Biotechnology is often perceived as a field exclusively for those with a background in biology. This overlooks the diverse and interdisciplinary nature of the industry, where expertise in chemistry, physics, and engineering can provide a significant competitive advantage. This is especially true for specialized fields like glycoanalytics and glycoengineering, which operate at the intersection of traditional sciences and cutting-edge biopharma.

Róisín O'Flaherty, a leading expert in glycoanalytics and glycoengineering, shared her unique journey and deep insights into the biopharma sector. Her career story and technical expertise illustrate how a non-traditional background can be a powerful asset in tackling the complex challenges of bioprocess development and manufacturing.

This concept is discussed in greater detail with Róisín O'Flaherty in an episode of the Smart Biotech Scientist Podcast, hosted by David Brühlmann, founder of Brühlmann Consulting.

A Serendipitous Career Path: From Physics to Biopharma

O'Flaherty's academic journey started with an unexpected focus. With degrees in chemistry, physics, mathematical physics, and mathematics, she initially planned on a career as a physicist. However, her passion for chemistry blossomed, leading her to pursue a Ph.D. in organic synthesis. It was during this time that she attended a talk by Nobel laureate Carolyn Bertozzi, a renowned glycoscientist whose work on the interface of chemistry and biology profoundly inspired her. This pivotal moment set her on a new trajectory.

After her Ph.D., Róisín leveraged her engineering and mathematics background to work in the semiconductor industry at Intel. While the experience was valuable, she missed the world of research. A serendipitous job opening at the National Institute for Bioprocessing Research and Training (NIBRT) sought someone with a background in both robotics and glycoscience. Her diverse skill set, acquired from different fields, made her a perfect fit. 

She began her career in biopharma as a trained chemist with a strong foundation in engineering, learning the trade of cell technology and bioprocessing on the job. This non-traditional path highlights the value of cross-disciplinary expertise in solving problems within the biopharma sector.

Addressing a Core Challenge: Reproducibility in Biomanufacturing

Drawing on her experience in academia and industry, Róisín identifies reproducibility as the most significant challenge in bioprocess development and manufacturing. She uses the analogy of a "butterfly effect," where even a slight variation in an early step of a multi-step process can lead to significant deviations downstream. This high variability makes it difficult to maintain products within tight quality controls and specifications.

To overcome this, she advocates for the widespread adoption of automation and the establishment of "exact conditions" in manufacturing. By reducing human error and ensuring consistency, automation can significantly narrow the margin of error, leading to more reliable and reproducible results. This is a crucial factor in ensuring product safety and efficacy and meeting regulatory requirements.

Glycoanalytics: Unpacking the Critical Quality Attribute

The core of Róisín's work lies in glycoanalytics, the study of glycans—sugars attached to proteins. These sugars are considered a critical quality attribute (CQA) for biopharmaceutical products, particularly for monoclonal antibodies and fusion proteins. The reason for their importance is that glycans directly influence the protein's function, structure, and clinical efficacy.

Róisín explains that glycans can be immunogenic (harmful to humans), but they can also be strategically leveraged to enhance the efficacy of biotherapeutics. By understanding and controlling glycosylation, scientists can improve a drug's effectiveness and prolong its half-life, potentially enabling lower and safer doses.

Glycoanalytics can be broadly categorized into three main approaches:

• Native Glycoprotein Analysis: This method studies the intact glycoprotein using mass spectrometry. While simple, it provides less detailed information about the specific glycan structures.
• Glycopeptide Analysis: Enzymes are used to break down the glycoprotein into smaller fragments (glycopeptides), which are then analyzed using mass spectrometry. This approach yields more detailed information about the glycosylation site.
• Released Glycan Analysis: Enzymes are used to completely remove sugars from the protein. Studying these released sugars provides the most comprehensive information and has been a cornerstone of the field since the 1980s. These methods are often used in combination to give a complete picture.

The most important thing is once you validate your assay and you check that the reproducibility is good using a series of technical replicates and things like that, known standards. That's the most important part. To get a good, robust, reproducible assay with small variation.

High-Throughput Analysis and the Importance of Sample Preparation

The choice of method in glycoanalytics depends heavily on the application. For Quality Control (QC) assays, the focus is on speed and cost-effectiveness. A simple, reproducible method, such as native glycoprotein analysis, is often sufficient. In contrast, for the structural analysis of unknown samples, a more extensive and time-consuming investigation is required. This involves using mass spectrometry, targeted ion searches, and enzymes to cleave specific sugars, thereby identifying their composition and the type of linkage. This process can take several months for a new molecule, but once characterized, it can be reduced to a five-minute run.

According to Róisín, the single most critical step in any glycoanalysis workflow is sample preparation. She emphasizes the principle of "garbage in, garbage out," stressing that a well-prepared, clean sample is essential for generating reliable and usable data. The use of cleanup steps and technologies, such as affinity chromatography, ensures the data is robust and reproducible.

Róisín's group has developed fully automated technologies that can process over 10,000 samples to handle the demands of modern bioprocessing. While automation has enabled high-throughput sample analysis, she notes that data interpretation is the most time-consuming part of the workflow. This is a significant bottleneck where she believes advanced tools are most needed.

