NIR spectroscopy-CNN-enabled chemometrics for multianalyte monitoring in microbial fermentation.

As the biopharmaceutical industry looks to implement Industry 4.0, the need for rapid and robust analytical characterization of analytes has become a pressing priority. Spectroscopic tools, like near-infrared (NIR) spectroscopy, are finding increasing use for real-time quantitative analysis. Yet detection of multiple low-concentration analytes in microbial and mammalian cell cultures remains an ongoing challenge, requiring the selection of carefully calibrated, resilient chemometrics for each analyte. The convolutional neural network (CNN) is a puissant tool for processing complex data and making it a potential approach for automatic multivariate spectral processing. This work proposes an inception module-based two-dimensional (2D) CNN approach (I-CNN) for calibrating multiple analytes using NIR spectral data. The I-CNN model, coupled with orthogonal partial least squares (PLS) preprocessing, converts the NIR spectral data into a 2D data matrix, after which the critical features are extracted, leading to model development for multiple analytes. Escherichia coli fermentation broth was taken as a case study, where calibration models were developed for 23 analytes, including 20 amino acids, glucose, lactose, and acetate. The I-CNN model result statistics depicted an average R2 values of prediction 0.90, external validation data set 0.86 and significantly lower root mean square error of prediction values ∼0.52 compared to conventional regression models like PLS. Preprocessing steps were applied to I-CNN models to evaluate any augmentation in prediction performance. Finally, the model reliability was assessed via real-time process monitoring and comparison with offline analytics. The proposed I-CNN method is systematic and novel in extracting distinctive spectral features from a multianalyte bioprocess data set and could be adapted to other complex cell culture systems requiring rapid quantification using spectroscopy.

Impact of Excipient Extraction and Buffer Exchange on Recombinant Monoclonal Antibody Stability.

The foundation of a biosimilar manufacturer's regulatory filing is the demonstration of analytical and functional similarity between the biosimilar product and the pertinent originator product. The excipients in the formulation may interfere with characterization using typical analytical and functional techniques during this biosimilarity exercise. Consequently, the producers of biosimilar products resort to buffer exchange to isolate the biotherapeutic protein from the drug product formulation. However, the impact that this isolation has on the product stability is not completely known. This study aims to elucidate the extent to which mAb isolation via ultrafiltration-diafiltration-based buffer exchange impacts mAb stability. It has been demonstrated that repeated extraction cycles do result in significant changes in higher-order structure (red-shift of 5.0 nm in fluorescence maxima of buffer exchanged samples) of the mAb and also an increase in formation of basic variants from 19.1 to 26.7% and from 32.3 to 36.9% in extracted innovator and biosimilar Tmab samples, respectively. It was also observed that under certain conditions of tertiary structure disruptions, Tmab could be restabilized depending on formulation composition. Thus, mAb isolation through extraction with buffer exchange impacts the product stability. Based on the observations reported in this paper, we recommend that biosimilar manufacturers take into consideration these effects of excipients on protein stability when performing biosimilarity assessments.

Role of Charge Heterogeneity on Physical Stability of Monoclonal Antibody Biotherapeutic Products.

PURPOSE: Chemical modifications in monoclonal antibodies can change hydrophobicity, charge heterogeneity as well as conformation, which eventually can impact their physical stability. In this study, the effect of the individual charge variants on physical stability and aggregation propensity in two different buffer conditions used during downstream purification was investigated. METHODS: The charge variants were separated using semi-preparative cation exchange chromatography and buffer exchanged in the two buffers with pH 6.0 and 3.8. Subsequently each variant was analysed for size heterogeneity using size exclusion chromatography and dynamic light scattering, conformational stability, colloidal stability, and aggregation behaviour under accelerated stability conditions. RESULTS: Size variants in each charge variant were similar in both pH conditions when analyzed without extended storage. However, conformational stability was lower at pH 3.8 than pH 6.0. All charge variants showed similar apparent melting temperature at pH 6.0. In contrast, at pH 3.8 variants A3, A5, B2, B3 and B4 display lower Tm, suggesting reduced conformational stability. Further, A2, A3 and A5 exhibit reduced colloidal stability at pH 3.8. In general, acidic variants are more prone to aggregation than basic variants. CONCLUSION: Typical industry practice today is to examine in-process intermediate stability with acidic species and basic species taken as a single category each. We suggest that perhaps stability evaluation needs to be performed at specie level as different acidic or basic species have different stability and this knowledge can be used for clever designing of the downstream process to achieve a stable product.

