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Monoclonal antibodies (mAbs) are often engineered at the sequence level for improved clinical performance yet are rarely evaluated prior to candidate selection for their “developability” characteristics, namely expression, which can necessitate additional resource investments to improve the manufacturing processes for problematic mAbs. A strong relationship between primary sequence and expression has emerged, with slight differences in amino acid sequence resulting in titers differing by up to an order of magnitude. Previous work on these “difficult‐to‐express” (DTE) mAbs has shown that these phenotypes are driven by post‐translational bottlenecks in antibody folding, assembly, and secretion processes. However, it has been difficult to translate these findings across cell lines and products. This work presents a systematic approach to study the impact of sequence variation on mAb expression at a larger scale and under more industrially relevant conditions. The analysis found 91 mutations that decreased transient expression of an IgG₁κ in Chinese hamster ovary (CHO) cells and revealed that mutations at inaccessible residues, especially those leading to decreases in residue hydrophobicity, are not favorable for high expression. This workflow can be used to better understand sequence determinants of mAb expression to improve candidate selection procedures and reduce process development timelines.

 

Chinese hamster ovary (CHO) cells are essential to biopharmaceutical manufacturing and production instability, the loss of productivity over time, is a long‐standing challenge in the industry. Accurate prediction of cell line stability could enable efficient screening to identify clones suitable for manufacturing saving significant time and costs. DNA repair genes may offer biomarkers to address this need. In this study, over 40 cell lines representing various host lineages from three companies/organizations were evaluated for expression of five DNA repair genes (Fam35a,Lig4,Palb2,Pari, andXrcc6). Expression measured in cells with less than 30 population doubling levels (PDLs) was correlated to stability profiles at 60+ PDL. Principal component analysis identified markers which separate stable and unstable CHO‐DG44 cell lines. Notably, two genes,Lig4andXrcc6, showed higher expression in unstable CHO‐DG44 cell lines with copy number loss identified as the mechanism of production instability. Expression levels across all cell ages showed lower DNA repair gene expression was associated with increased cell age. Collectively, DNA repair genes provide critical insight into long‐term behavior of CHO cells and their expression levels have potential to predict cell line stability in certain cases.

 

This review describes key milestones related to the production of biopharmaceuticals—therapies manufactured using recombinant DNA technology. The market for biopharmaceuticals has grown significantly since the first biopharmaceutical approval in 1982, and the scientific maturity of the technologies used in their manufacturing processes has grown concomitantly. Early processes relied on established unit operations, with research focused on process scale-up and improved culture productivity. In the early 2000s, changes in regulatory frameworks and the introduction of Quality by Design emphasized the importance of developing manufacturing processes to deliver a desired product quality profile. As a result, companies adopted platform processes and focused on understanding the dynamic interplay between product quality and processing conditions. The consistent and reproducible manufacturing processes of today's biopharmaceutical industry have set high standards for product efficacy, quality, and safety, and as the industry continues to evolve in the coming decade, intensified processing capabilities for an expanded range of therapeutic modalities will likely become routine. 

Efforts to leverage clustered regularly interspaced short palindromic repeats/CRISPR‐associated protein 9 (CRISPR/Cas9) for targeted genomic modifications in mammalian cells are limited by low efficiencies and heterogeneous outcomes. To aid method optimization, we developed an all‐in‐one reporter system, including a novel superfolder orange fluorescent protein (sfOrange), to simultaneously quantify gene disruption, site‐specific integration (SSI), and random integration (RI). SSI strategies that utilize different donor plasmid formats and Cas9 nuclease variants were evaluated for targeting accuracy and efficiency in Chinese hamster ovary cells. Double‐cut and double‐nick donor formats significantly improved targeting accuracy by 2.3–8.3‐fold and 19–22‐fold, respectively, compared to standard circular donors. Notably, Cas9‐mediated donor linearization was associated with increased RI events, whereas donor nicking minimized RI without sacrificing SSI efficiency and avoided low‐fidelity outcomes. A screen of 10 molecules that modulate the major mammalian DNA repair pathways identified two inhibitors that further enhance targeting accuracy and efficiency to achieve SSI in 25% of transfected cells without selection. The optimized methods integrated transgene expression cassettes with 96% efficiency at a single locus and with 53%–55% efficiency at two loci simultaneously in selected clones. The CRISPR‐based tools and methods developed here could inform the use of CRISPR/Cas9 in mammalian cell lines, accelerate mammalian cell line engineering, and support advanced recombinant protein production applications.

