UNITED KINGDOM – Building on their existing collaboration, BMS and Owkin have launched a multiyear partnership for data-driven trial design, with an initial focus on cardiovascular diseases.

The collaboration follows years of partnership between the companies, including a number of projects identifying biomarkers and improving clinical trial outcomes with covariate adjustment, using real-world data.

Under the new deal, data from a network of academic medical centres and “state-of-the-art machine learning techniques” will be used to enhance clinical trial design and execution with AI-based approaches that optimize endpoint definitions, patient subgroups, and treatment effect estimation with covariate adjustment and external control groups.

Under the agreement, Bristol Myer Squibb will make an upfront payment and investment in Owkin, totaling US$80 million, with the latter also eligible for milestones worth in excess of US$100 million.

Owkin said it will use the investment to support its data generation strategy in multiple therapeutic areas, focusing on multimodal and “rich biological data, including the most advanced spatial single-cell omics technologies.”

Owkin has also entered into partnerships with Johnson & Johnson, Amgen and Sanofi, with the latter investing US$180 million as part of a collaboration focused on developing treatments across four types of cancer.

Owkin’s research network sits between data scientists and international medical researchers and helps build and train deep-learning AI models on large, decentralized data sets without requiring all participants to pool their resources.

This past September, the AI startup showed off a deep learning model capable of classifying a breast cancer patient’s risk of a metastatic relapse over the next five years.

In a retrospective study conducted with French cancer research institute Gustave Roussy, Owkin’s program analyzed digital tumor slides alongside the patient’s medical history and other clinical data, including age at surgery and surgery type.

The results, showing accuracy of about 81%, were presented at the annual meeting of the European Society of Medical Oncology.

Before that, Owkin demonstrated it could help predict treatment responses in mesothelioma based on scans of tumor biopsy samples.

That program, dubbed MesoNet, could also visually highlight the areas of the tissue slide that drove its predictions to help pathologists better classify patients.

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