Polysorbate degradation by host cell-derived hydrolases presents a critical challenge in biopharmaceutical formulations. It can lead to fatty acid release, particle formation and reduced product stability. Mass spectrometry-based host cell protein (HCP) analysis is widely used for HCP identification, but detection becomes challenging in formulations where monoclonal antibodies are present in large excess. In such cases, hydrolases can remain undetected, despite being enzymatically active at trace levels.In this study, we demonstrate that individual CHO-derived hydrolases generate distinct polysorbate degradation fingerprints that can be detected by reverse phase ultra performance liquid chromatography coupled to mass spectrometry (RP-UPLC-MS) and classified using supervised machine learning. Models were trained on single time point fingerprints comprising approximately 50 measurements for five hydrolases (CES1F, CES2C, LPLA2, PPT1 and PAF-AH). Evaluated algorithms included Logistic Regression, Random Forest, Gradient Boosting, Support Vector Classifier, AdaBoost, and Artificial Neural Networks. Seven out of eight models achieved 100 % accuracy on the test set, confirming that enzyme-specific information is preserved in single measurements in the presence of individual enzymes, independent of enzyme concentration or degradation time.External validation using an independently prepared hydrolase spike sample confirmed the robustness of the models. Prediction confidence was high at early degradation stages and decreased at late stages, as enzyme-specific degradation fingerprints became more similar. This work presents an activity-based classification framework for the functional identification of polysorbate degrading hydrolases. The approach supports downstream monitoring and risk-based mitigation strategies by identifying the enzymes that drive polysorbate hydrolysis under formulation conditions.

Polysorbate-degrading host cell proteins (HCPs) represent a critical challenge in the manufacturing of monoclonal antibody therapeutics due to their potential to persist during downstream processing. While their enzymatic activity has been characterized, the role of direct HCP-mAb interactions, particularly those involving polysorbate degrading HCPs, remains poorly understood. In this study, we systematically investigated the binding behavior of four representative polysorbate-degrading HCPs (CES1F, LPLA2, PAF-AH, and PPT1) to a panel of mAbs using biolayer interferometry (BLI). All tested HCPs showed specific, transient interactions characterized by fast-on/fast-off kinetics, with apparent equilibrium dissociation constants (KD) in the low nanomolar range (40-90 nM for strong binders) and rapid dissociation kinetics (kd > 0.01 s-1). This indicates a binding mode characterized by relatively high affinity but limited kinetic stability. Due to incomplete saturation and partially not meeting the quality criteria for kinetic fitting, we complemented model-based analysis with equilibrium-derived descriptors. The initial slope of the binding isotherm correlated well with kinetic parameters and enabled robust ranking of interaction strength. To assess hitchhiking relevance during downstream processing, we performed a Protein A chromatography experiment using PLBL2 as a model HCP and two mAbs with different interaction profiles. PLBL2 levels in Protein A elution pools correlated well with interaction propensity confirming that transient interactions can contribute to HCP co-elution. Our results provide the first systematic and quantitative comparison of polysorbate hydrolase-antibody interactions. They also demonstrate that direct mAb-HCP interaction is a relevant mechanism contributing to HCP persistence during downstream processing.