 

Glycosylation is a critical quality attribute of monoclonal antibody (mAb) therapeutics. Hydrophilic interaction liquid chromatography-mass spectrometry (HILIC-MS) is an invaluable technology for the characterization of protein glycosylation. HILIC/MS-based glycan analysis relies on the library search using Glucose Units (GU) and accurate mass (AM) as the primary search parameters for identification. However, GU-based identifications are gradient-dependent and are not suitable for applications where separation gradients need to be optimized to analyze complex samples or achieve higher throughput. Additionally, the workflow requires calibration curves (using dextran ladder) to be generated for each analysis campaign, which in turn, are used to derive the GU values of the separated glycan species. To overcome this limitation, we employed a two-step strategy for targeted glycan analysis of a mAb expressed in Chinese Hamster Ovary (CHO) cells. The first step is to create a custom library of the glycans of interest independent of GU values (thereby eliminating the need for a calibration curve) and instead uses AM and retention time (RT) as the primary search variables. The second step is to perform targeted glycan screening using the custom-built library. The developed workflow was applied for targeted glycan analysis of a mAb expressed in CHO for 1) cell line selection 2) characterizing the day-wise glycan evolution in a model mAb during a fed-batch culture, 3) assessing the impact of different media conditions on glycosylation, and 4) evaluating the impact of two different process conditions on glycosylation changes in a model mAb grown in a bioreactor. Taken together, the data presented in this study provides insights into the sources of glycan heterogeneity in a model mAb that are seen during its commercial manufacturing. 

To select the most complete, continuous, and accurate assembly for an organism of interest, comprehensive quality assessment of assemblies is necessary. We present a novel tool, called Evaluation of De Novo Assemblies (EvalDNA), which uses supervised machine learning for the quality scoring of genome assemblies and does not require an existing reference genome for accuracy assessment.

EvalDNA calculates a list of quality metrics from an assembled sequence and applies a model created from supervised machine learning methods to integrate various metrics into a comprehensive quality score. A well-tested, accurate model for scoring mammalian genome sequences is provided as part of EvalDNA. This random forest regression model evaluates an assembled sequence based on continuity, completeness, and accuracy, and was able to explain 86% of the variation in reference-based quality scores within the testing data. EvalDNA was applied to human chromosome 14 assemblies from the GAGE study to rank genome assemblers and to compare EvalDNA to two other quality evaluation tools. In addition, EvalDNA was used to evaluate several genome assemblies of the Chinese hamster genome to help establish a better reference genome for the biopharmaceutical manufacturing community. EvalDNA was also used to assess more recent human assemblies from the QUAST-LG study completed in 2018, and its ability to score bacterial genomes was examined through application on bacterial assemblies from the GAGE-B study.

EvalDNA enables scientists to easily identify the best available genome assembly for their organism of interest without requiring a reference assembly. EvalDNA sets itself apart from other quality assessment tools by producing a quality score that enables direct comparison among assemblies from different species.

 

Chinese hamster ovary (CHO) cells are the primary mammalian cell lines utilized to produce monoclonal antibodies (mAbs). The upsurge in biosimilar development and the proven health benefits of mAb treatments reinforces the need for innovative methods to generate robust CHO clones and enhance production, while maintaining desired product quality attributes. Among various product titer-enhancing approaches, the use of histone deacetylase inhibitors (HDACis) such as sodium butyrate (NaBu) has yielded promising results. The titer-enhancing effect of HDACi treatment has generally been observed in lower producer cell lines but those studies are typically done on individual clones. Here, we describe a cell line development (CLD) platform approach for creating clones with varying productivities. We then describe a method for selecting an optimal NaBu concentration to evaluate potential titer-enhancing capabilities in a fed-batch study. Finally, a method for purifying the mAb using protein A chromatography, followed by glycosylation analysis using mass spectrometry, is described. The proposed workflow can be applied for a robust CLD process optimization to generate robust clones, enhance product expression, and improve product quality attributes. 

The ambr250 high-throughput bioreactor platform was adopted to provide a highly-controlled environment for a project investigating genome instability in Chinese hamster ovary (CHO) cells, where genome instability leads to lower protein productivity. Development of the baseline (control) and stressed process conditions highlighted the need to control critical process parameters, including the proportional, integral, and derivative (PID) control loops. Process parameters that are often considered scale-independent, include dissolved oxygen (DO) and pH; however, these parameters were observed to be sensitive to PID settings. For many bioreactors, control loops are cascaded such that the manipulated variables are adjusted concurrently. Conversely, for the ambr250 bioreactor system, the control levels are segmented and implemented sequentially. Consequently, each control level must be tuned independently, as the PID settings are independent by control level. For the CHO cell studies, it was observed that initial PID settings did not resulted in a robust process, which was observed as elevated lactate levels; which was caused by the pH being above the setpoint most of the experiment. After several PID tuning iterations, new PID settings were found that could respond appropriately to routine feed and antifoam additions. Furthermore, these new PID settings resulted in more robust cell growth and increased protein productivity. This work highlights the need to describe PID gains and manipulated variable ranges, as profoundly different outcomes can result from the same feeding protocol. Additionally, improved process models are needed to allow process simulations and tuning. Thus, these tuning experiments support the idea that PID settings should be fully described in bioreactor publications to allow for better reproducibility of results.