Biopharmaceutical processes are complex by nature. Upstream workflows involve raw materials, water, cell culture media, and bioreactors. Each of these sample types creates analytical challenges. Cell culture media, for example, contain carbohydrates, buffers, amino acids, minerals, vitamins, peptides, trace metals, and other components across very different concentration ranges. They are chemically, physically, and biologically unstable, and they can change during preparation, storage, and handling.
Downstream processing presents a different set of challenges. After harvest and purification, analytical attention shifts toward product-related quality attributes. In monoclonal antibody and other biologics workflows, scientists need to understand protein concentration, aggregation, stability, formulation behaviour, and changes that may occur during steps such as Protein A capture, low-pH viral inactivation, chromatography, concentration, and final formulation.
Traditional analytical methods are powerful, but they can be slow, expensive, sample-intensive, or difficult to transfer into real-time process monitoring. Dr. Ryder highlighted that the central challenge is not simply collecting data. It is collecting meaningful, reproducible, interpretable data from complex biological materials in a way that can support better process understanding.
A key part of Dr. Ryder’s work began with the analysis of cell culture media. His group evaluated multiple spectroscopic methods, including Raman, infrared, near-infrared, and fluorescence-based approaches. For highly complex media samples, intrinsic fluorescence proved especially valuable.
Intrinsic fluorescence is attractive in biopharmaceutical analysis because many biological samples naturally contain fluorescent molecules, including tryptophan and tyrosine residues in proteins. This means the method can be label-free, relatively inexpensive, and sustainable compared with techniques that require reagents, dyes, or complex sample preparation.
By collecting excitation-emission matrices, or EEMs, scientists can measure fluorescence across a broad excitation and emission space. The resulting three-dimensional spectral profile can be highly diagnostic. In simple terms, if the EEM shape changes, this often indicates that the chemical composition or molecular environment of the sample has changed.
This makes EEM spectroscopy useful for identifying cell culture media, comparing lot-to-lot variation, and in some cases linking media variability to process performance or final product yield. However, EEM data can also become complex to interpret, especially when many overlapping signals and photophysical effects are present.
To extend the capabilities of EEM-based analysis, Dr. Ryder’s group introduced polarised measurements. Polarised intrinsic emission, or PIE, adds another layer of information by measuring different polarisation states of the emitted and scattered light.
This becomes particularly important for protein analysis. In many fluorescence workflows, scattered light is treated as unwanted background and removed. Dr. Ryder challenged this assumption. In biologics, proteins are not simply small dissolved molecules. They can behave as particles, and when they change in size, associate, or aggregate, the scattering signal changes as well.
By using polarisation strategically, scattered light can either be enhanced or suppressed depending on the analytical goal. In one configuration, scattering becomes more visible and can provide particle-sensitive information. In another configuration, scattering can be reduced to focus more strongly on fluorescence emission. This makes polarised intrinsic emission a useful tool for studying protein aggregation, protein environment, and structural changes.
While EEM and polarised EEM measurements are powerful, they often generate large, multidimensional datasets. Traditionally, these datasets require multivariate data analysis, advanced chemometrics, and specialised software environments such as MATLAB.
Dr. Ryder described this as a practical barrier. In some cases, the data analysis can become more complicated than the experiment itself. Analysts may spend more time building and validating models than interpreting the sample. This can make method transfer difficult and can limit adoption in routine process development or manufacturing environments.
APIES was developed partly as a response to this problem. The aim is not to remove scientific depth, but to simplify the measurement strategy so that clear, robust, and transparent analytical outputs can be generated more directly.
APIES combines three simultaneous signals from the same sample:
Absorbance provides information on protein concentration, for example through A280, and can also contribute to aggregation indices when measured at selected wavelengths.
Polarised intrinsic emission provides information about protein environment, concentration-related effects, structural changes, and stability indicators such as emission ratios, shifts, and peak shape.
Scattering provides information on particles, protein aggregation, bubbles, and size-related changes through Rayleigh-Mie scattering signals.
