Streamlining Glycosylation Monitoring through Automation

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Biopharmaceutical analysis is one area in biopharma drug development that requires improvement, namely to manual processes that take up significant amounts of time and labor. Sample preparation remains a major bottleneck point to productivity because of the number of manual steps involved and, in some cases, the outdated technology used to ready samples for analysis.

Glycosylation monitoring is one aspect of biopharma analysis that falls under the strain of laborious manual processes. However, advances made in technology and approaches to automate the workflows for glycosylation monitoring offer quicker sample preparation and can cut down on overall time needed for sample analysis.

Importance of glycosylation monitoring

Because many recombinant biotherapeutics, such as monoclonal antibodies (mAbs), are glycoproteins, monitoring glycosylation activity is necessary for biopharma drug development. Bioprocessing requires precise control and monitoring of multiple parameters, including cell line stability, product yield, protein folding, and post-translational modifications (PTMs), specifies Ning Zhang, PhD, senior product marketing manager, Chemistry Technology Center, Waters Corporation.

Among the parameters of interest, the host cell’s biosynthesis of glycans is routinely monitored as a critical quality attribute (CQA) because glycan composition and structure are crucial for the biological or clinical activity of the drug. “Slight changes in manufacturing conditions can alter the glycosylation patterns of recombinant proteins and, consequently the biological activity, safety, stability, efficacy, and immunogenicity of the end drug product,” explains Zhang. The manufacturing of therapeutic glycoproteins, therefore, requires careful monitoring and characterization of their glycosylation profile to achieve consistent glycan composition and meet desired quality, clinical safety, and effectiveness targets.

“Protein glycosylation plays an important role in bioactivity, stability, biological half-life, immunogenicity, and pharmacokinetics of a biopharmaceutical,” confirms Radoslaw Kozak, PhD, head of Glycoprofiling at Ludger. Kozak explains that glycosylation is a PTM and, unlike transcription, is a non-template-driven enzymatic modification process. Glycosylation of proteins can therefore change with alterations to production conditions.

Glycosylation could be considered the “canary in the coalmine” for monitoring the consistency and reliability of a manufacturing process, adds Amy Claydon, PhD, Scientific Account Manager, Genedata. She explains that even slight changes to the bioreactor conditions during protein expression can lead to measurable differences in the glycosylation profile.

“The presence, or absence, of glycosylation modulates antibody activity by enhancing either antagonist (blocking, inhibiting) or agonist (activation) functions. Subsequently, glycans may impact the safety, efficacy, structure, and circulating half-life of biotherapeutics; for example, a mAb with no glycosylation will not fold correctly and so will be ineffective as a treatment. This effect on the immunogenicity of a therapeutic antibody increases the risks for patients,” says Claydon.

Claydon further explains that pharmacokinetics, especially clearance and circulating half-life, are also affected by N-glycosylation. For instance, higher levels of sialic acid-containing glycans can increase the circulating half-life because these glycans become more resistant to removal by hepatic asialoglycoprotein receptors.

Bottlenecks in productivity

Dealing with processes in sample preparation for glycosylation monitoring that currently depend on manual labor tends to be a bottleneck point in productivity. To start with, there are several analytical approaches used for the analysis and characterization of protein N-glycans during process development, notes Zhang.

One common approach involves the enzymatic release of N-glycan chains from the protein of interest and their derivatization with a fluorescent label. “A manual procedure, this conventional deglycosylation method requires the incubation of a glycoprotein sample for about one hour, while many researchers generally employ an overnight (16 hours) incubation. This is followed by a lengthy two-to-four-hour labeling step based on reductive amination reactions,” explains Zhang.

The analysis of released N-glycans is challenging because of the number of steps that are involved in preparing the glycoprotein samples and processing the released glycans to prepare them for analysis, adds Richard Gardner, PhD, lead scientist, Ludger. The release of peptide-N-glycosidase F (PNGase F) and the fluorescent labeling of N-glycans, followed by a cleanup, are necessary steps for N-glycans to be analyzed. Gardner points out that these steps are challenging and time consuming when performed manually, especially when the numbers of samples increase beyond a certain amount. “This makes it more difficult for the user to accurately pipette low volume solutions and to efficiently track which samples have been processed,” he states.

Each stage needed for optimizing sample preparation for N-glycan analysis requires on-going evaluation to determine effectiveness, says Claydon. This can subsequently lead to lengthy assay development times if rapid data analysis techniques are not available. “Of course, there are also numerous methods to implement automation (robotic liquid handling, for example) in sample preparation itself, but these often can highlight process inefficiencies further along the process too,” Claydon says.

Screening necessarily large numbers of samples can be time-consuming and monotonous, says Zhang. Furthermore, methods for glycan sample preparation and analysis must be robust and reproducible to ensure accuracy and consistency, she adds.

Alleviating bottlenecks

Automating different aspects of a workflow is one method to alleviate the bottlenecks caused by processes dependent on manual labor. Automated data analysis, for example, can improve many areas of glycosylation monitoring workflows, says Claydon. “When automated data analysis is employed, it can quickly facilitate understanding the effects of different sample preparation optimization steps—the impact of altering digestion conditions and so on. This, in turn, can lead to more rapid improvements to the digestion and labeling protocols during the method development process and an overall increase in assay throughput,” she states.

Bottlenecks often go “hand-in-hand”, Claydon also notes, and solving one bottleneck often leads to another in a different part of the process. “This is why we like to look at data solutions as part of facilitating the whole analytical workflow. In this example, we can easily predict that making sample preparation faster in some way (e.g., automated robotics) can create a lot more data that then needs to be analyzed. So, it is not a simple case of adding automation in just one part of the workflow that ultimately makes the challenges easier.”