Convolutional Neural Networks Guided Raman Spectroscopy as a Process Analytical Technology (PAT) Tool for Monitoring and Simultaneous Prediction of Monoclonal Antibody Charge Variants.

BACKGROUND: Charge related heterogeneities of monoclonal antibody (mAb) based therapeutic products are increasingly being considered as a critical quality attribute (CQA). They are typically estimated using analytical cation exchange chromatography (CEX), which is time consuming and not suitable for real time control. Raman spectroscopy coupled with artificial intelligence (AI) tools offers an opportunity for real time monitoring and control of charge variants. OBJECTIVE: We present a process analytical technology (PAT) tool for on-line and real-time charge variant determination during process scale CEX based on Raman spectroscopy employing machine learning techniques. METHOD: Raman spectra are collected from a reference library of samples with distribution of acidic, main, and basic species from 0-100% in a mAb concentration range of 0-20 g/L generated from process-scale CEX. The performance of different machine learning techniques for spectral processing is compared for predicting different charge variant species. RESULT: A convolutional neural network (CNN) based model was successfully calibrated for quantification of acidic species, main species, basic species, and total protein concentration with R2 values of 0.94, 0.99, 0.96 and 0.99, respectively, and the Root Mean Squared Error (RMSE) of 0.1846, 0.1627, and 0.1029 g/L, respectively, and 0.2483 g/L for the total protein concentration. CONCLUSION: We demonstrate that Raman spectroscopy combined with AI-ML frameworks can deliver rapid and accurate determination of product related impurities. This approach can be used for real time CEX pooling decisions in mAb production processes, thus enabling consistent charge variant profiles to be achieved.

Explainable AI for CHO cell culture media optimization and prediction of critical quality attribute.

Cell culture media play a critical role in cell growth and propagation by providing a substrate; media components can also modulate the critical quality attributes (CQAs). However, the inherent complexity of the cell culture media makes unraveling the impact of the various media components on cell growth and CQAs non-trivial. In this study, we demonstrate an end-to-end machine learning framework for media component selection and prediction of CQAs. The preliminary dataset for feature selection was generated by performing CHO-GS (-/-) cell culture in media formulations with varying metal ion concentrations. Acidic and basic charge variant composition of the innovator product (24.97 ± 0.54% acidic and 11.41 ± 1.44% basic) was chosen as the target variable to evaluate the media formulations. Pearson's correlation coefficient and random forest-based techniques were used for feature ranking and feature selection for the prediction of acidic and basic charge variants. Furthermore, a global interpretation analysis using SHapley Additive exPlanations was utilized to select optimal features by evaluating the contributions of each feature in the extracted vectors. Finally, the medium combinations were predicted by employing fifteen different regression models and utilizing a grid search and random search cross-validation for hyperparameter optimization. Experimental results demonstrate that Fe and Zn significantly impact the charge variant profile. This study aims to offer insights that are pertinent to both innovators seeking to establish a complete pipeline for media development and optimization and biosimilar-based manufacturers who strive to demonstrate the analytical and functional biosimilarity of their products to the innovator. KEY POINTS: • Developed a framework for optimizing media components and prediction of CQA. • SHAP enhances global interpretability, aiding informed decision-making. • Fifteen regression models were employed to predict medium combinations.

Deep learning framework for peak detection at the intact level of therapeutic proteins.

