatography

The adsorption mechanism for three peptides was studied under overloaded conditions through adsorption isotherm measurements in the presence of an ion-pairing reagent, trifluoroacetic acid (TFA), on an end-capped C₁₈-bonded stationary phase. The overall aim of the study was to obtain a better understanding of how the acetonitrile and the TFA fractions in the eluent affected the overloaded elution profiles and the selectivity between peptides using mechanistic modelling and multivariate design of experiments. When studying the effect of TFA, direct evidence for ion pair formation between a peptide and TFA in acetonitrile-water solutions was provided by fluorine-proton nuclear Overhauser NMR enhancement experiments and the adsorption of TFA on the stationary phase was measured by frontal analysis. The adsorption isotherms for each peptide were then determined by the inverse method at eight TFA concentrations ranging from 2.6mM to 37.3mM (0.02-0.29vol-%) in isocratic elution. The equilibrium between the peptide ion and the peptide-TFA complex was modelled by coupling the mass-balance to reaction kinetics and determining separate adsorption isotherms for the two species. We found that a Langmuir isotherm described the elution profile of peptide-TFA complex well while the peptide ion was described by a bi-Langmuir adsorption isotherm since it exhibited strong secondary interactions. The elution profiles had an unfavorable shape at low TFA concentrations consisting of a spike in their front and a long tailing rear due to the secondary interactions for the peptide ion having very low saturation capacity. The acetonitrile dependence on the adsorption isotherms was studied by determination of adsorption isotherms directly from elution profiles obtained in gradient elution which enabled a broad acetonitrile interval to be studied. Here, it was found that the column saturation capacity was quickly reached at very low acetonitrile fractions and that there were significant variations in adsorption with the molecular weight. Finally, practical implications for method development are discussed based on an experimental design where gradient slope and TFA concentrations are used as factors.