Polysorbate degradation in biopharmaceutical formulations can impact the stability and efficacy of therapeutic proteins. This degradation is predominantly caused by specific residual host cell hydrolases present at sub-ppm concentrations. Their low abundance, combined with the lack of sensitive, enzyme specific detection methods, is a major analytical challenge. This study presents a novel approach using reverse-phase ultra-performance liquid chromatography coupled with mass spectrometry (RP-UPLC-MS) to systematically analyse the specific polysorbate degradation patterns of hydrolases expressed by Chinese hamster ovary (CHO) cells. Our findings reveal distinct degradation fingerprints of five CHO-derived hydrolases, highlighting their unique preferences for different polysorbate species based on ester linkages (e.g. monoester or multiester), hydrophilic head groups and FA chain length. This study is the first of its kind to provide such detailed insights into the enzymatic cleavage patterns of polysorbates using enzymes directly derived from CHO cells. Our results underscore the limitations of indirect polysorbate quantification assays, such as the fluorescence micelle assay (FMA) and 4-methylumbelliferone (MU4)-based hydrolytic activity assays, in accurately reflecting the activity of specific hydrolases. For instance, the FMA tends to overestimate the contribution of certain polysorbate species due to its reliance on micelle formation, while the MU4 assay's predictive reliability is limited by its use of surrogate substrates. In contrast, RP-UPLC-MS enables the precise identification of individual polysorbate species and their degradation products, providing a direct measure of hydrolase activity. The detailed enzymatic fingerprints obtained in this study pave the way for future advancements in enzyme classification and the potential application of computational tools for automated hydrolase identification.

The estimation of ion-exchange chromatography model parameters is crucial to enable efficient model-assisted biopharmaceutical downstream process development. Model calibration methods can be hindered by model limitations combined with parameter correlations, leading to time-consuming repeated parameter estimations. While Steric Mass Action isotherm estimation methods exist, there is a need for a systematic approach to estimate model parameters for an emerging Colloidal Particle Adsorption (CPA) model proposed by Briskot et al. This study presents a novel strategy that addresses this challenge, offering significant improvements. Through a parameter sensitivity analysis, we identified key levers for improved CPA parameter estimation, enabling the prediction of elution behavior for low and high load densities in gradient and step elution mode. This analysis also revealed the correlation structure of parameters, allowing the establishment of a minimalized experimental data set for parameter estimation, by using one breakthrough, a high load and three low load density gradient elution experiments. Our workflow leverages a surrogate-assisted global-optimization tool, minimizing computationally expensive function evaluations during parameter fitting. Furthermore, we employed a customized objective function, specifically adapted to the model structure and sensitivity results, to enhance the solver's performance. Our strategy was tested on three model proteins with molecular weights of approximately 50, 150 and 200 kDa using a strong cation exchange Poros 50 HS resin. Our final approach enabled high throughput CPA model calibration for single components. The resulting CPA models were able to describe non-binding protein-pulses, low and high-loaded gradient elution, break through, as well as isocratic elution experiments.

Polysorbates (PS) are commonly used as stabilizers of biopharmaceuticals such as monoclonal antibodies (mAbs). However, they are prone to chemical and enzymatic degradation. The latter can be caused by residual host cell proteins (HCPs) in the drug substance. Degradation affects the functionality of the PS surfactant which can lead to formation of particles. An increasing number of publications describe enzymatic PS degradation. Significant efforts have been made to characterize HCP removal during Downstream Processing (DSP) of mAbs and to develop mitigation strategies. Here we describe the use of glycine buffer for acidic elution in Protein A affinity chromatography compared to acetate buffer, which is more commonly used in the biopharmaceutical industry. Increased turbidity was observed during pH re-adjustment after low pH virus inactivation when using glycine buffer. Analytical data suggests that this turbidity is caused by the formation of precipitates which include HCP and DNA impurities. Additionally, as a zwitterion, glycine does not contribute to conductivity; this further enhances HCP removal during anion-exchange flow-through chromatography. Although glycine is well known as a possible elution buffer for Protein A affinity chromatography, its positive impact on HCP removal and PS stability have not yet been described in literature.

Mechanistic process models play an increasingly important role in biopharmaceutical process development and manufacturing in supporting process design, characterization, and informing process control strategies. Despite the potential of mechanistic models, there is currently no clear consensus or regulatory guideline on their qualification, i.e. the processes of determining whether a model is suitable to support decision making in process development. In this work, a systematic and risk-based qualification framework for mechanistic models in biopharmaceutical process development is introduced. The framework integrates key concepts from other modeling frameworks and guidelines such as the ASME V&V 40 published by the American Society of Mechanical Engineers (ASME) and preliminary considerations in process models published by Quality Innovation Group (QIG) of the European Medicines Agency (EMA). Key concepts of the proposed framework are discussed using two case studies, including a model-informed optimization of a biopharmaceutical ultrafiltration and diafiltration process and a model-informed control strategy of a chromatography polishing step. The suggested framework can act as a foundation for dialogue and guide for other modelers in biopharmaceutical process development. It holds the capability to harmonize modeling procedures throughout the industry and establish an agreement on the qualification of mechanistic models in biopharmaceutical process development.