While automated peak detection functionalities are available in commercially accessible software, achieving optimal true positive rates frequently necessitates visual inspection and manual adjustments. In the initial phase of this study, hetero-variants (glycoforms) of a monoclonal antibody were distinguished using liquid chromatography-mass spectrometry, revealing discernible peaks at the intact level. To comprehensively identify each peak (hetero-variant) in the intact-level analysis, a deep learning approach utilizing convolutional neural networks (CNNs) was employed in the subsequent phase of the study. In the current case study, utilizing conventional software for peak identification, five peaks were detected using a 0.5 threshold, whereas seven peaks were identified using the CNN model. The model exhibited strong performance with a probability area under the curve (AUC) of 0.9949, surpassing that of partial least squares discriminant analysis (PLS-DA) (probability AUC of 0.8041), and locally weighted regression (LWR) (probability AUC of 0.6885) on the data acquired during experimentation in real-time. The AUC of the receiver operating characteristic curve also illustrated the superior performance of the CNN over PLS-DA and LWR.

An efficient computational protocol for template-based design of peptides that inhibit interactions involving SARS-CoV-2 proteins.

The RNA-dependent RNA polymerase (RdRp) complex of SARS-CoV-2 lies at the core of its replication and transcription processes. The interfaces between holo-RdRp subunits are highly conserved, facilitating the design of inhibitors with high affinity for the interaction interface hotspots. We, therefore, take this as a model protein complex for the application of a structural bioinformatics protocol to design peptides that inhibit RdRp complexation by preferential binding at the interface of its core subunit nonstructural protein, nsp12, with accessory factor nsp7. Here, the interaction hotspots of the nsp7-nsp12 subunit of RdRp, determined from a long molecular dynamics trajectory, are used as a template. A large library of peptide sequences constructed from multiple hotspot motifs of nsp12 is screened in-silico to determine sequences with high geometric complementarity and interaction specificity for the binding interface of nsp7 (target) in the complex. Two lead designed peptides are extensively characterized using orthogonal bioanalytical methods to determine their suitability for inhibition of RdRp complexation. Binding affinity of these peptides to accessory factor nsp7, determined using a surface plasmon resonance (SPR) assay, is slightly better than that of nsp12: dissociation constant of 133nM and 167nM, respectively, compared to 473nM for nsp12. A competitive ELISA is used to quantify inhibition of nsp7-nsp12 complexation, with one of the lead peptides giving an IC50 of 25µM . Cell penetrability and cytotoxicity are characterized using a cargo delivery assay and MTT cytotoxicity assay, respectively. Overall, this work presents a proof-of-concept of an approach for rational discovery of peptide inhibitors of SARS-CoV-2 protein-protein interactions.

Rapid Estimation of Size-Based Heterogeneity in Monoclonal Antibodies by Machine Learning-Enhanced Dynamic Light Scattering.

Aggregation of monoclonal antibody therapeutics is a serious concern that is believed to impact product safety and efficacy. There is a need for analytical approaches that enable rapid estimation of mAb aggregates. Dynamic light scattering (DLS) is a well-established technique for estimating the average size of protein aggregates or for evaluating sample stability. It is usually used to measure the size and size distribution over a wide range of nano- to micro-sized particles using time-dependent fluctuations in the intensity of scattered light arising from the Brownian motion of particles. In this study, we present a novel DLS-based approach that allows us to quantify the relative percentage of multimers (monomer, dimer, trimer, and tetramer) in a monoclonal antibody (mAb) therapeutic product. The proposed approach uses a machine learning (ML) algorithm and regression to model the system and predict the amount of relevant species such as monomer, dimer, trimer, and tetramer of a mAb in the size range of 10-100 nm. The proposed DLS-ML technique compares favorably to all potential alternatives with respect to the key method attributes, including per sample cost of analysis, per sample time of data acquisition along with ML-based aggregate prediction (<2 min), sample requirements (<3 µg), and user-friendliness of analysis. The proposed rapid method can serve as an orthogonal tool to size exclusion chromatography, which is the current industry workhorse for aggregate assessment.

High-throughput capillary electrophoresis analysis of biopharmaceuticals utilizing sequential injections.

