An Exclusive Spectroscopy Interview on Flow Imaging Microscopy in Collaboration with the American Association of Pharmaceutical Scientists (AAPS), Part 2

Introduction

In Part 2 of this two-part interview, we continue to speak with Austin Daniels, application scientist for Yokogawa Fluid Imaging Technologies, about the subject of flow imaging microscopy (FIM).

Flow imaging microscopy (FIM) offers a more comprehensive approach, capturing high-resolution images and quantitative data on particle size, shape, and composition in pharmaceutical preparations. Instruments like FlowCam enable detailed characterization, distinguishing protein aggregates from contaminants and monitoring process stability. Recognized in regulatory guidelines such as USP <1787> and <1788>, FIM enhances compliance and quality control in biotherapeutic development (1–16).

In Part 2 of this interview, we continue to discuss the advantages of FIM over traditional techniques, its role in regulatory compliance, and real-world applications for improving biologic safety and efficacy. Join us as we explore the impact of this innovative technology on subvisible particle characterization.

How does FIM address challenges in low-volume or high-viscosity formulations while meeting USP <787> and <789> particle limits?

FIM instruments help with low-volume formulations simply by not requiring a ton of volume relative to many other particle analysis techniques. FIM measurements only require around 100 μL to perform and get consistent measurements. This can still be a lot of volume to spend on a single measurement, but as it's much lower than traditional particle analysis techniques like light obscuration (LO) it can allow scientists to capture subvisible particle measurements as part of their formulation development. It’s important to note that FIM can also process larger volumes using a similar standard operating procedure (SOP), meaning that the technique can also process very large volumes as well depending on the available volume and experiment goals.

With regards to viscosity, there is an interesting quirk to how FIM instruments perform concentration measurements that help the technique be robust to viscosity changes. To measure concentrations, the number of particles that are detected is normalized to the volume imaged during the measurement rather than the total volume that passes through the flow cell. The volume analyzed is based on the number of times the flow cell was imaged and the volume sampled per image. Handling concentration calculations this way makes the technique robust to small errors in flow rate—the main concern viscosity poses for particle measurements. While I would still recommend diluting a very viscous sample to ensure that the sample flows through at the recommended flow rates, the technique can provide reasonable measurements on most typical biotherapeutics regardless of their viscosity.

How does FIM offer an advantage in monitoring silicone oil droplets compared to LO or membrane microscopy (MM)?

The main advantage of FIM for monitoring silicone oil droplets is the ability to analyze them using one analytical technique instead of two. Neither LO nor MM are ideal for monitoring silicone oil droplets but for completely different reasons. LO measurements detect silicone oil droplets in a sample but are unable to distinguish them from other particle types that may be in the sample and thus cannot provide a concentration or size distribution for these particle types. MM captures particle morphology info, but silicone oil droplets are removed during membrane filtration and thus not detected. To get a measurement of the silicone oil content with these techniques, researchers must use both methods to analyze a sample and take the difference in the measured concentrations. Since FIM can distinguish between silicone oil droplets while still counting them, it can be used in isolation and capture a reasonable estimate of a sample’s silicone oil content.

How does FIM differ from LO in sensitivity to particle types, like protein aggregates, and how does this impact USP <788> compliance?

FIM primarily differs from LO in its sensitivity to transparent particle types. LO measurements depend on both the size and refractive index of the particles and will generally report smaller particle sizes the more translucent a particle appears. This often results in LO underestimating the concentration and size of particles in a sample, particularly biologics that contain a high concentration of translucent particles. FIM measurements are not impacted by these issues to the same extent, resulting in higher particle concentrations and sizes relative to LO.

When thinking about USP <788> compliance, it is important to remember that the pharmacopeia limits were set with the limitations of LO in mind. Many researchers are often surprised to see FIM and related particle techniques report much higher particle concentrations than are allowed via USP <788>. However, that only means that LO is missing particles in a sample and not necessarily that the sample is not USP <788> compliant. The sample only fails to meet pharmacopeia guidelines if it has more particles than are allowed via USP <788> when tested by a compendial method. That said, regulators are aware of the limitations of the compendial methods and the current subvisible particle limits.

How does FIM’s particle morphology analysis help identify sources of particles and enhance control in line with USP <1787> and <1788>?

