Large-Scale Purification and Characterization of Recombinant Receptor-Binding Domain (RBD) of SARS-CoV-2 Spike Protein Expressed in Yeast.

SARS-CoV-2 spike protein is an essential component of numerous protein-based vaccines for COVID-19. The receptor-binding domain of this spike protein is a promising antigen with ease of expression in microbial hosts and scalability at comparatively low production costs. This study describes the production, purification, and characterization of RBD of SARS-CoV-2 protein, which is currently in clinical trials, from a commercialization perspective. The protein was expressed in Pichia pastoris in a large-scale bioreactor of 1200 L capacity. Protein capture and purification are conducted through mixed-mode chromatography followed by hydrophobic interaction chromatography. This two-step purification process produced RBD with an overall productivity of ~21 mg/L at >99% purity. The protein's primary, secondary, and tertiary structures were also verified using LCMS-based peptide mapping, circular dichroism, and fluorescence spectroscopy, respectively. The glycoprotein was further characterized for quality attributes such as glycosylation, molecular weight, purity, di-sulfide bonding, etc. Through structural analysis, it was confirmed that the product maintained a consistent quality across different batches during the large-scale production process. The binding capacity of RBD of spike protein was also assessed using human angiotensin-converting enzyme 2 receptor. A low binding constant range of KD values, ranging between 3.63 × 10-8 to 6.67 × 10-8, demonstrated a high affinity for the ACE2 receptor, revealing this protein as a promising candidate to prevent the entry of COVID-19 virus.

Mass spectrometry-based glycoprofiling of biopharmaceuticals by using an automated data processing tool: SimGlycan®.

The therapeutic and immunological properties of biopharmaceuticals are governed by the glycoforms contained in them. Thus, bioinformatics tools capable of performing comprehensive characterization of glycans are significantly important to the biopharma industry. The primary structural elucidation of glycans using mass spectrometry is tricky and tedious in terms of spectral interpretation. In this study, the biosimilars of a therapeutic monoclonal antibody and an Fc-fusion protein with moderate and heavy glycosylation, respectively, were employed as representative biopharmaceuticals for released glycan analysis using liquid chromatography-tandem mass spectrometry instead of conventional mass spectrometry-based analysis. SimGlycan® is a software with proven ability to process tandem MS data for released glycans could identify eight additional glycoforms in Fc-fusion protein biosimilar, which were not detected during mass spectrometry analysis of released glycans or glyco-peptide mapping of the same molecule. Thus, liquid chromatography-tandem mass spectrometry analysis of released glycans not only complements conventional liquid chromatography-mass spectrometry-based glycan profiling but can also identify additional glycan structures that may otherwise be omitted during conventional liquid chromatography-tandem mass spectrometry based analysis of mAbs. The mass spectrometry data processing tools, such as PMI Byos™, SimGlycan® , etc., can display pivotal analytical capabilities in automated liquid chromatography-mass spectrometry and liquid chromatography-tandem mass spectrometry-based glycan analysis workflows, especially for high-throughput structural characterization of glycoforms in biopharmaceuticals.

Development and optimization of a LC-MS based multi-attribute method (MAM) workflow for characterization of therapeutic Fc-fusion protein.

The growing complexity of novel biopharmaceutical formats, such as Fc-fusion proteins, in increasingly competitive environment has highlighted the need of high-throughput analytical platforms. Multi-attribute method (MAM) is an emerging analytical technology that utilizes liquid chromatography coupled with mass spectrometry to monitor critical quality attributes (CQAs) in biopharmaceuticals. MAM is intended to supplement or replace the conventional chromatographic and electrophoretic approaches used for quality control and drug release purpose. In this investigation, we have developed an agile sample preparation approach for deploying MAM workflow for a complex VEGFR-targeted therapeutic Fc-fusion protein. Initially, a systematic time course evaluation of tryptic digestion step was performed to achieve maximum amino acid sequence coverage of >96.5%, in a short duration of 2 h, with minimum assay artifacts. This approach facilitated precise identification of five sites of N-glycosylation with successful monitoring of other CQAs such as deamidation, oxidation, etc. Subsequently, the developed MAM workflow with suitable tryptic digestion time was qualified according to the International council for harmonisation (i.e. ICH) Q2R1 guidelines for method validation. Post-validation, the analytical workflow was also evaluated for its capability to identify unknown moieties, termed as 'New Peak Detection' (i.e. NPD), and assess fold change between the reference and non-reference samples, in a representative investigation of pH stress study. The study, thus, demonstrated the suitability of the MAM workflow for characterization of heavily glycosylated Fc-fusion proteins. Moreover, its NPD feature could offer an all-encompassing view if applied for forced degradation and stability studies.