The complexity of biotherapeutic products implies an ever-increasing list of product quality attributes that need to be monitored and characterized. In addition, the growing interest in implementing process analytical technology in biopharmaceutical production has further increased the testing burden, together with the need for rapid testing that can facilitate real-time or near-real-time decision-making. Capillary electrophoresis (CE) has made a place in biopharmaceutical analysis but is regarded as a low-throughput method, with the instrument dead time constituting more than 80% of the total time of analysis. In this study, the dead time of CE was utilized to analyse 3 mAb samples in a single-CE run. This approach resulted in an up to 77% reduction in the total analysis time and increased the productivity by up to 300%, compared to traditional single CE-ultraviolet runs, without compromising resolution or relative peak areas. Additionally, good method reproducibility was observed. The compatibility of the method has been demonstrated with protein A eluate and cation exchange chromatography fractions. We, thus, propose that sequential injections can be applied for fast and robust CE analysis of biopharmaceuticals.

Continuous integrated manufacturing for biopharmaceuticals: A new paradigm or an empty promise?

Continuous integrated bioprocessing has elicited considerable interest from the biopharma industry for the many purported benefits it promises. Today many major biopharma manufacturers around the world are engaged in the development of continuous process platforms for their products. In spite of great potential, the path toward continuous integrated bioprocessing remains unclear for the biologics industry due to legacy infrastructure, process integration challenges, vague regulatory guidelines, and a diverging focus toward novel therapies. In this article, we present a review and perspective on this topic. We explore the status of the implementation of continuous integrated bioprocessing among biopharmaceutical manufacturers. We also present some of the key hurdles that manufacturers are likely to face during this implementation. Finally, we hypothesize that the real impact of continuous manufacturing is likely to come when the cost of manufacturing is a substantial portion of the cost of product development, such as in the case of biosimilar manufacturing and emerging economies.

Advances in bioreactor control for production of biotherapeutic products.

Advanced control strategies are well established in chemical, pharmaceutical, and food processing industries. Over the past decade, the application of these strategies is being explored for control of bioreactors for manufacturing of biotherapeutics. Most of the industrial bioreactor control strategies apply classical control techniques, with the control system designed for the facility at hand. However, with the recent progress in sensors, machinery, and industrial internet of things, and advancements in deeper understanding of the biological processes, coupled with the requirement of flexible production, the need to develop a robust and advanced process control system that can ease process intensification has emerged. This has further fuelled the development of advanced monitoring approaches, modeling techniques, process analytical technologies, and soft sensors. It is seen that proper application of these concepts can significantly improve bioreactor process performance, productivity, and reproducibility. This review is on the recent advancements in bioreactor control and its related aspects along with the associated challenges. This study also offers an insight into the future prospects for development of control strategies that can be designed for industrial-scale production of biotherapeutic products.

Digital twin of a continuous chromatography process for mAb purification: Design and model-based control.

Model-based design of integrated continuous train coupled with online process analytical technology (PAT) tool can be a potent facilitator for monitoring and control of Critical Quality Attributes (CQAs) in real time. Charge variants are product related variants and are often regarded as CQAs as they may impact potency and efficacy of drug. Robust pooling decision is required for achieving uniform charge variant composition for mAbs as baseline separation between closely related variants is rarely achieved in process scale chromatography. In this study, we propose a digital twin of a continuous chromatography process, integrated with an online HPLC-PAT tool for delivering real time pooling decisions to achieve uniform charge variant composition. The integrated downstream process comprised continuous multicolumn capture protein A chromatography, viral inactivation in coiled flow inverter reactor (CFIR), and multicolumn CEX polishing step. An online HPLC was connected to the harvest tank before protein A chromatography. Both empirical and mechanistic modeling have been considered. The model states were updated in real time using online HPLC charge variant data for prediction of the initial and final cut point for CEX eluate, according to which the process chromatography was directed to switch from collection to waste to achieve the desired charge variant composition in the CEX pool. Two case studies were carried out to demonstrate this control strategy. In the first case study, the continuous train was run for initially 14 h for harvest of fixed charge variant composition as feed. In the second case study, charge variant composition was dynamically changed by introducing forced perturbation to mimic the deviations that may be encountered during perfusion cell culture. The control strategy was successfully implemented for more than ±5% variability in the acidic variants of the feed with its composition in the range of acidic (13%-17%), main (18%-23%), and basic (59%-68%) variants. Both the case studies yielded CEX pool of uniform distribution of acidic, main and basic profiles in the range of 15 ± 0.8, 31 ± 0.3, and 53 ± 0.5%, respectively, in the case of empirical modeling and 15 ± 0.5, 31 ± 0.3, and 53 ± 0.3%, respectively, in the case of mechanistic modeling. In both cases, process yield for main species was >85% and the use of online HPLC early in the purification train helped in making quicker decision for pooling of CEX eluate. The results thus successfully demonstrate the technical feasibility of creating digital twins of bioprocess operations and their utility for process control.