Mechanistic modeling has shown to contribute greatly to the process understanding of chromatography and filtration processes. However, these are mostly considered individually and not connected for an entire downstream process. In this study, mechanistic models were connected to describe an entire downstream process of a Fab fragment. For the capture step, a transport-dispersion model (TDM) combined with an extended Langmuir isotherm was applied. Depth filtration was modeled with a combined pore blocking model. The polishing ion exchange chromatography steps were described by a TDM combined with the colloidal particle adsorption model. The tangential flow filtration model accounts for both the Donnan effects and flow limitations. The presented downstream process model could predict online and offline data recorded at 12,000 L manufacturing scale. Process variations of 23 manufacturing batches were adequately reproduced by the model based on the consideration of input process parameter variations.

The development of ultrafiltration and diafiltration (UF/DF) processes in the biopharmaceutical industry is largely empirical and rarely mechanistic. As a result, the full potential of process understanding is not unleashed. In this study, a model for a biopharmaceutical UF/DF process is introduced and applied for a Fab fragment from lab to manufacturing scale using a Ultracel Pellicon & REG; 3 C-Screen cassette. For model calibration viscosity data, membrane permeability and a single ultrafiltration experiment were used. The calibrated model responded correctly to extrapolating process conditions regarding the control parameters crossflow and transmembrane pressure (TMP). This was demonstrated by a multivariate dataset of ultrafiltration experiments ranging from a protein concentration of 5 g/L to 200 g/L. The validated model was used to predict the entire UF/DF process at lab and manufacturing scale.

A fundamental process understanding of an entire downstream process is essential for achieving and maintaining the high-quality standards demanded for biopharmaceutical drugs. A holistic process model based on mechanistic insights could support process development by identifying dependencies between process parameters and critical quality attributes across unit operations to design a holistic control strategy. In this study, state-of-the-art mechanistic models were calibrated and validated as digital representations of a biopharmaceutical manufacturing process. The polishing ion exchange chromatography steps (Q Sepharose FF, Poros 50 HS) were described by a transport-dispersive model combined with a colloidal particle adsorption model. The elution behavior of four size variants was analyzed and included in the model. Titration curves of pH adjustments were simulated using a mean-field approach considering interactions between the protein of interest and other ions in solution. By including adjustment steps the important process control inputs ionic strength, dilution, and pH were integrated. The final process model was capable to predict online and offline data at manufacturing scale. Process variations at manufacturing scale of 94 runs were adequately reproduced by the model. Furthermore, the process robustness against a 20% input variation of concentration, size variant and ion composition, volume, and pH could be confirmed with the model. The presented model demonstrates the potential of the integrated approach for predicting manufacturing process performance across scales and operating units.

The Gibbs–Donnan effect is a well-known phenomenon causing ions to be distributed unevenly across semi-permeable membranes that are permeable to ions but not to larger macromolecules such as proteins. In protein ultrafiltration and diafiltration (UF/DF) processes, this effect often leads to discrepancies between the pH and excipient concentrations in the final drug substance and in the DF buffer. In this work, a model describing the retentate and permeate composition throughout combined UF/DF processes is introduced. The model accounts for volume exclusion effects and electrostatic interactions between ions and the protein based on the Poisson–Boltzmann theory in combination with a basic Stern model. Advantages and limitations of the proposed model were analyzed using UF/DF experiments with multiple diafiltration buffers and proteins. A comparison between simulated and experimental permeate data showed good agreement for low to moderate Donnan potentials but model limitations for high Donnan potentials at protein concentration larger 100 gL-1. In contrast, simulated retentate data showed good agreement for both low and high Donnan potentials and for protein concentrations up to 190 gL-1. It was demonstrated that in this high protein concentration regime, the applied basic Stern model provides more accurate predictions compared to previous theories based on the Poisson–Boltzmann theory alone. This makes the model a valuable tool to describe discrepancies between pH and excipient concentrations in the final drug substance and DF buffer for highly concentrated protein formulations. As model predictions are based solely on structural information on the protein and the composition of the DF buffer, the model is particularly beneficial at an early stage in process development to streamline process development and improve process understanding.

