Decoupling Protein Concentration and Aggregate Content Using Diffusion and Water NMR.

Protein-based biopharmaceutical drugs, such as monoclonal antibodies, account for the majority of the best-selling drugs globally in recent years. For bioprocesses, key performance indicators are the concentration and aggregate level for the product being produced. In water NMR (wNMR), the use of the water transverse relaxation rate [R2(1H2O)] has been previously used to determine protein concentration and aggregate level; however, it cannot be used to separate between them without using an additional technique. This work shows that it is possible to "decouple" these two key characteristics by recording the water diffusion coefficient [D(1H2O)] in conjunction with R2(1H2O), even in the event of overlap in either D(1H2O) or R2(1H2O). This method is demonstrated on three different systems, following appropriate D(1H2O) or R2(1H2O) calibration data acquisition for a protein of interest. Our method highlights the potential use of benchtop NMR as an at-line process analytical technique.

Enhanced harvest performance predictability through advanced multivariate data analysis of mammalian cell culture particle size distribution.

The industry's pursuit for higher antibody production has led to increased cell density cultures that impact the performance of subsequent product recovery steps. This increase in cell concentration has highlighted the critical role of solids concentration in centrifugation yield, while recent product degradation cases have shed light on the impact of cell lysis on product quality. Current methods for measuring solids concentration and cell lysis are not suited for early-stage high-throughput experimentation, which means that these cell culture outputs are not well characterized in early process development. This article describes a novel approach that leveraged the data from a widely-used automated cell counter (Vi-CELL™ XR) to accurately predict solids concentration and a common cell lysis indicator represented as lactate dehydrogenase (LDH) release. For this purpose, partial least squares (PLS) models were derived with k-fold cross-validation from the particle size distribution data generated by the cell counter. The PLS models showed good predictive potential for both LDH release and solids concentration. This novel approach reduced the time required for evaluating the solids concentration and LDH for a typical high-throughput cell culture system (with 48 bioreactors in parallel) from around 7 h down to a few minutes.

Computer-Aided Strategies for Determining the Amino Acid Composition of Medium for Chinese Hamster Ovary Cell-Based Biomanufacturing Platforms.

Chinese hamster ovary (CHO) cells are used for the production of the majority of biopharmaceutical drugs, and thus have remained the standard industry host for the past three decades. The amino acid composition of the medium plays a key role in commercial scale biologics manufacturing, as amino acids constitute the building blocks of both endogenous and heterologous proteins, are involved in metabolic and non-metabolic pathways, and can act as main sources of nitrogen and carbon under certain conditions. As biomanufactured proteins become increasingly complex, the adoption of model-based approaches become ever more popular in complementing the challenging task of medium development. The extensively studied amino acid metabolism is exceptionally suitable for such model-driven analyses, and although still limited in practice, the development of these strategies is gaining attention, particularly in this domain. This paper provides a review of recent efforts. We first provide an overview of the widely adopted practice, and move on to describe the model-driven approaches employed for the improvement and optimization of the external amino acid supply in light of cellular amino acid demand. We conclude by proposing the likely prevalent direction the field is heading towards, providing a critical evaluation of the current state and the future challenges and considerations.

Comprehensive Stress Stability Studies Reveal the Prominent Stability of the Liquid-Formulated Biotherapeutic Asymmetric Monovalent Bispecific IgG1 Monoclonal Antibody Format.

The developed asymmetric monovalent bispecific IgG1 or Duet monoclonal antibody (Duet mAb) has two distinct fragment antigen-binding region (Fab) subunits that target two different epitope specificities sequentially or simultaneously. The design features include unique engineered disulfide bridges, knob-into-hole mutations, and kappa and lambda chains to produce Duet mAbs. These make it structurally and functionally complex, so one expects challenging developability linked to instability, degradation of products and pathways, and limited reports available. Here, we have treated the product with different sources of extreme stress over a lengthy period, including varying heat, pH, photo stress, chemical oxidative stress, accelerated stress in physiological conditions, and forced glycation conditions. The effects of different stress conditions on the product were assessed using various analytical characterization tools to measure product-related substances, post-translational modifications (PTMs), structural integrity, higher-order disulfide linkages, and biological activity. The results revealed degradation products and pathways of Duet mAb. A moderate increase in size, charge, and hydrophobic variants, PTMs, including deamidation, oxidation, isomerization, and glycation were observed, with most conditions exhibiting biological activity. In addition, the characterization of fractionated charge variants, including deamidated species, showed satisfactory biological activity. This study demonstrated the prominent stability of the Duet mAb format comparable to most marketed mAbs.

