Bristol Myers pays $80M to AI firm Owkin as part of cardiovascular trial accord

Bristol Myers Squibb will work with French biotech AI developer Owkin to design and optimize cardiovascular drug trials under a deal announced June 8.

The partnership will use machine learning techniques to “enhance clinical trial design and execution,” according to the Parisian tech firm, which cites optimized endpoint definitions, patient subgroup identification and treatment effect estimation as examples.

For its efforts, Owkin will receive $80 million across a series B-1 equity round and an upfront payment, with potentially $100 million to follow in milestone payments. The deal comes less than a year after French pharma Sanofi invested $180 million in its own cancer research collaboration with Owkin.

As part of the deal, which has the potential to expand to other therapeutic areas, BMS will be able to access data from a network of academic medical research centers with which Owkin has partnered.

“Owkin works with a network of leading hospitals across the world. This means that we have access to the high-quality data required to power [our] machine learning approaches,” a spokesperson for the AI company said.

The company has equipped 18 leading academic research centers with its federated learning technology and is part of eight consortia of leading biopharma and research partners. It works closely with 160 leading opinion leaders from oncology, cardiology and beyond, the company added.

Data quality is key, according to the spokesperson. “An AI is only as powerful as the data it's trained on. Federated learning, pioneered by Owkin, allows for data to be analysed by machine learning models without the data being sent off-site.”

Owkin’s technologies have already demonstrated the ability to analyze data more effectively than other techniques. In a study published in 2020, for example, the firm used two deep learning algorithms to build models for predicting the survival of patients with hepatocellular carcinoma treated by surgical resection.