Both USP <1787> and <1788> motivate the need to monitor particle type and source as part of a robust particle control strategy. For <1787>, this comes from a need to distinguish between extrinsic and intrinsic particles in a drug product (which often need to be prevented or minimized) from inherent protein aggregate particles in a protein therapy (which cannot be completely prevented). It also helps researchers better characterize protein aggregate particles in general which is necessary given their potential to impact patient safety. FIM is one of the many techniques cited in this chapter to help formulation scientists with this effort.

For USP <1788>, the ability of FIM to help determine particle source is the primary cited reason the technique was included in the chapter—otherwise, the chapter solely focuses on methods compendial to USP <788>. FIM allows researchers to capture this information without using MM and the measurement bias it can introduce. This feature also makes FIM a good “referee” method between the two compendial methods, providing particle type/source information that can help scientists interpret the results from the compendial methods (for example presence of silicone oil droplets in a sample).

How does FIM track protein aggregation, particularly under stress conditions, and how does it compare to traditional methods?

FIM tracks protein aggregation the way most particle analyzers do—by tracking changes in particle concentrations and size distributions following different stress conditions. One of the advantages of this approach relative to legacy methods like LO is simply the accuracy and sensitivity of the method. This often helps researchers identify if even subtle changes in how a sample is formulated or handled can impact the stability of the protein. This data can sometimes help researchers find differences in protein aggregation that sometimes don’t show up via other analytical techniques like size-exclusion chromatography (SEC) and high-performance liquid chromatography (HPLC).

The other main advantage is the ability to track changes in the types of aggregates that are formed under different stress conditions. Some studies have shown that different stress conditions generate protein aggregates whose morphology is characteristic of the stress at their root cause. With appropriate study design, this data can help researchers glean information about the mechanisms of aggregation that are occurring in different samples.

How does FIM help identify aggregation mechanisms in biologics and guide formulation improvements to minimize risks?

As discussed previously, the concentration, size, and morphology data FIM provides can help researchers identify even subtle changes in aggregation mechanisms between formulations and stress conditions. It can also give some general insights into aggregation mechanisms under certain conditions.

Knowing the sources of instability in a protein formulation can help formulation scientists be more methodical in designing formulation changes to mitigate known instabilities. Imagine we noticed a significant change in aggregate concentrations following an agitation stress. We might decide to try changes like adding surfactants or increasing the fill volume to address the issue as these changes will often mitigate aggregation via shaking stresses.

Can FIM be used to control visible particles?

Yes! While FIM is most typically used as a subvisible particle monitoring tool, some FIM instruments are also able to measure visible particles. This can be a helpful orthogonal technique to visual inspection for monitoring visible particles. FIM is a destructive measurement and thus cannot be used as a primary lot release test for visible particles. In exchange, FIM provides more consistent detection and enumeration of visible particles, including gray zone particles that are too small to be consistently observed via visual inspection. It also provides particle images that can be used to determine the root cause of visible particles that may appear in samples. This makes FIM a useful method to screen for visible particles during drug product development or as secondary destructive testing as part of lot release testing in a similar fashion as it is used for subvisible particle monitoring.

How does integrating FIM into training programs improve operator qualification and visual inspection accuracy for visible particles in therapeutics?

FIM can be used as an orthogonal technique for visual inspection, providing more sensitive and quantitative visible particle measurements albeit in a destructive fashion. This data can help validate visual inspection processes, allowing scientists to quantify the number of particles detected via visual inspection against the actual number of visible particles in a sample. This can help researchers develop processes and training strategies that catch as many visible particles as possible, greatly reducing the number of withheld units and helping minimize the risk of complaints and recalls in the clinic.

FIM has also been used to develop better particle standards for visual inspection by groups at the US National Institute of Standards and Technology (NIST) and other organizations. These efforts have mostly focused on making particles that mimic visible protein aggregates, one of the most challenging yet important particles for inspectors to detect. Using these standards in training kits as part of operator training and qualification may help inspectors find these particles more reliably, potentially reducing their number in the final drug products. These standards may also make visual inspection processes more consistent between organizations, ensuring that these improvements can be implemented by any organization developing parenteral therapies.

What future potential or new applications do you envision for FIM going forward?

The main potential I see in FIM is in its use as a lot release test for subvisible particles. FIM has replaced LO and MM throughout much of biotherapeutic development for its greater accuracy and access to morphology information. These features make it equally valuable in a quality control context. While I don’t think it will be added to USP <788> any time soon, I think it is likely to happen once the technology becomes more mature and methods for using the image data are more established.