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.

With the quality by design (QbD) initiative, regulatory authorities demand a consistent drug quality originating from a well-understood manufacturing process. This study demonstrates the application of a previously published mechanistic chromatography model to the in silico process characterization (PCS) of a monoclonal antibody polishing step. The proposed modeling workflow covered the main tasks of traditional PCS studies following the QbD principles, including criticality assessment of 11 process parameters and establishment of their proven acceptable ranges of operation. Analyzing effects of multi-variate sampling of process parameters on the purification outcome allowed identification of the edge-of-failure. Experimental validation of in silico results demanded approximately 75% less experiments compared to a purely wet-lab based PCS study. Stochastic simulation, considering the measured variances of process parameters and loading material composition, was used to estimate the capability of the process to meet the acceptance criteria for critical quality attributes and key performance indicators. The proposed workflow enables the implementation of digital process twins as QbD tool for improved development of biopharmaceutical manufacturing processes.

A fundamental understanding of the protein retention mechanism in preparative ion exchange (IEX) chromatography columns is essential for a model-based process development approach. For the past three decades, the mechanistic description of protein retention has been based predominantly on the steric mass action (SMA) model. In recent years, however, retention profiles of proteins have been reported more frequently for preparative processes that are not consistent with the mechanistic understanding relying on the SMA model. In this work, complex elution behavior of proteins in preparative IEX processes is analyzed using a colloidal particle adsorption (CPA) model. The CPA model is found to be capable of reproducing elution profiles that cannot be described by the traditional SMA model. According to the CPA model, the reported complex behavior can be ascribed to a strong compression and concentration of the elution front in the lower unsaturated part of the chromatography column. As the unsaturated part of the column decreases with increasing protein load density, exceeding a critical load density can lead to the formation of a shoulder in the peak front. The general applicability of the model in describing preparative IEX processes is demonstrated using several industrial case studies including multiple monoclonal antibodies on different IEX adsorber systems. In this context, the work covers both salt controlled and pH-controlled protein elution.

Biotechnology and BioengineeringVolume 118, Issue 8 p. 2823-2828 ISSUE INFORMATIONFree Access Biotechnology and Bioengineering: Volume 118, Number 8, August 2021 First published: 16 July 2021 https://doi.org/10.1002/bit.27413AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume118, Issue8August 2021Pages 2823-2828 RelatedInformation

β‐Glucan process‐related impurities can be introduced into biopharmaceutical products via upstream or downstream processing or via excipients. This study obtained a comprehensive process‐mapping dataset for five monoclonal antibodies to assess β‐glucan introduction and clearance during development and production runs at various scales. Overall, 198 data points were available for analysis. The greatest β‐glucan concentrations were found in the depth‐filtration filtrate (37–2,745 pg/ml). Load volume correlated with β‐glucan concentration in the filtrate, whereas flush volume was of secondary importance. Cation‐exchange chromatography significantly cleared β‐glucans. Furthermore, β‐glucan leaching from the Planova 20N virus removal filter was reduced by increasing the flush volume (1 vs. 10 L/m2). β‐glucan concentrations after filter flush with 10 L/m2 were consistently <10 pg/ml. No or only limited β‐glucan clearance was attained via ultrafiltration/diafiltration (UF/DF). However, during the first run with monoclonal antibody (mAb) 4, β‐glucan concentration in the UF/DF retentate was 10.8 pg/mg, potentially due to β‐glucan leaching from the first run with a regenerated cellulose membrane. Overall, β‐glucan levels in the final mAb drug substance were 1–12 pg/mg. Assuming high doses of 1,000–5,000 mg, a β‐glucan contamination at 20 pg/mg would translate to 20–100 ng/dose, which is below the previously suggested threshold for product safety (≤500 ng/dose).