FcγRIIIA affinity chromatography complements conventional functional characterization of rituximab.

Analytical and functional characterization of batches of biologics/biosimilar products are imperative towards qualifying them for pre-clinical and clinical investigations. Several orthogonal strategies are employed to characterize the functional attributes of these drugs. However, the use of conventional techniques for online monitoring of functional attributes is not feasible. Liquid chromatography is one of the crucial unit operations during the downstream processing of biopharmaceuticals. In this work, we have demonstrated the utility of FcγRIIIA affinity chromatography as an independent quantitative functional characterization tool. FcγRIIIA affinity chromatography aided in sequential elution of Rituximab glycoform mixtures, based on varying levels of galactosylation, and thereby the affinity for the receptor protein. The predominant glycans present in the three Rituximab glycoform mixture peaks were G0F, G1F, and G2F, respectively. Dissociation rate constants were derived from the chromatographic elution profiles by the peak profiling method, for the control and glucose stress conditions. The glucose stress conditions did not result in unfavorable binding kinetics of Rituximab and FcγRIIIA. The dissociation rate constants of the glycoform mixture 2, predominantly consisting of G1F, were similar to the dissociation rate constants obtained by surface plasmon resonance. Moreover, the glycosylation profiles obtained from chromatographic estimation can be corroborated with the ADCC activity. However, the ex vivo ADCC reporter assay indicated that there was an increase in the effector activity with increasing glucose stress. Thus, FcγRIIIA affinity chromatography permitted three independent assessments via a single analysis. Such approaches can be utilized as potential process analytical technology (PAT) tools in the biosimilar development process.

Exploring the potential of machine learning for more efficient development and production of biopharmaceuticals.

Principles of Industry 4.0 direct us to predict how pharmaceutical operations and regulations may exist with automation, digitization, artificial intelligence (AI), and real time data acquisition. Machine learning (ML), a sub-discipline of AI, involves the use of statistical tools to extract the desired information either through understanding the underlying patterns in the information or by development of mathematical relationships among the critical process parameters (CPPs) and critical quality attributes (CQAs) of biopharmaceuticals. ML is still in its infancy for directly supporting the quality-by-design based development and manufacturing of biopharmaceuticals. However, adoption of ML-based models in place of conventional multi-variate-data-analysis (MVDA) is increasing with the accumulation of large-scale data. This has been majorly contributed by the real-time monitoring of process variables and quality attributes of products through the implementation of process analytical technology in biopharmaceutical manufacturing. All aspects of healthcare, from drug design to product distribution, are complex and multidimensional. Thus, ML-based approaches are being applied to achieve sophistication, accuracy, flexibility and agility in all these areas. This review discusses the potential of ML for addressing the complex issues in diverse areas of biopharmaceutical development, such as biopharmaceuticals design and assessment of early stage development, upstream and downstream process development, analysis, characterization and prediction of post-translational modifications (PTMs), formulation, and stability studies. Moreover, the challenges in acquisition, cleaning and structuring the bioprocess data, which is one of the major hurdles in implementation of ML in biopharma industry, have also been discussed. Regulatory perspectives on implementation of AI/ML in the biopharma sector have also been briefly discussed. This article is a bird's eye view on the recent developments and applications of ML in overcoming the challenges for adopting "Industry - 4.0" in the biopharma industry.

