An adaptive soft-sensor for advanced real-time monitoring of an antibody-drug conjugation reaction.

In the production of antibody-drug conjugates (ADCs), the conjugation reaction is a central step defining the final product composition and, hence, directly affecting product safety and efficacy. To enable real-time monitoring, spectroscopic sensors in combination with multivariate regression models have gained popularity in recent years. The extended Kalman filter (EKF) can be used as so-called soft-sensor to fuse sensor predictions with long-horizon forecasts by process models. This enables the dynamic update of the current state and provides increased robustness against experimental noise or model errors. Due to the uncertainty associated with sensor and process models in biopharmaceutical applications, the deployment of such soft-sensors is challenging. In this study, we demonstrate the combination of an uncertainty-aware sensor model with a kinetic reaction model using an EKF to monitor a site-directed ADC conjugation reaction. As the sensor model, a Gaussian process regression model is presented to realize a time-variant determination of the sensor uncertainty. The EKF fuses the time-discrete predictions of the amount of conjugated drug from the sensor model with the time-continuous predictions from the kinetic model. While the ADC species are not distinguishable by on-line recorded UV/Vis spectra, the developed soft-sensor is able to dynamically update all relevant reaction species. It could be shown that the use of time-variant process and sensor noise computation approaches improved the performance of the EKF and achieved a reduction of the prediction error of up to 23% compared with the kinetic model. The developed framework proved to enhance robustness against noisy sensor measurements or wrong model initialization and was successfully transferred from batch to fed-batch mode. In future, this framework could be implemented for model-based process control and be adopted for other ADC conjugation reaction types.

A multiscale modeling method for therapeutic antibodies in ion exchange chromatography.

The development of biopharmaceutical downstream processes relies on exhaustive experimental studies. The root cause is the poorly understood relationship between the protein structure of monoclonal antibodies (mAbs) and their macroscopic process behavior. Especially the development of preparative chromatography processes is challenged by the increasing structural complexity of novel antibody formats and accelerated development timelines. This study introduces a multiscale in silico model consisting of homology modeling, quantitative structure-property relationships (QSPR), and mechanistic chromatography modeling leading from the amino acid sequence of a mAb to the digital representation of its cation exchange chromatography (CEX) process. The model leverages the mAbs' structural characteristics and experimental data of a diverse set of 21 therapeutic antibodies to predict elution profiles of two mAbs that were removed from the training data set. QSPR modeling identified mAb-specific protein descriptors relevant for the prediction of the thermodynamic equilibrium and the stoichiometric coefficient of the adsorption reaction. The consideration of two discrete conformational states of IgG4 mAbs enabled prediction of split-peak elution profiles. Starting from the sequence, the presented multiscale model allows in silico development of chromatography processes before protein material is available for experimental studies.

Monitoring of ultra- and diafiltration processes by Kalman-filtered Raman measurements.

Monitoring the protein concentration and buffer composition during the Ultrafiltration/Diafiltration (UF/DF) step enables the further automation of biopharmaceutical production and supports Real-time Release Testing (RTRT). Previously, in-line Ultraviolet (UV) and Infrared (IR) measurements have been used to successfully monitor the protein concentration over a large range. The progress of the diafiltration step has been monitored with density measurements and Infrared Spectroscopy (IR). Raman spectroscopy is capable of measuring both the protein and excipient concentration while being more robust and suitable for production measurements in comparison to Infrared Spectroscopy (IR). Regardless of the spectroscopic sensor used, the low concentration of excipients poses a challenge for the sensors. By combining sensor measurements with a semi-mechanistic model through an Extended Kalman Filter (EKF), the sensitivity to determine the progress of the diafiltration can be improved. In this study, Raman measurements are combined with an EKF for three case studies. The advantages of Kalman-filtered Raman measurements for excipient monitoring are shown in comparison to density measurements. Furthermore, Raman measurements showed a higher measurement speed in comparison to Variable Pathlength (VP) UV measurement at the trade-off of a slightly worse prediction accuracy for the protein concentration. However, the Raman-based protein concentration measurements relied mostly on an increase in the background signal during the process and not on proteinaceous features, which could pose a challenge due to the potential influence of batch variability on the background signal. Overall, the combination of Raman spectroscopy and EKF is a promising tool for monitoring the UF/DF step and enables process automation by using adaptive process control.

