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November 08, 2021 04:41 PM

Genomenon, Deep 6 AI Forge Data Partnership for Clinical Trial Matching

Genomeweb
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    Genomenon said Thursday that it has entered into a data partnership with Deep 6 AI, a maker of artificial intelligence-based clinical trial acceleration software.

    Under what the firms dubbed a proof-of-concept agreement, they will effectively explore  combining their technology in search of insights about conditions including cancer, Parkinson's disease, and Wilson's disease, according to a spokesperson for Ann Arbor, Michigan-based Genomenon.

    Pasadena, California-based Deep 6 AI offers software that mines structured and unstructured patient records, and genomics and pathology data to support trial-feasibility site selection and patient recruitment. That firm will integrate with Genomenon's AI-powered Genomic Landscapes platform to match patients to trial inclusion/exclusion criteria and then rank trial candidates by match percentage.

    "Our collaboration with Deep 6 AI will help patients who will benefit from matching knowledge, while our Genomic Landscapes enable pharma to accelerate target discovery, identify genetic biomarkers for better clinical trial stratification, and develop [clinical diagnostics] for regulatory approval," Genomenon CEO Mike Klein said in a statement.

    "Precision matching is at the heart of our mission to support precision medicine and rapidly deliver more targeted therapies to patients," said Jason Attanucci, Deep 6 AI's VP of life sciences. "Together, both Genomenon and Deep 6 AI will provide a combined, AI-driven understanding of disease, enabling actionable insight into their genetic drivers, clinical representation, and comorbidity."

    Attanucci is scheduled to co-present at the CNS Summit Nov. 9 on results of a study highlighting Deep 6 AI's matching technology.

    In September, Genomenon announced a partnership with AstraZeneca's Alexion Pharmaceuticals to apply Genomic Landscapes to diagnosis of rare diseases including Wilson's.

    This story first appeared in Genomeweb.

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