Identification and quantification of chain-pairing variants or mispaired species of asymmetric monovalent bispecific IgG1 monoclonal antibody format using reverse-phase polyphenyl chromatography coupled electrospray ionization mass spectrometry.

Developing a knob-into-hole asymmetric bispecific IgG1 monoclonal antibody (mAb) poses manufacturing challenges due to the expression of chain pairing variants, also called mispaired species, in the desired product. The incorrect pairing of light and heavy chains could result in heterogeneous mispaired species of homodimers, heterodimers, light chain swapping, and low molecular weight species (LMWS). Standard chromatography, capillary electrophoretic, or spectroscopic methods poorly resolve these from the main variants. Here, we report a highly sensitive reverse-phase polyphenyl ultra-high-performance liquid chromatography (RP-UHPLC) method to accurately measure mispaired species of Duet mAb format, an asymmetric IgG1 bispecific mAb, for both process development and quality control analytical tests. Coupled with electrospray ionization mass spectrometry (ESI-MS), it enabled direct online characterization of mispaired species. This single direct assay detected diverse mispaired IgG-like species and LMWS. The method resolved eight disulfide bonds dissociated LMWS and three mispaired LMWS. It also resolved three different types of IgG-like mispaired species, including two homodimers and one heterodimer. The characterization and quantification simultaneously enabled the cell line selection that produces a lesser heterogeneity and lower levels of mispaired species with the desired correctly paired product. The biological activity assessment of samples with increased levels of these species quantified by the method exhibited a linear decline in potency with increasing levels of mispaired species in the desired product. We also demonstrated the utility of the technique for testing in-process intermediate materials to determine and assess downstream purification process capability in removing diverse mispaired IgG-like species and LMWS to a certain level during the downstream purification process. Our investigation demonstrates that adopting this method was vital in developing asymmetric bispecific mAb from the initial stage of cell line development to manufacturing process development. Therefore, this tool could be used in the control strategy to monitor and control mispaired species during manufacturing, thus improving the quality control of the final product.

Next-generation cell line selection methodology leveraging data lakes, natural language generation and advanced data analytics.

Cell line development is an essential stage in biopharmaceutical development that often lies on the critical path. Failure to fully characterise the lead clone during initial screening can lead to lengthy project delays during scale-up, which can potentially compromise commercial manufacturing success. In this study, we propose a novel cell line development methodology, referenced as CLD 4, which involves four steps enabling autonomous data-driven selection of the lead clone. The first step involves the digitalisation of the process and storage of all available information within a structured data lake. The second step calculates a new metric referenced as the cell line manufacturability index (MI CL) quantifying the performance of each clone by considering the selection criteria relevant to productivity, growth and product quality. The third step implements machine learning (ML) to identify any potential risks associated with process operation and relevant critical quality attributes (CQAs). The final step of CLD 4 takes into account the available metadata and summaries all relevant statistics generated in steps 1-3 in an automated report utilising a natural language generation (NLG) algorithm. The CLD 4 methodology was implemented to select the lead clone of a recombinant Chinese hamster ovary (CHO) cell line producing high levels of an antibody-peptide fusion with a known product quality issue related to end-point trisulfide bond (TSB) concentration. CLD 4 identified sub-optimal process conditions leading to increased levels of trisulfide bond that would not be identified through conventional cell line development methodologies. CLD 4 embodies the core principles of Industry 4.0 and demonstrates the benefits of increased digitalisation, data lake integration, predictive analytics and autonomous report generation to enable more informed decision making.

Data integrity within the biopharmaceutical sector in the era of Industry 4.0.