The major advantage is that these measurements are collected simultaneously from the same aliquot, in the same instrument, at the same time. This reduces variability caused by transferring samples between instruments or splitting material into multiple aliquots. It also helps identify artifacts. For example, bubbles may increase absorbance and scattering while reducing emission, creating a recognisable signal pattern.
By combining the three signals, APIES can produce fast, multi-attribute readouts for concentration, aggregation, and stability. According to Dr. Ryder, the approach can be analysed with transparent, Excel-based workflows rather than relying entirely on complex black-box chemometrics.
One of the key applications presented was low-pH viral inactivation, a critical downstream step in monoclonal antibody manufacturing. Viral inactivation is essential for process safety, but low-pH conditions can also create stress for proteins and may promote aggregation or structural changes.
Using APIES, Dr. Ryder’s group monitored an IgG-type protein under viral inactivation conditions. The method enabled simultaneous tracking of aggregation-related signals, protein concentration, and stability-related indicators. Importantly, APIES could detect aggregation behaviour in real time.
The comparison with dynamic light scattering and size exclusion chromatography was especially important. APIES showed aggregation behaviour in the vessel that was not always captured by size exclusion chromatography. In some conditions, aggregation appeared to be present in real time, while SEC did not show a corresponding increase in irreversible aggregates. This suggests that APIES may provide valuable insight into in-vessel aggregation, including reversible or transient aggregation pathways that can be missed when relying only on offline endpoint methods.
This is highly relevant for process analytical technology. A method that can detect aggregation rapidly, during the process, and with minimal sample handling could help teams understand how process conditions influence product quality before issues become visible in final analytical results.
The second case study focused on protein-liposome interactions, an important topic for drug delivery and nanoparticle-based therapeutic systems. When lipid nanoparticles or liposomes enter biological environments, proteins can rapidly adsorb onto their surfaces, forming a protein corona. Understanding how this corona forms and evolves is important for predicting biological behaviour, stability, and delivery performance.
APIES enabled second-scale monitoring of early protein-liposome interactions. In the first phase, the method showed rapid adsorption of protein onto the liposome surface. Over longer timeframes, changes in emission behaviour suggested that proteins were interacting more deeply with the lipid bilayer.
This case study highlights a broader strength of the APIES approach: it can capture fast kinetic processes and connect different optical signals to concentration, size, surface interaction, and structural information. This makes it promising not only for classical protein aggregation monitoring, but also for emerging drug delivery applications.
Process Analytical Technology aims to bring measurement closer to the process. Instead of relying only on delayed offline analysis, PAT supports real-time or near-real-time understanding of critical process and product attributes. For biopharmaceuticals, this is especially important because product quality can be shaped by subtle changes in raw materials, process conditions, purification steps, and formulation environments.
APIES is aligned with this direction. It is label-free, relatively fast, multi-attribute, and suitable for automation. Dr. Ryder also noted that the technology has been tested in flow-cell formats, which supports its potential for online or at-line process monitoring.
The broader message is that better analytics do not always need to mean more complexity. In many situations, the goal should be to design measurement platforms that generate robust, interpretable outputs quickly enough to support process decisions. APIES moves in this direction by combining absorbance, polarised intrinsic emission, and scattering into one practical analytical framework.
Dr. Ryder’s presentation showed how APIES has developed from years of work on complex molecular systems, cell culture media, fluorescence spectroscopy, polarised emission, and protein analysis. The result is a measurement approach that makes better use of optical data already available from the sample.
By using absorbance, intrinsic emission, and scattering together, APIES can provide a richer picture of biopharmaceutical samples than any single signal alone. It can help monitor protein concentration, aggregation, stability, particle formation, and kinetic interactions in real time or near real time.
For bioprocess development teams, this opens exciting possibilities. APIES may support faster process understanding, more responsive downstream monitoring, and better integration of analytics into PAT and Quality by Design strategies.
Watch the recorded presentation by Dr. Alan G. Ryder to learn more about APIES and its potential role in the future of biopharmaceutical analysis.
To explore how automated sampling, analyzer integration, and process data management can support modern PAT workflows, learn more about Securecell’s Lucullus® and Numera® solutions.