Phesi, Sensyne Health partner on synthetic arm tech for trials

By Jenni Spinner contact

- Last updated on GMT

(Alise Fox/iStock via Getty Images Plus)
(Alise Fox/iStock via Getty Images Plus)

Related tags: Phesi, data analysis, Data management, Patient outcomes, Clinical trials

The two organizations will collaborate on development of synthetic arms for clinical studies, drawing from millions of patient records and trial subject.

Phesi, a company that provides artificial intelligence (AI) fueled clinical solutions, and clinical AI firm Sensyne Health, have entered into a strategic collaboration. The latter will make a $10m equity investment in the former, to be used to accelerate product development t both firms.

According to the organizations, Phesi will harness the resources to promote development of its trial planning, protocol design and investigator site selection platform. Similarly, Sensyne reportedly plans to pursue growth of its data platform, and to work on its US market strategy.

As part of the partnership, Sensyne’s anonymized real-world evidence data set (consisting of 6.1m patient records) will be analyzed alongside Phesi’s data, currently covering 30m clinical trial subjects. The collaborators reportedly plan to offer enhanced clinical trial and real-world data predictive analytics, empowering clients to determine the feasibility of replacing or supplementing a comparator or placebo arm with a synthetic arm.

Gen Li, Phesi founder and president, explained that use of a synthetic arm, or synthetic patient data, can offer an advantage over placebo or comparator arms.

It removes the ethically questionable placebo arm of a clinical trial, which is especially relevant when patients are in chronic and severe pain,​” Li explained. “At the same time, it reduces the burden on sites and sponsors; fewer patients have to be recruited and all of those that are will be in the active arm of the tria​l."

Li added that the massive amounts of data available in clinical research enables trial teams to conduct analysis and model precise outcomes.

“[Synthetic arms] are best used in clinical trials where control group performance has been historically well characterized, and where results have been consistent from trial to trial; for example, trials in late-stage cancers or progressive genetic disorders where a patient’s health would deteriorate were they to receive a placebo rather than the investigational treatment​,” he said. “Synthetic patient data can be used to define the boundaries of a trial, to model and predict what types of patients should be included and excluded, and to minimize or eradicate the need for placebo patient enrollment​.”

Also, Li told us, use of synthetic arms has evolved in recent years, with many changes happening “behind the scenes.​”

We have been accumulating more data in various dimensions, including from patients, from clinical trial designs, and from implemented clinical trials; capabilities around extracting and interpreting data have also improved, and increased computing capacity has made it possible to apply complicated algorithms to obtain and structure various data​,” he outlined. “In parallel, the thinking of regulatory authorities has changed around the use of data not directly collected through highly regulated clinical trials, but from sources with trustable and verifiable quality​.

Synthetic arms are not a new concept, and have been used sporadically over the years. Now we are building an infrastructure to allow us to utilize historical data pragmatically and systematically​,” Li added.

Paul Drayson, founder and CEO of Sensyne Health, said, “The collaboration creates a compelling offering aimed at accelerating the development of pharmaceutical products, fundamentally improving patient care, and enabling us to offer the global pharmaceutical industry a compelling combination of expertise and data across real-world and clinical data, at the forefront of innovation in clinical development​.”

As part of the agreement, Li will become a strategic advisor to the Sensyne board.

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