Characterization of outcomes of amino acid modifications using a combinatorial approach to reveal physical and structural perturbations: A case study using trastuzumab biosimilar.

Biopharmaceuticals, such as monoclonal antibodies, are considered as life-saving drugs for autoimmune diseases, cancer and infectious diseases. However, biotherapeutics tend to undergo chemical degradation during various stages of manufacturing. The conditions of chemical degradation, along with the physical degradation pathways, have a direct influence on the overall stability, safety and efficacy of these therapeutics. While site-specific chemical changes have been well-explored and investigated using various analytical approaches, the resulting conformational and structural changes have not been much studied. Thus, we explored various biophysical techniques for assessing the influence of three representatives forced degradation conditions viz. oxidation, deamidation, and glycation, in a model therapeutic trastuzumab biosimilar. The site-specific modifications caused by these stress conditions were analysed using high resolution mass spectrometry. While their thermodynamic and conformational consequences were investigated by using differential scanning colorimetry (Nano-DSC), circular dichroism (CD) spectroscopy, analytical ultracentrifugation (AUC), and dynamic light scattering (DLS). The investigated stress conditions resulted in reduced thermodynamic stability of mAb, as confirmed using Nano-DSC. Secondary structure analysis performed with CD spectroscopy indicated detectable structural alterations in the beta sheets of stressed samples. DLS and SV-AUC studies demonstrated an enhanced level of aggregation and fragmentation in presence of all stress conditions. Thus, the biophysical analytical toolkits, when used simultaneously, could offer deeper insights into the subtle conformational changes that result from site-specific chemical modifications in mAbs. Hence, these analytical approaches may serve as significant additions to the battery of techniques used for forced degradation analysis of biopharmaceuticals.

Advanced strategies in glycosylation prediction and control during biopharmaceutical development: Avenues toward industry 4.0.

Glycosylation has been shown to define the safety and efficacy of biopharmaceuticals, thus classified as a critical quality attribute. However, controlling glycan heterogeneity has always been a major challenge owing to the multivariate factors that govern the glycosylation process. Conventional approaches for controlling glycosylation such as gene editing and metabolic control have succeeded in obtaining desired glycan profiles in accordance with the Quality by Design paradigm. Nonetheless, the development of smart algorithms and omics-enabled complete cell characterization has made it possible to predict glycan profiles beforehand, and manipulate process variables accordingly. This review thus discusses the various approaches available for control and prediction of glycosylation in biopharmaceuticals. Further, the futuristic goal of integrating such technologies is discussed in order to attain an automated and digitized continuous bioprocess for control of glycosylation. Given, control of a process as complex as glycosylation requires intense monitoring intervention, we examine the current technologies that enable automation. Finally, we discuss the challenges and the technological gap that currently limits incorporation of an automated process in routine bio-manufacturing, with a glimpse into the economic bearing.

A comparison between analytical approaches for molecular weight estimation of proteins with variable levels of glycosylation.

Biotherapeutics, such as mAbs and fusion proteins, are a major and rapidly growing class of pharmaceuticals. Majority of the biopharmaceuticals are glycoproteins, wherein about 1 to 30% of their molecular weight (MW) are contributed by the glycans. Determination of MW of heavily glycosylated proteins, such as Fc-fusion proteins, is seriously hampered by the physicochemical characteristics and heterogeneity of the attached carbohydrates. Glycosylation influences the expected size of the glycoprotein, which leads to disproportionate MW estimation, in size-dependent methods. Hence, in this study, we have demonstrated the advantages and limitations of four widely used MW estimation techniques for three proteins having varying levels of glycosylation. It was proven that glycosylation had least impact on MW determination by SEC-MALS and SV-AUC. However, MW estimation by LC-MS and SDS-PAGE was extensively hampered by the degree of glycosylation. It is, thus, essential to consider the structural characteristics of proteins while selecting a technique for determining their MW.