Data Integrity (DI) in the highly regulated biopharmaceutical sector is of paramount importance to ensure decisions on meeting product specifications are accurate and hence assure patient safety and product quality. The challenge of ensuring DI within this sector is becoming more complex with the growing amount of data generated given increasing adoption of process analytical technology (PAT), advanced automation, high throughput microscale studies, and managing data models created by machine learning (ML) tools. This paper aims to identify DI risks and mitigation strategies in biopharmaceutical manufacturing facilities as the sector moves towards Industry 4.0. To achieve this, the paper examines common DI violations and links them to the ALCOA+ principles used across the FDA, EMA, and MHRA. The relevant DI guidelines from the ISPE's GAMP5 and ISA-95 standards are also discussed with a focus on the role of validated computerised and automated manufacturing systems to avoid DI risks and generate compliant data. The paper also highlights the importance of DI whilst using data analytics to ensure the developed models meet the required regulatory standards for process monitoring and control. This includes a discussion on possible mitigation strategies and methodologies to ensure data integrity is maintained for smart manufacturing operations such as the use of cloud platforms to facilitate the storage and transfer of manufacturing data, and migrate away from paper-based records.

A decade in review: use of data analytics within the biopharmaceutical sector.

There are large amounts of data generated within the biopharmaceutical sector. Traditionally, data analysis methods labelled as multivariate data analysis have been the standard statistical technique applied to interrogate these complex data sets. However, more recently there has been a surge in the utilisation of a broader set of machine learning algorithms to further exploit these data. In this article, the adoption of data analysis techniques within the biopharmaceutical sector is evaluated through a review of journal articles and patents published within the last ten years. The papers objectives are to identify the most dominant algorithms applied across different applications areas within the biopharmaceutical sector and to explore whether there is a trend between the size of the data set and the algorithm adopted.

Multivariate Data Analysis Methodology to Solve Data Challenges Related to Scale-Up Model Validation and Missing Data on a Micro-Bioreactor System.

Multivariate data analysis (MVDA) is a highly valuable and significantly underutilized resource in biomanufacturing. It offers the opportunity to enhance understanding and leverage useful information from complex high-dimensional data sets, recorded throughout all stages of therapeutic drug manufacture. To help standardize the application and promote this resource within the biopharmaceutical industry, this paper outlines a novel MVDA methodology describing the necessary steps for efficient and effective data analysis. The MVDA methodology is followed to solve two case studies: a "small data" and a "big data" challenge. In the "small data" example, a large-scale data set is compared to data from a scale-down model. This methodology enables a new quantitative metric for equivalence to be established by combining a two one-sided test with principal component analysis. In the "big data" example, this methodology enables accurate predictions of critical missing data essential to a cloning study performed in the ambr15 system. These predictions are generated by exploiting the underlying relationship between the off-line missing values and the on-line measurements through the generation of a partial least squares model. In summary, the proposed MVDA methodology highlights the importance of data pre-processing, restructuring, and visualization during data analytics to solve complex biopharmaceutical challenges.

Gene transfection of HEK cells on supermacroporous polyacrylamide monoliths: a comparison of transient and stable recombinant protein expression in perfusion culture.

Transient and continuous recombinant protein expression by HEK cells was evaluated in a perfused monolithic bioreactor. Highly porous synthetic cryogel scaffolds (10 ml bed volume) were characterised by scanning electron microscopy and tested as cell substrates. Efficient seeding was achieved (94% inoculum retained, with 91-95% viability). Metabolite monitoring indicated continuous cell growth, and endpoint cell density was estimated by genomic DNA quantification to be 5.2 x 10(8), 1.1 x 10(9) and 3.5 x 10(10) at day 10, 14 and 18. Culture of stably transfected cells allowed continuous production of the Drosophila cytokine Spätzle by the bioreactor at the same rate as in monolayer culture (total 1.2mg at day 18) and this protein was active. In transient transfection experiments more protein was produced per cell compared with monolayer culture. Confocal microscopy confirmed homogenous GFP expression after transient transfection within the bioreactor. Monolithic bioreactors are thus a flexible and powerful tool for manufacturing recombinant proteins.