I also think there is a lot more that scientists can do with the image data that FIM provides. Several labs have started to explore what can be done with cutting-edge image analysis approaches like artificial intelligence in recognizing different particle types in samples using FIM data. FIM is unlikely to completely replace forensic methods for identifying particles in samples—the technique only has access to morphology data that can be encoded in a brightfield microscopy image. However, as image analysis tools become more powerful and commonplace I think there is a lot of untapped potential for FIM as a tool to screen samples for different particle types.

Application-wise, I am most excited to see what FIM can do in the context of cell therapies. Cell therapies pose unique challenges from a quality control strategy since the active ingredient is a subvisible particle. As some researchers have found, FIM can be a useful way to get around this issue, letting researchers distinguish between cells that are supposed to be there and other particle types that are not.

Likewise, FIM can also process much bigger cell assemblies as well that can be useful to monitor. This not only includes cell clusters but even larger assemblies like organoids that have applications outside of parenteral therapy development.

Supplemental Definitions

Flow Imaging Microscopy (FIM)

Flow Imaging Microscopy (FIM) is a technique used to analyze particle size, morphology, and distribution in liquid suspensions. It captures images of particles as they flow through a microscope system, providing high-resolution data on particle shape, size, and concentration.

Light Obscuration (LO)

LO is a method for measuring particle size and concentration in suspensions. It works by detecting changes in light transmission through a sample as particles pass through a sensing zone. The extent of light blockage correlates with the size of the particles, offering a quantitative measurement.

Electron Microscopy (EM)

EM uses a beam of electrons instead of light to form an image. This technique provides high-resolution images of biological and non-biological samples, revealing fine details at the nanoscale, including cellular structures and molecular components.

Membrane Microscopy (MM)

MM is a particle analysis technique that involves using membrane filtration to remove the background buffer of a sample, then using microscopy to image and analyze particles on the membrane filter. It provides particle morphology information in addition to some particle concentration and size data.

USP Methods Summary:

<787> – Subvisible Particulate Matter in Therapeutic Protein Injections

This method provides guidelines for detecting and quantifying subvisible particles in therapeutic protein injections using LO. It applies to protein formulations administered by injection and ensures product safety by limiting particulate contamination.

<788> – Particulate Matter in Injections

This monograph defines the requirements for detecting and quantifying particulate matter in injectable drug products using LO or MM. It outlines acceptable levels of subvisible particles in injectable medications to ensure patient safety.

<789> – Detection of Subvisible Particulate Matter in Injectable Drug Products

Similar to <788>, this method focuses on assessing subvisible particles in ophthalmic drug products. It emphasizes techniques such as LO for determining particle size and distribution in these critical formulations.

<1787> – Particulate Matter in Parenteral Drug Products

This chapter focuses on protein therapies. It details various methods researchers can use to characterize protein aggregates and other subvisible particles, including flow imaging microscopy, Raman Spectroscopy, and electron microscopy.

<1788> – Determining the Amount of Subvisible Particles in Parenteral Drug Products

This method outlines how to assess subvisible particle contamination in parenteral drug products. It includes guidelines for using light obscuration, flow imaging microscopy, and other validated techniques to detect particles and ensure product safety.

References

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About the Interviewee

Dr. Austin Daniels is an application scientist for Yokogawa Fluid Imaging Technologies. He received his Ph.D. in Chemical and Biological Engineering from the University of Colorado. His research focused on flow imaging microscopy and similar subvisible particle imaging techniques combined with artificial intelligence-driven image analysis tools. These methods were used to compare protein aggregates generated via different stress conditions in biotherapeutics. Currently, he is working on exploring and improving applications for flow imaging microscopy in biotherapeutics development and beyond.

About the Interviewer

Jerome Workman, Jr. serves on the Editorial Advisory Board of Spectroscopy and is the Executive Editor for LCGC and Spectroscopy. He is the co-host of the Analytically Speaking podcast and has published multiple reference text volumes, including the three-volume Academic Press Handbook of Organic Compounds, the five-volume The Concise Handbook of Analytical Spectroscopy, the 2nd edition of Practical Guide and Spectral Atlas for Interpretive Near-Infrared Spectroscopy, the 2nd edition of Chemometrics in Spectroscopy, and the 4th edition of The Handbook of Near-Infrared Analysis. Author contact: JWorkman@MJHlifesciences.com ●