Merck KGaA signs agreement with Quris to test AI in drug discovery

Science and technology company Merck KGaA has signed an agreement with artificial intelligence (AI) company Quris to assess the company’s safety prediction platform in drug discovery projects.

Merck KGaA will assess Quris’ BioAI safety prediction platform in both traditional in vitro and in vivo approaches. Quris’ BioAI safety prediction platform integrates miniaturised human tissues on a chip, nano-sensing and machine learning to predict which drug candidates will work safely in humans.

Merck will initially assess Quris’s platform to identify potential liver toxicity risks for drug candidates, with special emphasis on ones that preclinical experiments failed to identify. Merck will also have the option to obtain up to a five-ear exclusive license to a specific disease domain.

“The pharmaceutical industry is modernising drug discovery, and innovations in AI hold significant promise,” said Quris scientific advisory board member Dr Robert S. Langer, co-founder of Moderna and MIT professor. “As Quris expands its collaborations with pharmaceutical companies, this will hopefully lead to new ways to find novel drugs that safely meet patients’ needs in the years ahead.”

“The drug development process must be improved; drugs that are successful in mice often still fail clinical trials in humans,” added Quris CEO Isaac Bentwich. “We are thrilled to be working with the Merck KGaA, Darmstadt, Germany team to assess Quris’s BioAI platform for clinical safety prediction.”

“In recent years, leaders around the globe have increasingly recognised that experiments in mice do not faithfully mimic what will work in people. With this and the goal to reduce and replace animal testing, there is now a great need for development of a new AI-based approach,” commented Nobel Laureate Aaron Ciechanover, chairman of Quris’s Scientific Advisory Board. “Collaborations with pharmaceutical companies like Merck KGaA, Darmstadt, Germany, will help assess Quris’s BioAI platform for clinical prediction, which uniquely integrates AI along with miniaturised ‘patients'-on-a-chip. If successful, this could lead to a much-needed transformation in drug development speed, safety and cost.”