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.

Mechanistic modeling of chromatography processes is one of the most promising techniques for the digitalization of biopharmaceutical process development. Possible applications of chromatography models range from in silico process optimization in early phase development to in silico root cause investigation during manufacturing. Nonetheless, the cumbersome and complex model calibration still decelerates the implementation of mechanistic modeling in industry. Therefore, the industry demands model calibration strategies that ensure adequate model certainty in a limited amount of time. This study introduces a directed and straightforward approach for the calibration of pH-dependent, multicomponent steric mass action (SMA) isotherm models for industrial applications. In the case investigated, the method was applied to a monoclonal antibody (mAb) polishing step including four protein species. The developed strategy combined well-established theories of preparative chromatography (e.g. Yamamoto method) and allowed a systematic reduction of unknown model parameters to 7 from initially 32. Model uncertainty was reduced by designing two representative calibration experiments for the inverse estimation of remaining model parameters. Dedicated experiments with aggregate-enriched load material led to a significant reduction of model uncertainty for the estimates of this low-concentrated product-related impurity. The model was validated beyond the operating ranges of the final unit operation, enabling its application to late-stage downstream process development. With the proposed model calibration strategy, a systematic experimental design is provided, calibration effort is strongly reduced, and local minima are avoided.

Cation exchange chromatography (CEX) is an essential part of most monoclonal antibody (mAb) purification platforms. Process characterization and root cause investigation of chromatographic unit operations are performed using scale down models (SDM). SDM chromatography columns typically have the identical bed height as the respective manufacturing-scale, but a significantly reduced inner diameter. While SDMs enable process development demanding less material and time, their comparability to manufacturing-scale can be affected by variability in feed composition, mobile phase and resin properties, or dispersion effects depending on the chromatography system at hand. Mechanistic models can help to close gaps between scales and reduce experimental efforts compared to experimental SDM applications. In this study, a multicomponent steric mass-action (SMA) adsorption model was applied to the scale-up of a CEX polishing step. Based on chromatograms and elution pool data ranging from laboratory- to manufacturing-scale, the proposed modeling workflow enabled early identification of differences between scales, for example, system dispersion effects or ionic capacity variability. A multistage model qualification approach was introduced to measure the model quality and to understand the model's limitations across scales. The experimental SDM and the in silico model were qualified against large-scale data using the identical state of the art equivalence testing procedure. The mechanistic chromatography model avoided limitations of the SDM by capturing effects of bed height, loading density, feed composition, and mobile phase properties. The results demonstrate the applicability of mechanistic chromatography models as a possible alternative to conventional SDM approaches.

In the biopharmaceutical industry, development and characterization of chromatography processes is typically based on statistical models. Although these approaches are easy to apply, the resulting models may fail to predict non-linear behavior in preparative chromatography with complex protein feed streams. An alternative to empirical methods are mechanistic models. In chemical engineering, mechanistic modeling has been a standard method for decades. As mechanistic models continue their advance in the biopharmaceutical industry, this study underlines the need of a standardized methodology for mechanistic model calibration. A lumped rate model was applied to the polishing chromatography of a bispecific antibody. Following guidelines for good modeling practice, the model was thoroughly analyzed. Potential limitations such as over-parameterization, parameter correlations, imprecise parameter estimates or systematic errors were considered by evaluation of parameter confidence intervals, visual sensitivity analysis and model validation across different scales. Application of simulations for identification of critical process parameters will be discussed.