Process monitoring framework for cross-flow diafiltration-based virus-like particle disassembly: Tracing product properties and filtration performance.

Virus-like particles (VLPs) are an emerging biopharmaceutical modality with great potential as a platform technology. VLPs can be applied as gene therapy vectors and prophylactic or therapeutic vaccines. For non-enveloped VLPs, recombinant production of the protein subunits leads to intracellular self-assembly. The subsequent purification process includes VLP dis- and reassembly which aim at removing encapsulated impurities and improving particle properties. Filtration-based separation and processing has proven successful for VLPs but requires large product quantities and laborious experiments in early development stages. Both challenges can be tackled by implementation of process analytical technology (PAT) to efficiently obtain extensive process information. In this study, an existing PAT setup was extended to comprehensively monitor the diafiltration-based disassembly of hepatitis B core antigen (HBcAg) VLPs. Process-related signals were monitored in-line, while product-related signals, such as ultraviolet light (UV) spectra as well as static and dynamic light scattering (SLS and DLS), were monitored on-line. The applicability of the sensors for disassembly monitoring was evaluated under varying processing conditions. HBcAg VLP subunit concentrations were accurately predicted based on UV data using ordinary and partial least squares regression models (Q2 from 0.909 to 0.976). DLS data were used for aggregation monitoring while the SLS intensity qualitatively reflected the disassembly progress.

Process development exploiting competitive adsorption-based displacement effects in monoclonal antibody aggregate removal-A new high-throughput screening procedure for membrane chromatography.

High-throughput screening (HTS) approaches are commonly used to accelerate downstream process development. Although most HTS approaches use batch isothermal data (KP screen) or bind and elute mode as screening procedure, different or new process designs are rarely investigated. In this paper, a mechanistic model case study for the separation of two different two-component solutions was conducted and confirmed prior evidence. With these outcomes, a novel HTS screening procedure was developed including the determination of competitive adsorption-based displacement effects and key parameter identification. The screening procedure employing an overload bind and elute (OBE) mode is presented in a case study dealing with IgG aggregate removal in a typical monoclonal antibody purification step, applying a Sartobind® S membrane adsorber (MA). Based on a MA scale down device, the OBE mode allows the determination of classical process parameters and dynamic effects, such as displacement effects. Competitive adsorption-based displacement effects are visualized by introducing a displacement identifier leading to a displacement process map. Based on this map, the approach is transferred to and confirmed by the OBE recycle experiments with 4.6 and 8.2 ml benchtop scsale devices resulting in 45% reduced IgG monomer and 88% increased higher molecular weight species binding capacities.

Raman spectroscopy as a process analytical technology to investigate biopharmaceutical freeze concentration processes.

Freezing processes are a well-established unit operation in the biopharmaceutical industry to increase the shelf-life of protein-based drugs. While freezing reduces degradation reaction rates, it may also exert stresses such as freeze concentration. Macroscopic freeze concentration in large-scale freezing processes has been described thoroughly by examination of frozen bulk material, but the transient process leading to such freeze concentration profiles has not been monitored yet for biopharmaceutical solutions. In this study, Raman spectroscopy as a process analytical technology is demonstrated for model formulations containing monoclonal antibodies (mAbs) or bovine serum albumin (BSA) in varying concentrations of sucrose and buffer salts. Therefore, a Raman probe was immersed into a bulk volume at different heights, monitoring the freeze concentration in the liquid phase during the freezing processes. Partial least square regression models were used to quantitatively discriminate between the protein and excipients simultaneously. The freeze concentration profiles were dependend on freezing temperature and formulation with freeze concentrations up to 2.4-fold. Convection currents at the bottom of the freezing container were observed with a maximum height of 1 mm. Furthermore, freeze concentration was correlated with the sucrose concentration in a formulation. Analysis of the freeze concentration slope indicated diffusion from the bottom to the top of the container. In summary, Raman spectroscopy is a valuable tool for process validation of freeze concentration simulations and to overcome scale-dependent challenges.

Comparison of UV- and Raman-based monitoring of the Protein A load phase and evaluation of data fusion by PLS models and CNNs.