Combined Presence of Ferrous Ions and Hydrogen Peroxide in Normal Saline and In Vitro Models Induces Enhanced Aggregation of Therapeutic IgG due to Hydroxyl Radicals.

Therapeutic monoclonal antibodies (mAb) are known to form aggregates and fragments upon exposure to hydrogen peroxide (H2O2) and ferrous ions (Fe2+). H2O2 and Fe2+ react to form hydroxyl radicals that are detrimental to protein structures. In this study, aggregation of mAb in the combined presence of Fe2+ and H2O2 was investigated in saline and physiologically relevant in vitro models. In the first case study, forced degradation of mAb in saline (a fluid used for administration of mAb) was carried out at 55 °C in the combined presence of 0.2 mM Fe2+ and 0.1% H2O2. The control and stressed samples were analyzed using an array of techniques including visual observation, size-exclusion chromatography (SEC), dynamic light scattering (DLS), microscopy, UV-vis, fluorescence, Fourier transform infrared spectroscopy, and cell-based toxicity assays. At the end of 1 h, samples having the combined presence of both Fe2+ and H2O2 exhibited more than 20% HMW (high molecular weight species), whereas samples having only Fe2+, H2O2, or neither resulted in less than 3% HMW. Aggregate-rich samples also exhibited altered protein structures and hydrophobicity. Aggregation increased upon increasing the time, temperature, and concentration of Fe2+ and H2O2. Samples having both Fe2+ and H2O2 also showed higher cytotoxicity in red blood cells. Samples of mAb with chlorides of copper and cobalt with H2O2 also resulted in multifold degradation. The first case study showed enhanced aggregation of mAb in the combined presence of Fe2+ and H2O2 in saline. In the second case study, aggregation of mAb was investigated in artificially prepared extracellular saline and in vitro models such as macromolecule free fraction of serum and serum. In the presence of both Fe2+ and H2O2, %HMW was higher in extracellular saline compared to macromolecule free fraction of serum. Further, in vitro models having the combined presence of Fe2+ and H2O2 resulted in enhanced aggregation of mAb compared to models that had neither.

Rapid analysis of titer, aggregate, and intact mass of antibody therapeutics using automated multi-dimensional liquid chromatography coupled with native mass spectroscopy.

Monoclonal antibodies are tetrameric complex proteins, primarily produced using mammalian cell culture. Attributes such as titer, aggregates, and intact mass analysis are monitored during process development/optimization. In the present study, a novel workflow such that the Protein-A affinity chromatography is performed in the first dimension for purification and titer estimation, whereas size exclusion chromatography is employed in the second dimension to characterize size variants using native mass spectrometry. The present workflow offers a significant advantage over the traditionally used standalone Protein-A affinity chromatography followed by size exclusion chromatography analysis in that it can monitor these four attributes in 8 min while requiring a minimal sample size (10-15 µg) and not requiring any manual peak collection. In contrast, the traditional standalone approach requires manual collection of eluted peaks in Protein-A affinity chromatography followed by buffer exchange to a mass-compatible buffer, which can take up to 2-3 h with considerable risk of sample loss, degradation, and induced modifications. As the biopharma industry moves to make analytical testing efficient, we believe that the approach proposed here would be of significant interest due to its ability to monitor multiple process and product quality attributes in a single workflow and via rapid analysis.

Long term culturing of CHO cells: phenotypic drift and quality attributes of the expressed monoclonal antibody.