Another instance of laborious manual work centers on the fact that many of these workflows have multiple pipetting steps in a range of volumes, which takes time. Automation can save time and allow for more reproducible liquid handling, says Zhang. In addition, automation allows multiple samples to be prepared in parallel, thus improving the throughput.

“Standardized sample preparation is essential to minimize user variations and pipetting errors and to ensure that glycan data is accurate and reproducible. Minimal user intervention—or none at all—allows users to stay away from the bench and work on documentation or setting up an instrument while preparing the samples,” Zhang emphasizes.

Gardner concurs that automation would alleviate this bottleneck by allowing a higher throughput of samples where user intervention is minimized. “Automation would improve the repeatability, reproducibility, and robustness of N-glycan processing by removing the user pipetting steps, thereby decreasing possible human error when transferring the small amounts of liquids that are used in these methods. One outcome would be a harmonized method that many users can operate that removes potential differences in the way users work and interpret SOPs [standard operating procedures],” he notes.

Ease of automation

Some technological advances have been helpful in easing the incorporation of automation in the glycosylation monitoring workflow. For instance, specialty sample preparation kits (GlycoWorks RapiFluor–MS, Waters) available commercially and an associated workflow can make fast work of de-glycosylation and labeling. This reduces sample preparation time from a day to less than one hour while enhancing the sensitivity for both fluorescent and mass spectrometry (MS) detections, notes Zhang. “Reagents and reaction conditions that come with these kits have been optimized to enable a fast and complete de-N-glycosylation of glycoproteins and subsequently the rapid and efficient labeling of released N-glycans. Following a HILIC [hydrophilic interaction liquid chromatography]–SPE [solid phase extraction] sample clean-up step, the samples are then ready for analysis by LC–MS [liquid chromatography–mass spectrometry],” Zhang says.

The main technological advance in the field of automation, notes Gardner, is the introduction of robotic liquid handling platforms. Gardner explains that the incorporation of robotic liquid handlers such as Hamilton’s Microlab STAR into glycosylation analysis workflows has allowed many of the processes to be automated, from the release of N-glycans using PNGase F to fluorescent labeling of N-glycans all the way through to preparation of samples for LC–MS analysis.

“Many larger robotic platforms including Hamilton’s Microlab STAR not only incorporate liquid handling and vacuum manifolds but also incorporate plate sealers, heater shakers, and the ability to move plates off deck for centrifuge steps. This ability to incorporate different lab equipment into a robotic workflow enables the move towards full automation of glycosylation analysis. Laboratories require an automated system that is simple to operate, scalable, and repeatable,” Gardner states. To facilitate the move to higher throughout and automated analysis, a range of products have been introduced onto the market that are compatible and adaptable to many robotic platforms, including a 96-sample recombinant PNGase F kit, a 96-well protein binding membrane cleanup plate, a 96-sample procainamide glycan labeling kit, and a procainamide cleanup plate (Ludger).

Meanwhile, Claydon says that automation is best thought of as an end-to-end process, and we must therefore consider how the introduction of automation in one area may move bottlenecks to another part of the overall workflow. “If one considers that an automated data analysis platform actually facilitates both upstream and downstream areas of the entire assay, then it can be a tool to help fully understand where bottlenecks may have moved to, and, in doing so, then accelerate optimization to reduce the time spent in other areas, if they do not significantly affect the end result. For example, long chromatography runs can be avoided if the challenges introduced by co-eluting peaks can be later simplified using software that can display the data two-dimensionally, and deconvolute them,” she states.

“The number one request we get is for help with automating the liquid handling steps, essentially the pipetting of sample preparation protocols,” adds Zhang. “Liquid handling devices now come in several forms including robots (Andrew+ Pipetting Robot, Waters) that automate many sample preparation steps including pipetting which, as everyone knows, is tedious.”

In addition, according to Zhang, robotic pipetting systems can prepare up to 48 samples in under 2.5 hours. Greater automation is achieved by adding connected devices for heating/cooling and a vacuum for sample clean up with SPE.

There are many ways that samples can be prepared for glycosylation analysis, according to Kozak, who explains that some samples may need filtering and/or buffer exchange while other samples may need an enrichment step to extract the glycoprotein to be analyzed. Technologies using 96-well plates that incorporate filters or protein capture technology have allowed this type of processing to happen on automated platforms, Kozak states.

Employing a fully automated liquid handling robot is one innovative approach for automating and streamlining routine sample preparation steps, Zhang adds. This improves both reproducibility and sample throughput. Another innovative approach is by means of a guided pipetting system (Andrew Alliance Pipette+, Waters) that features Bluetooth-connected “smart” pipettes connected to a personal computer or tablet via software for designing, sharing, and executing protocols with audit trails, she also notes.

Meanwhile, the ability to routinely monitor many glycopeptides independently using automated LC–MS analysis offers the ability to trend and compare various aspects of glycosylation, says Claydon. “If one were to observe a change in the sample-to-sample response for specific glycopeptides, for example, the signal for one glycopeptide remains stable but that of a different glycopeptide (or a combination of glycopeptides) gives a varying response, then site-specific information can be derived,” she states.

About the author

Feliza Mirasol is the science editor for BioPharm International.

Article Details

BioPharm International
Vol. 34, No. 12
December 2021
Pages: 28–29, 32

Citation

When referring to this article, please cite it as F. Mirasol, “Streamlining Glycosylation Monitoring through Automation,” BioPharm International 34 (12) 28–29, 32 (2021).