A promising application of Process Analytical Technology to the downstream process of monoclonal antibodies (mAbs) is the monitoring of the Protein A load phase as its control promises economic benefits. Different spectroscopic techniques have been evaluated in literature with regard to the ability to quantify the mAb concentration in the column effluent. Raman and Ultraviolet (UV) spectroscopy are among the most promising techniques. In this study, both were investigated in an in-line setup and directly compared. The data of each sensor were analyzed independently with Partial-Least-Squares (PLS) models and Convolutional Neural Networks (CNNs) for regression. Furthermore, data fusion strategies were investigated by combining both sensors in hierarchical PLS models or in CNNs. Among the tested options, UV spectroscopy alone allowed for the most precise and accurate prediction of the mAb concentration. A Root Mean Square Error of Prediction (RMSEP) of 0.013 g L-1 was reached with the UV-based PLS model. The Raman-based PLS model reached an RMSEP of 0.232 g L-1 . The different data fusion techniques did not improve the prediction accuracy above the prediction accuracy of the UV-based PLS model. Data fusion by PLS models seems meritless when combining a very accurate sensor with a less accurate signal. Furthermore, the application of CNNs for UV and Raman spectra did not yield significant improvements in the prediction quality. For the presented application, linear regression techniques seem to be better suited compared with advanced nonlinear regression techniques, like, CNNs. In summary, the results support the application of UV spectroscopy and PLS modeling for future research and development activities aiming to implement spectroscopic real-time monitoring of the Protein A load phase.

Impact of freeze-thaw processes on monoclonal antibody platform process development.

Freezing of cell culture supernatant (CCS) is a standard procedure in process development of monoclonal antibody (mAb) platform processes as up- and downstream development are usually separated. In the manufacturing process of mAb, however, freezing is avoided, which poses the question of comparability and transferability from process development to manufacturing. In this case study, mAb CCS from Chinese hamster ovary (CHO) cells is frozen and thawed in a novel active freezing device and subsequently captured by protein A chromatography. Critical quality attributes such as host cell protein (HCP) concentration and soluble mAb dimer shares have been monitored throughout the case study. Furthermore, cryo-concentration of individual proteins was investigated. The main factors that drive cryo-concentration are diffusion and natural convection. Natural convection in freezing processes was found to increase at warmer freezing temperatures and thus slower freezing, leading to higher concentration gradients from top to bottom of a freezing chamber. The freeze concentration was dependent on protein size and correlated to diffusivity, where smaller proteins are exposed to higher cryo-concentration. Our results suggest that as a result of freezing processes, large particles based on mAb and specific host cell proteins (HCPs) expressing a certain affinity to mAbs are formed that have to be removed before purification. This leads to a significant improvement in HCP reduction by the protein A step, when compared with reference samples, where twice as much HCP remained in the eluate. Furthermore, HCP and mAb dimer concentrations in protein A eluate were dependent on the freezing temperature. As a conclusion, CCS should be frozen as rapidly as possible during process development to minimize issues of transferability from process development to manufacturing.

A multisensor approach for improved protein A load phase monitoring by conductivity-based background subtraction of UV spectra.

Real-time monitoring and control of protein A capture steps by process analytical technologies (PATs) promises significant economic benefits due to the improved usage of the column's binding capacity, by eliminating time-consuming off-line analytics and costly resin lifetime studies, and enabling continuous production. The PAT method proposed in this study relies on ultraviolet (UV) spectroscopy with a dynamic background subtraction based on the leveling out of the conductivity signal. This point in time can be used to collect a reference spectrum for removing the majority of spectral contributions by process-related contaminants. The removal of the background spectrum facilitates chemometric model building and model accuracy. To demonstrate the benefits of this method, five different feedstocks from our industry partner were used to mix the load material for a case study. To our knowledge, such a large design space, which covers possible variations in upstream condition besides the product concentration, has not been disclosed yet. By applying the conductivity-based background subtraction, the root mean square error of prediction (RMSEP) of the partial least squares (PLS) model improved from 0.2080 to 0.0131 g L-1 . Finally, the potential of the background subtraction method was further evaluated for single wavelength-based predictions to facilitate implementation in production processes. An RMSEP of 0.0890 g L-1 with univariate linear regression was achieved, showing that by subtraction of the background better prediction accuracy is achieved then without subtraction and a PLS model. In summary, the developed background subtraction method is versatile, enables accurate prediction results, and is easily implemented into existing chromatography setups with typically already integrated sensors.

