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Akoya Keeping Eye on the Clinic Amidst New Product Launches and Collaborations


NEW YORK — With a series of new collaborations and product launches, Akoya Biosciences is continuing its push toward clinical trials and, ultimately, clinical markets.

In recent months, the Marlborough, Massachusetts-based spatial omics firm has inked collaborations to add RNA analysis as well as AI-based data analysis capabilities and has launched a new platform called the PhenoCycler-Fusion that aims to combine the high-multiplexing of the company's PhenoCycler platform (formerly called CODEX) with the throughput of its PhenoImager (formerly called Phenoptics) system.

The company also recently received CLIA certification for its Advanced Biopharma Solutions (ABS) laboratory, which it believes better positions its technology for use in late-stage clinical trials.

Akoya has traditionally offered its PhenoCycler system as a high-multiplexing, low-throughput system targeting the research market and its Phenoptics platform, which it acquired from PerkinElmer in 2018, as a lower-multiplexing, high-throughput platform targeted at translational and clinical work.

The PhenoCycler system was designed to be compatible with a wide range of fluorescent microscopes, meaning that customers didn't have to buy new instruments to use the system. During Akoya's recent presentation at the annual JP Morgan Healthcare Conference, CEO Brian McKelligon noted, however, that many CODEX customers were buying new microscopes anyway to dedicate to the system.

This indicated potential customer demand for a fully integrated system to Akoya that would combine the reagent handling capabilities of the PhenoCycler with the PhenoImager imaging platform. McKelligon announced the launch of that system, the PhenoCycler-Fusion, at the JP Morgan conference last week.

The Fusion platform can be run in either a high-multiplex, low-throughput mode or a low-multiplex, high-throughput mode. The former can currently measure up to 50 proteins in parallel at a pace of around 10 samples per week, while the latter can measure up to six proteins per sample in around 100 samples per week. McKelligon said at the conference that Akoya aims by the end of the year to increase multiplexing and throughput of the high-multiplex workflow to 100 proteins per sample and 30 samples per week.

Akoya continues to sell its Polaris system, now called the PhenoImager HT, which can measure up to six proteins per sample in around 300 samples per week. It will also continue to sell the PhenoCycler as a standalone system that users can pair with a microscope of their choice.

The pairing of the two systems will allow researchers to use the same platform for discovery and validation work, McKelligon said, noting that the firm expects this combination to drive increased use and instrument revenues.

During the company's JP Morgan presentation, Niro Ramachandran, Akoya's chief business officer, said the PhenoCycler-Fusion will likely prove attractive to core labs and CROs due to its high throughput and ability to offer a variety of workflows on a single platform. To date, much of the company's business for the PhenoCycler has come from individual investigators.

In addition to the platform launch, Akoya is moving into RNA analysis, having inked a deal this month with Bio-Techne to develop a version of that company's RNAScope HiPlex v2 assay on the PhenoCycler-Fusion system. With that assay, users will be able to measure around 10 different RNA targets in addition to protein targets.

Akoya is also working internally on a transcriptomic assay for use on the PhenoCycler-Fusion that will allow users to measure up to 1,000 RNA targets. The Bio-Techne assay will be available around Q2 of this year, with the larger transcriptomic assay likely becoming available in the middle of 2023, Ramachandran said in an interview.

Akoya expects the addition of RNA data to open up a new base of customers among researchers in areas like genomics and single-cell RNA-seq, he said, as well as add more functional data to its analyses.

In single-cell analysis, he said, protein is most commonly used for typing cells, while RNA-level data — due in large part to being easier to measure at large scale than single-cell protein data — is often collected to get insights into cell state and activity, for instance whether a particular cell signaling pathway is turned off or on. RNA data can also be useful for identifying cells that are producing particular secreted proteins, something that he noted is difficult to assess by measuring these proteins directly given that they have been secreted out of the cell that originally produced them.

While Akoya's PhenoCycler-Fusion launch and RNA work are largely targeted at the discovery and translational markets, the company ultimately aims to move into the clinical space. It took a step in that direction in November, when it received CLIA certification for its ABS lab. McKelligon said at JP Morgan that Akoya has seen an expansion of its pharma and cancer immunotherapy work since the opening of that facility, though the company has not announced any additional projects beyond those already disclosed with AstraZeneca, the University of California, San Francisco, and Johns Hopkins University.

"The reason we took that lab through CLIA is so that we can migrate from retrospective studies to enrollment studies on our path to broaden our participation in clinical trial and ultimately get companion diagnostic partnerships," McKelligon said.

In December, Akoya announced a collaboration with Boston-based PathAI that McKelligon said was likewise aimed at this move toward the clinic. Under the collaboration, Akoya will use its platform in combination with PathAI's artificial intelligence tools to identify predictive markers for biopharma customers.

"It's about us partnering with PathAI, with large pharma, on large-scale clinical trial studies where we are creating the data on our platform, doing some of the preliminary analysis, and then handing that off to PathAI and saying 'what are the unique AI-derived signatures that can segment patient populations and allow this clinical program to continue to progress?'" McKelligon said.

While Akoya's clinical ambitions to date have focused largely on the development of predictive markers and potential companion diagnostics in immuno-oncology, PathAI, with its roots in traditional pathology, suggests another area where Akoya might eventually play clinically — improving existing pathology practices.

"In certain indications, there are multiple slides that are made per patient," McKelligon said. "We can consolidate those and make [the data] more quantifiable. So there is potential change to current standard of care as an opportunity."