Flow Cytometry Advancements Accelerate Drug Discovery
Advances in flow cytometry, automation and AI are accelerating drug discovery by improving efficiency, data processing and reproducibility.
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Flow cytometry, which provides multi-parametric cell characterization, is a cornerstone tool in clinical and laboratory research, including drug discovery research and development (R&D).
At The Society for Laboratory Automation and Screening (SLAS) 2025 international event, Technology Networks spoke with Dr. Richard Cuthbert, global commercialization product manager at Bio-Rad.
A specialist in flow cytometry, cell sorting and cellular biology, Cuthbert shared his perspectives on the current and future applications of flow cytometry – and automated flow cytometry systems – in modern drug discovery.
Innovations in flow cytometry
As drug discovery pipelines become increasingly complex, the demand for high-throughput and multiparameter flow cytometry continues to rise. "The versatility of flow cytometry is why it is such a crucial tool in drug discovery," Cuthbert said. He explained how the depth and volume of information provided can support the identification and characterization of novel bioactive drugs through phenotypic screening, analyzing drug mechanisms at the single-cell level and assessing drug safety and efficacy.
Flow cytometry trends that are accelerating drug discovery include the democratization of the technology and advancements in data processing capabilities, Cuthbert explained: "Manufacturers, including Bio-Rad, have made substantial efforts to simplify what is inherently a complex technology, making it easier to use and more widely accessible.”
"Not long ago, analyzing 10 parameters was considered quite a lot and manually loading 5 mL tubes one by one was the standard,” he added. “Now, instruments like the ZE5 Cell AnalyzerTM can process 384 samples in under an hour, handling 30-parameter analysis with full automation.” This represents a step change in the volume and depth of data collection.
Rigorous reproducibility and standardization are critical in drug discovery, which has an extremely low success rate. Flow cytometry advancements are having a significant impact on improving data consistency, Cuthbert said: "One of the major historical advancements in improving data reproducibility has been the development of standardization beads, which provide a set reference point for size, brightness or color."
Advances in dye technologies have also driven progress. "For example, Bio-Rad's StarBright DyesTM are extremely bright, improving signal-to-noise ratios. They are also highly stable and resistant to photobleaching, ensuring researchers get the same results over time,” Cuthbert said.
Another major advantage of StarBright Dyes is that they can be premixed months in advance. “You can’t do that with many of the other antibodies on the market because they tend to stick to each other,” he added.
“It’s really easy to introduce errors when you make up your antibody mix, being able to premix has a huge positive impact on reproducibility and saves a lot of time.”
The impact of automation and AI on flow cytometry in drug discovery research
Cuthbert emphasized how the ability to automate flow cytometry systems carries major benefits for the biopharma and small-molecule drug industries, which are becoming increasingly streamlined. “We have recently seen the pharmaceutical and biopharmaceutical industries undergo significant restructuring efforts. Often these restructuring efforts are aimed at creating leaner, more agile organizations focused on technology innovation and R&D,” he said.
Automation can be applied in data acquisition and upstream workflows, such as cell culture and sample preparation. “We are seeing automation primarily used in a reader-feeder setup for analyzing prepared samples currently. However, I think that as time goes on, greater automation in upstream processes could significantly enhance productivity while minimizing human error and improving reproducibility,” Cuthbert predicted.
Automation enhances operational efficiency and increases the amount of data that can be obtained from experiments. In turn, this creates a further bottleneck – how to handle that data. “Historically, the bottleneck was data collection, but we've solved that with instruments like the ZE5 Cell Analyzer which, thanks to automation, can collect data 24/7. Now, the next bottleneck is data processing and that's where machine learning comes in. Some of our customers are already using machine learning techniques to process the massive data volumes generated using the ZE5 Cell Analyzer,” Cuthbert added.
While the use of machine learning and more broadly artificial intelligence (AI) might be outside the comfort zone of many flow cytometry users right now, Cuthbert envisages that its role will only continue to grow.
“I think in the future we will see more integration of flow cytometry technologies with automation and advanced data analysis techniques, and we will see how that synergy accelerates drug discovery,” Cuthbert concluded.