Process development for cross-flow diafiltration-based VLP disassembly: A novel high-throughput screening approach.

Virus-like particles (VLPs) are particulate structures, which are applied as vaccines or delivery vehicles. VLPs assemble from subunits, named capsomeres, composed of recombinantly expressed viral structural proteins. During downstream processing, in vivo-assembled VLPs are typically dis- and reassembled to remove encapsulated impurities and to improve particle morphology. Disassembly is achieved in a high-pH solution and by the addition of a denaturant or reducing agent. The optimal disassembly conditions depend on the VLP amino acid sequence and structure, thus requiring material-consuming disassembly experiments. To this end, we developed a low-volume and high-resolution disassembly screening that provides time-resolved insight into the VLP disassembly progress. In this study, two variants of C-terminally truncated hepatitis B core antigen were investigated showing different disassembly behaviors. For both VLPs, the best capsomere yield was achieved at moderately high urea concentration and pH. Nonetheless, their disassembly behaviors differed particularly with respect to disassembly rate and aggregation. Based on the high-throughput screening results, a diafiltration-based disassembly process step was developed. Compared with mixing-based disassembly, it resulted in higher yields of up to 0.84 and allowed for integrated purification. This process step was embedded in a filtration-based process sequence of disassembly, capsomere separation, and reassembly, considerably reducing high-molecular-weight species.

Modeling the impact of amino acid substitution in a monoclonal antibody on cation exchange chromatography.

A vital part of biopharmaceutical research is decision making around which lead candidate should be progressed in early-phase development. When multiple antibody candidates show similar biological activity, developability aspects are taken into account to ease the challenges of manufacturing the potential drug candidate. While current strategies for developability assessment mainly focus on drug product stability, only limited information is available on how antibody candidates with minimal differences in their primary structure behave during downstream processing. With increasing time-to-market pressure and an abundance of monoclonal antibodies (mAbs) in development pipelines, developability assessments should also consider the ability of mAbs to integrate into the downstream platform. This study investigates the influence of amino acid substitutions in the complementarity-determining region (CDR) of a full-length IgG1 mAb on the elution behavior in preparative cation exchange chromatography. Single amino acid substitutions within the investigated mAb resulted in an additional positive charge in the light chain (L) and heavy chain (H) CDR, respectively. The mAb variants showed an increased retention volume in linear gradient elution compared with the wild-type antibody. Furthermore, the substitution of tryptophan with lysine in the H-CDR3 increased charge heterogeneity of the product. A multiscale in silico analysis, consisting of homology modeling, protein surface analysis, and mechanistic chromatography modeling increased understanding of the adsorption mechanism. The results reveal the potential effects of lead optimization during antibody drug discovery on downstream processing.

Influence of image analysis strategy, cooling rate, and sample volume on apparent protein cloud-point temperature determination.

The protein cloud-point temperature (TCloud) is a known representative of protein-protein interaction strength and provides valuable information during the development and characterization of protein-based products, such as biopharmaceutics. A high-throughput low volume TCloud detection method was introduced in preceding work, where it was concluded that the extracted value is an apparent TCloud (TCloud,app). As an understanding of the apparent nature is imperative to facilitate inter-study data comparability, the current work was performed to systematically evaluate the influence of 3 image analysis strategies and 2 experimental parameters (sample volume and cooling rate) on TCloud,app detection of lysozyme. Different image analysis strategies showed that TCloud,app is detectable by means of total pixel intensity difference and the total number of white pixels, but the latter is also able to extract the ice nucleation temperature. Experimental parameter variation showed a TCloud,app depression for increasing cooling rates (0.1-0.5 °C/min), and larger sample volumes (5-24 µL). Exploratory thermographic data indicated this resulted from a temperature discrepancy between the measured temperature by the cryogenic device and the actual sample temperature. Literature validation confirmed that the discrepancy does not affect the relative inter-study comparability of the samples, regardless of the image analysis strategy or experimental parameters. Additionally, high measurement precision was demonstrated, as TCloud,app changes were detectable down to a sample volume of only 5 µL and for 0.1 °C/min cooling rate increments. This work explains the apparent nature of the TCloud detection method, showcases its detection precision, and broadens the applicability of the experimental setup.