OBJECTIVE: Establishing cell lines with enhanced protein production requires a deep understanding of the cellular dynamics and cell line stability. The aim of the study is to investigate the impact of long term culturing (LTC) on cell morphology and altered cellular functions possibly leading to phenotypic drift, impacting product yield and quality. Study highlights the orthogonal cellular and analytical assay toolbox to define cell line stability for optimal culture performance and product quality. METHODS: We investigated recombinant monoclonal antibody (mAb) expressing CHO cells for 60 passages or 180 generations and assessed the cell growth characteristics and morphology by confocal and scanning electron microscopy. Quality attributes of expressed mAb is accessed by performing charge variants, glycan, and host cell protein analysis. RESULTS: We observed a 1.65-fold increase in viable cell population and 1.3-fold increase in cell specific growth rate. A 2.5-fold decrease in antibody titer and abatement of actin filament indicate cellular phenotypic drift. Mitochondrial membrane potential (∆ΨM) signified cell health and metabolic activity during LTC. Host cell protein production is reduced by 1.8-fold. Charge heterogeneity was perturbed with 12.5% and 43% reduction in abundance of acidic and basic charge variants respectively. Glycan profile indicated a decline in fucosylation with 17% increase in galactosylated species as compared with early passaged cells. CONCLUSION: LTC impinges on cellular phenotype as well as the quality of the expressed antibody, suggesting a defined subculturing limit to retain stable protein expression and cell morphology to achieve consistent product quality. Study signifies the changes in cellular and metabolic markers, suggesting cellular and analytical toolbox which could play a significant role in defining cell characteristics and ensured product quality.

A Domain-Shift Invariant CNN Framework for Cardiac MRI Segmentation Across Unseen Domains.

The emergence of various deep learning approaches in diagnostic medical image segmentation has made machines capable of accomplishing human-level accuracy. However, the generalizability of these architectures across patients from different countries, Magnetic Resonance Imaging (MRI) scans from distinct vendors, and varying imaging conditions remains questionable. In this work, we propose a translatable deep learning framework for diagnostic segmentation of cine MRI scans. This study aims to render the available SOTA (state-of-the-art) architectures domain-shift invariant by utilizing the heterogeneity of multi-sequence cardiac MRI. To develop and test our approach, we curated a diverse group of public datasets and a dataset obtained from private source. We evaluated 3 SOTA CNN (Convolution neural network) architectures i.e., U-Net, Attention-U-Net, and Attention-Res-U-Net. These architectures were first trained on a combination of three different cardiac MRI sequences. Next, we examined the M&M (multi-center & mutli-vendor) challenge dataset to investigate the effect of different training sets on translatability. The U-Net architecture, trained on the multi-sequence dataset, proved to be the most generalizable across multiple datasets during validation on unseen domains. This model attained mean dice scores of 0.81, 0.85, and 0.83 for myocardial wall segmentation after testing on unseen MyoPS (Myocardial Pathology Segmentation) 2020 dataset, AIIMS (All India Institute of Medical Sciences) dataset and M&M dataset, respectively. Our framework achieved Pearson's correlation values of 0.98, 0.99, and 0.95 between the observed and predicted parameters of end diastole volume, end systole volume, and ejection fraction, respectively, on the unseen Indian population dataset.

Production of bioactive recombinant monoclonal antibody fragment in periplasm of Escherichia coli expression system.

The microbial expression system (Escherichia coli) is the most widely studied host for the production of biotherapeutic products, such as antibody fragments, single chain variable fragments and nanobodies. However, recombinant biotherapeutic proteins are often expressed as insoluble proteins, thereby limiting the utility of E. coli as expression system. To overcome this limitation, various strategies have been developed, such as changes at DNA level (codon optimization), fusion with soluble tags and variations in process parameters (temperature), and inducer concentration. However, there is no "one size fits all" strategy. The most commonly used approach involves induction at low temperature, as reducing the temperature during cultivation has been reported to increase bioactive protein production in E. coli. In this study, we examine the impact of various process parameters, such as temperature and inducer concentration, as well as, high plasmid copy number vector for achieving enhanced soluble expression of TNFα inhibitor Fab. An interaction amongst these parameters has been observed and their optimization has been demonstrated to result in expression of 30 ± 3 mg/L antibody fragment using E. coli. This case study illustrates how process optimization can contribute toward making biotherapeutics affordable.