The Future of Analytics: The Role of AI and Bioinformatics

Róisín sees the future of glycoanalytics as being driven by advancements in bioinformatics, system biology, and AI. She points out that the field of glycoscience has historically lagged behind genomics and proteomics because it is less template-driven, which has caused many scientists to shy away from it. However, the development of user-friendly tools and databases will make glycan analysis more accessible and widespread.

Looking ahead five years, she predicts a significant shift away from released glycan approaches towards glycopeptide analysis using mass spectrometry. She cites a study by the National Institute of Standards and Technology (NIST), which showed a significant increase in the use of mass spec in both R&D and routine manufacturing labs. The growing interest in technologies like ion mobility also suggests a trend towards more sophisticated and detailed analysis.

Chemoenzymatic Glycoengineering: Precision at the End of the Line

Róisín delves into chemoenzymatic glycoengineering, a powerful technique that involves using specific enzymes to modify a protein's glycan landscape after it has been produced. This "at the end of the process" approach is highly valuable for R&D labs, allowing them to create and test different variants with desirable attributes like enhanced ADCC (antibody-dependent cell-mediated cytotoxicity), CDC (complement-dependent cytotoxicity), or an increased half-life.

This approach is distinct from:

• Genetic Engineering: Modifying the protein at the DNA level to control glycosylation.
• Metabolomic Changes: Altering the cell culture environment to influence the glycan profile.

While genetic engineering is often the long-term solution for companies once desired features are identified, chemoenzymatic engineering offers a faster, more flexible way to test those features. Róisín notes that this technology is already being exploited by major players, such as Roche, and cites an example of Paleo and Pharmaceuticals using an enzyme-antibody fusion in Phase 2 clinical trials for cancer treatment.

Broadly speaking, there are three different ways that you could change the glycosylation. As we mentioned, changing the post translational modifications of proteins such as glycosylation influences the structure and the function. So it's very desirable to have certain features, e.g. enhanced ADCC or CDC or change the half life. So to that end, what we can do is we can glyco engineer the protein or your product to have your desirable attributes.

Limitations and Overcoming Hurdles

Despite its promise, chemoenzymatic glycoengineering faces several challenges, with cost being the most significant. The enzymes required are typically costly. Róisín suggests a solution: tethering enzymes to solid supports, allowing them to be reused and dramatically reducing costs.

Another major limitation is selectivity. While it's easy to clip off glycans on a released level, they are often protected on the protein, making it challenging to remove specific motifs selectively. This requires careful tweaking of reaction conditions and, in some cases, the engineering of smaller enzymes that can access these protected sites. 

Róisín explains that considerable effort is being invested in engineering new, smaller Fucosidase enzymes to create desirable a-fucosylated variants, which are highly sought after by companies.

The Combined Future: AI and Experimental Science

The discussion culminates with the increasing role of AI and machine learning in glycoengineering. As an experimentalist, Róisín's group performs the physical engineering and testing of proteins. However, she sees immense value in the predictive power of bioinformatic tools. She highlights a software called ReGlyco that can predict the 3D confirmation of a sugar after it's added to a protein structure, allowing scientists to predict its binding efficiency. In her view, this symbiotic relationship between computational and experimental approaches is the field's future.

Róisín concludes with a powerful takeaway: the role of scientists in the future will shift. Instead of spending years manually performing tasks, their time will be freed up by AI to focus on deeper thinking and critical analysis. Their primary responsibility will be to validate the accuracy of the models and ensure the integrity of the data. This change, although potentially unsettling, is inevitable and must be accepted. The most important thing, she says, is always to learn, constantly adapt, and move forward.

About Róisín O'Flaherty

Róisín O'Flaherty has been an Assistant Professor in the Department of Chemistry at Maynooth University since October 2020. She is a Principal Investigator in the Kathleen Lonsdale Institute for Human Health Research and leads the O'Flaherty Glycoscience group. 

She is a proud first-generation student and alumnus of MU. Beyond her scholarly pursuits, she embraces the joys of motherhood, balancing the demands of academia with the care of her two toddlers. She did her Bachelor of Science degree at MU and placed 1st in her class. She then pursued her PhD here, where she designed sugars as antibacterial agents in the Department of Chemistry. During her PhD, she was awarded an Australian Award to undertake a research secondment at the University of Melbourne. 

After completing her PhD, she worked as a Process Engineer at Intel before returning to pursue postdoctoral studies at NIBRT. There, she spent five years developing automated technologies to characterize sugars in biological samples, such as blood, as well as biotherapeutics like monoclonal antibodies. She is jointly responsible for the development of the first clinical diagnostic technology for classical galactosaemia (a rare congenital disorder) in Europe.

Connect with Róisín O'Flaherty on LinkedIn. 

David Brühlmann is a strategic advisor who helps C-level biotech leaders reduce development and manufacturing costs to make life-saving therapies accessible to more patients worldwide.

He is also a biotech technology innovation coach, technology transfer leader, and host of the Smart Biotech Scientist podcast—the go-to podcast for biotech scientists who want to master biopharma CMC development and biomanufacturing.  

Hear It From The Horse’s Mouth

Want to listen to the full interview? Go to Smart Biotech Scientist Podcast. 

Want to hear more? Do visit the podcast page and check out other episodes. 
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