A critical review of recent trends, and a future perspective of optical spectroscopy as PAT in biopharmaceutical downstream processing.

As competition in the biopharmaceutical market gets keener due to the market entry of biosimilars, process analytical technologies (PATs) play an important role for process automation and cost reduction. This article will give a general overview and address the recent innovations and applications of spectroscopic methods as PAT tools in the downstream processing of biologics. As data analysis strategies are a crucial part of PAT, the review discusses frequently used data analysis techniques and addresses data fusion methodologies as the combination of several sensors is moving forward in the field. The last chapter will give an outlook on the application of spectroscopic methods in combination with chemometrics and model predictive control (MPC) for downstream processes. Graphical abstract.

Multi-attribute PAT for UF/DF of Proteins-Monitoring Concentration, particle sizes, and Buffer Exchange.

Ultrafiltration/diafiltration (UF/DF) plays an important role in the manufacturing of biopharmaceuticals. Monitoring critical process parameters and quality attributes by process analytical technology (PAT) during those steps can facilitate process development and assure consistent quality in production processes. In this study, a lab-scale cross-flow filtration (CFF) device was equipped with a variable pathlength (VP) ultraviolet and visible (UV/Vis) spectrometer, a light scattering photometer, and a liquid density sensor (microLDS). Based on the measured signals, the protein concentration, buffer exchange, apparent molecular weight, and hydrodynamic radius were monitored. The setup was tested in three case studies. First, lysozyme was used in an UF/DF run to show the comparability of on-line and off-line measurements. The corresponding correlation coefficients exceeded 0.97. Next, urea-induced changes in protein size of glucose oxidase (GOx) were monitored during two DF steps. Here, correlation coefficients were ≥ 0.92 for static light scattering (SLS) and dynamic light scattering (DLS). The correlation coefficient for the protein concentration was 0.82, possibly due to time-dependent protein precipitation. Finally, a case study was conducted with a monoclonal antibody (mAb) to show the full potential of this setup. Again, off-line and on-line measurements were in good agreement with all correlation coefficients exceeding 0.92. The protein concentration could be monitored in-line in a large range from 3 to 120 g L- 1. A buffer-dependent increase in apparent molecular weight of the mAb was observed during DF, providing interesting supplemental information for process development and stability assessment. In summary, the developed setup provides a powerful testing system for evaluating different UF/DF processes and may be a good starting point to develop process control strategies. Graphical Abstract Piping and instrumentation diagram of the experimental setup and data generated by the different sensors. A VP UV/Vis spectrometer (FlowVPE, yellow) measures the protein concentration. From the data of the light scattering photometer (Zetasizer, green) in the on-line measurement loop, the apparant molecular weight and z-average are calculated. The density sensor (microLDS) measures density and viscosity of the fluid in the on-line loop.

Investigation of the reversibility of freeze/thaw stress-induced protein instability using heat cycling as a function of different cryoprotectants.

Formulation conditions have a significant influence on the degree of freeze/thaw (FT) stress-induced protein instabilities. Adding cryoprotectants might stabilize the induced FT stress instabilities. However, a simple preservation of protein stability might be insufficient and further methods are necessary. This study aims to evaluate the addition of a heat cycle following FT application as a function of different cryoprotectants with lysozyme as exemplary protein. Sucrose and glycerol were shown to be the most effective cryoprotectants when compared to PEG200 and Tween20. In terms of heat-induced reversibility of aggregates, glycerol showed the best performance followed by sucrose, NaCl and Tween20 systems. The analysis was performed using a novel approach to visualize complex interplays by a clustering and data reduction scheme. In addition, solubility and structural integrity were measured and confirmed the obtained results.

A phase diagram-based toolbox to assess the impact of freeze/thaw ramps on the phase behavior of proteins.

