Biologics have changed the expectations placed on quality control laboratories. Unlike small molecules, biologics do not present as single, static structures. They exist as heterogeneous populations, shaped by production conditions, molecular complexity, and subtle variations that can affect safety, efficacy, and release decisions.
In a recent episode of Concentrating on Chromatography, produced in collaboration with Separation Science, host David Oliva spoke with Colette Quinn, Senior Director within the Biologics Business at Waters Corporation, about how QA/QC has evolved as biologics have become more prominent in pharmaceutical development and manufacturing.
Quinn describes QC as far more than a final checkbox. For biologics, QC teams must understand what the data reveals about the molecule, the process, and the confidence behind release decisions.
Why Biologics QC Is Different
Small molecule QC often starts with a defined structure. Analysts know the expected molecule, chirality, excipients, and test conditions. Biologics create a different challenge.
Quinn notes that biologics are “heterogeneous by nature.” A single molecule in solution may differ from the molecule next to it. QC testing therefore focuses on populations, distributions, glycosylation patterns, aggregation, higher-order structure, and other critical quality attributes.
That complexity changes the role of the QC lab. Instead of asking whether a molecule matches one fixed structure, analysts often ask whether a population fits an accepted model. This makes statistical understanding, orthogonal methods, and data interpretation essential.
LC Remains Foundational in Biologics QC
Liquid chromatography continues to play a central role in biologics testing. Quinn describes LC as foundational, noting its value across many critical quality attributes in biosimilars and innovator therapeutics.
Different LC modes support different QC needs. Size exclusion chromatography can flag aggregation and high molecular weight species. Ion exchange can support charge variant analysis. Reversed-phase LC, often paired with high-resolution mass spectrometry, can help characterize complex molecules and post-translational modifications.
As biologics become more complex, Quinn points to growing interest in 2D-LC, particularly for conjugated species, bispecifics, multispecifics, and antibody-drug conjugates. These molecules can combine hydrophobic and hydrophilic regions, multiple binding sites, and complex structural features that require more than one separation mode.
Advanced Detection Adds Confidence
LC can reveal when something looks unusual, but it may not explain what sits under a peak or shoulder. Quinn emphasizes that downstream detection can provide the added information needed to understand hidden complexity.
High-resolution mass spectrometry supports identity testing, peptide mapping, intact mass analysis, and characterization of post-translational modifications. It also becomes more important as biologics expand into oligonucleotides, conjugates, radioligands, lipid nanoparticles, and other advanced modalities.
Multi-angle light scattering provides another orthogonal layer. Quinn highlights its value for larger species such as lipid nanoparticles, virus-like particles, conjugated vaccines, and high molecular weight species. In QC, this can help distinguish an oligomer from an aggregate and clarify whether a co-eluting species represents a true product-related concern.
The Harder Questions Behind Critical Quality Attributes
Some of the most difficult biologics QC questions involve high molecular weight species. A chromatogram may show material eluting before the monomer or dimer, but QC teams need more than a label. They need to quantify and identify what that material represents.
For molecules such as GLP-1-related therapies and other complex biologics, Quinn notes that analysts may need to determine whether high molecular weight species represent oligomers, aggregates, or other forms. Light scattering can help measure molecular weight under a peak and support more confident interpretation.
Co-elution creates another challenge. A contaminant may share a similar elution profile, even with a different molecular weight or structure. In these cases, relying on optical detection alone can leave too much uncertainty.
Automation, Data Integrity, and Real-Time Release
Automation has a growing role in QC, though Quinn frames it as both a hardware and software issue. Manual steps such as pipetting, dilution, and additive preparation can introduce variability. Automated workflows can help reduce that risk.
Software automation may prove just as important. Quinn points to automated system checks, quality flags, and tools that help analyze large data sets. As QC labs use more orthogonal techniques, data often sits across multiple systems. Bringing that information together could help teams monitor molecules, methods, instruments, and environmental factors with greater consistency.
Quinn identifies data integrity as the top priority for any QC lab. Reproducibility followed closely, particularly across instruments, analysts, and sites. She also stresses the importance of education. QC scientists need to understand what the data means, not just whether a result passes or fails.
Designing Methods with QC in Mind
Transferring methods from development into QC can create delays when methods were not built for routine use. Quinn describes method transfer as a major burden and noted that it can take months to test, qualify, and move a method into QC.
Quality by design and method lifecycle management can reduce downstream friction. Teams need to stress methods early, understand variables that may affect pass/fail criteria, and choose software that can support both development and QC environments.
Standardized workflows may offer one of the strongest returns. Quinn highlights their ability to reduce variability and save time.
Skills for the Next Generation of QC Scientists
Analytical fundamentals still matter. Future QC scientists need to understand LC, mass spectrometry, statistics, and core measurement principles.
Quinn argues that data literacy has become just as important. As automation, machine learning, and larger data sets enter QC environments, scientists need to interpret complex outputs and connect insights from multiple instruments.
Her advice for labs considering advanced techniques is to start with the scientific decision they struggle to make. If current methods require assumptions, orthogonal analytical technology may help reduce risk and improve confidence.
The Future of Biologics QC
Quinn expects QC to move toward faster, more informed decisions. Machine learning, centralized data, and deeper data lakes could help connect information from multiple instruments and support movement toward real-time evaluation or real-time release.
But advanced tools still need accessible workflows. Quinn pushes back on the idea that techniques such as high-resolution mass spectrometry or light scattering belong only in development. If those tools provide critical insight into a molecule, QC labs may need them downstream as well.
For biologics, the product’s complexity demands a QC mindset built around curiosity, reproducibility, and confidence. LC may raise the flag. Orthogonal detection can explain the signal. Data literacy can help scientists decide what it means.
Learn More:
- Explore the Concentrating On Chromatography podcast to dive into the frontiers of chromatography, mass spectrometry, and sample preparation with host David Oliva.
Connect with Colette:
- LinkedIn: Colette Quinn