Multiattribute Monitoring of Charge-Based Heterogeneity of Recombinant Monoclonal Antibodies Using 2D HIC-WCX-MS.

Charged heterogeneity of monoclonal antibody (mAb) products is regarded as a critical quality attribute (CQA) depending on its impact on the safety and efficacy profile of the product. Hence, manufacturers are expected to perform a comprehensive characterization of the charge heterogeneity to ensure that the manufactured product meets its specifications. Further, monitoring is also expected during the product lifecycle to demonstrate consistency in product quality. However, conventional analytical methods for characterization of hydrophobic and charge variants are nonvolatile salt-based and require manual fraction collection and desalting steps before analysis through mass spectrometry can be performed. In the present study, a workflow of a two-dimensional liquid chromatography method using mass spectrometry (MS)-compatible buffers coupled with native mass spectrometry was performed to characterize hydrophobic variants in the first dimension and charge variants in the second dimension without any need for manual fractionation. This novel two-dimensional (2D) hydrophobic interaction chromatography (HIC)-weak cation-exchange chromatography (WCX)-MS workflow identified 10 variants in mAb A, out of which 2 variants are exclusive to the 2D orthogonal method. Similarly, for mAb B, a total of 11 variants are identified, including 5 variants exclusive to the 2D orthogonal workflow. When compared to stand-alone, HIC resolved only 4 variants for both mAbs and WCX resolved 7 variants for mAb A and 6 variants for mAb B. In addition, the proposed method allows direct characterization of hydrophobic/charge variant peaks through native mass spectrometry in a single-run workflow.

Applications of capillary electrophoresis for biopharmaceutical product characterization.

Capillary electrophoresis (CE), after being introduced several decades ago, has carved out a niche for itself in the field of analytical characterization of biopharmaceutical products. It does not only offer fast separation, high resolution in miniaturized format, but equally importantly represents an orthogonal separation mechanism to high-performance liquid chromatography. Therefore, it is not surprising that CE-based methods can be found in all major pharmacopoeias and are recommended for the analysis of biopharmaceutical products during process development, characterization, quality control, and release testing. Different separation formats of CE, such as capillary gel electrophoresis, capillary isoelectric focusing, and capillary zone electrophoresis are widely used for size and charge heterogeneity characterization as well as purity and stability testing of therapeutic proteins. Hyphenation of CE with MS is emerging as a promising bioanalytical tool to assess the primary structure of therapeutic proteins along with any impurities. In this review, we confer the latest developments in capillary electrophoresis, used for the characterization of critical quality attributes of biopharmaceutical products covering the past 6 years (2015-2021). Monoclonal antibodies, due to their significant share in the market, have been given prioritized coverage.

Cole-Cole modeling of real-time capacitance data for estimation of cell physiological properties in recombinant Escherichia coli cultivation.

Real-time estimation of physiological properties of the cell during recombinant protein production would ensure enhanced process monitoring. In this study, we explored the application of dielectric spectroscopy to track the fed-batch phase of recombinant Escherichia coli cultivation for estimating the physiological properties, namely, cell diameter and viable cell concentration (VCC). The scanning capacitance data from the dielectric spectroscopy were pre-processed using moving average. Later, it was modeled through a nonlinear theoretical Cole-Cole model and further solved using a global evolutionary genetic algorithm (GA). The parameters obtained from the GA were further applied for the estimation of the aforementioned physiological properties. The offline cell diameter and cell viability data were obtained from particle size analyzer and flow cytometry measurements to validate the Cole-Cole model. The offline VCC was calculated from the cell viability % from flow cytometry data and dry cell weight concentration. The Cole-Cole model predicted the cell diameter and VCC with an error of 1.03% and 7.72%, respectively. The proposed approach can enable the operator to take real-time process decisions to achieve desired productivity and product quality.