The influence of process parameters during freeze/thaw (FT) operations is essential for the preservation of the protein stability/activity during production and storage processes in the biopharmaceutical industry. Process parameters, such as FT ramps, the final storage time and temperature, affect the occurring FT stress onto the target protein in different ways. FT stress includes cold denaturation, freeze concentration, and ice crystal formation which can result in protein aggregation. To visualize the impact of variations in FT ramps, descriptors such as solubility, phase behavior and crystal morphology were evaluated. The phase diagram-based toolbox in combination with an HTS-compatible cryo-device allowed the identification of suitable ramping schemes during FT operations. It could be clearly shown that rapid operations are needed above the glass transition temperature of the target protein to circumvent precipitation during FT cycles. Finally, a stability index is introduced which allows ranking of the systems investigated.

Apparent protein cloud point temperature determination using a low volume high-throughput cryogenic device in combination with automated imaging.

Short-term parameters correlating to long-term protein stability, such as the protein cloud point temperature (Tcloud), are of interest to improve efficiency during protein product development. Such efficiency is reached if short-term parameters are obtained in a low volume and high-throughput (HT) manner. This study presents a low volume HT detection method for (sub-zero) Tcloud determination of lysozyme, as such an experimental method is not available yet. The setup consists of a cryogenic device with an automated imaging system. Measurement reproducibility (median absolute deviation of 0.2 °C) and literature-based parameter validation (Pearson correlation coefficient of 0.996) were shown by a robustness and validation study. The subsequent case study demonstrated a partial correlation between the obtained apparent Tcloud parameter and long-term protein stability as a function of lysozyme concentration, ion type, ionic strength, and freeze/thaw stress. The presented experimental setup demonstrates its ability to advance short-term strategies for efficient protein formulation development.

3D-Printed Phenacrylate Decarboxylase Flow Reactors for the Chemoenzymatic Synthesis of 4-Hydroxystilbene.

Continuous flow systems for chemical synthesis are becoming a major focus in organic chemistry and there is a growing interest in the integration of biocatalysts due to their high regio- and stereoselectivity. Methods established for 3D bioprinting enable the fast and simple production of agarose-based modules for biocatalytic reactors if thermally stable enzymes are available. We report here on the characterization of four different cofactor-free phenacrylate decarboxylase enzymes suitable for the production of 4-vinylphenol and test their applicability for the encapsulation and direct 3D printing of disk-shaped agarose-based modules that can be used for compartmentalized flow microreactors. Using the most active and stable phenacrylate decarboxylase from Enterobacter spec. in a setup with four parallel reactors and a subsequent palladium(II) acetate-catalysed Heck reaction, 4-hydroxystilbene was synthesized from p-coumaric acid with a total yield of 14.7 % on a milligram scale. We believe that, due to the convenient direct immobilization of any thermostable enzyme and straightforward tuning of the reaction sequence by stacking of modules with different catalytic activities, this simple process will facilitate the establishment and use of cascade reactions and will therefore be of great advantage for many research approaches.

Process monitoring of virus-like particle reassembly by diafiltration with UV/Vis spectroscopy and light scattering.

Virus-like particles (VLPs) have shown great potential as biopharmaceuticals in the market and in clinics. Nonenveloped, in vivo assembled VLPs are typically disassembled and reassembled in vitro to improve particle stability, homogeneity, and immunogenicity. At the industrial scale, cross-flow filtration (CFF) is the method of choice for performing reassembly by diafiltration. Here, we developed an experimental CFF setup with an on-line measurement loop for the implementation of process analytical technology (PAT). The measurement loop included an ultraviolet and visible (UV/Vis) spectrometer as well as a light scattering photometer. These sensors allowed for monitoring protein concentration, protein tertiary structure, and protein quaternary structure. The experimental setup was tested with three Hepatitis B core Antigen (HBcAg) variants. With each variant, three reassembly processes were performed at different transmembrane pressures (TMPs). While light scattering provided information on the assembly progress, UV/Vis allowed for monitoring the protein concentration and the rate of VLP assembly based on the microenvironment of Tyrosine-132. VLP formation was verified by off-line dynamic light scattering (DLS) and transmission electron microscopy (TEM). Furthermore, the experimental results provided evidence of aggregate-related assembly inhibition and showed that off-line size-exclusion chromatography does not provide a complete picture of the particle content. Finally, a Partial-Least Squares (PLS) model was calibrated to predict VLP concentrations in the process solution. Q2 values of 0.947-0.984 were reached for the three HBcAg variants. In summary, the proposed experimental setup provides a powerful platform for developing and monitoring VLP reassembly steps by CFF.