Biomedical research generates a flood of information that no scientist can realistically absorb. At the University of Edinburgh, bioengineer Filippo Menolascina uses Co-Scientist to search the literature for missed connections and generate novel hypotheses.
His team focused on a common liver disease called metabolic dysfunction-associated steatohepatitis (MASH). Therapeutic development is challenging because MASH involves interrelated biological processes, including liver inflammation and metabolism, which means that single-target drugs are not sufficient. This is pushing researchers toward combination therapies, but the number of potential drug pairs is overwhelming.
Faced with this combinatorial explosion, Menolascina used the Co-Scientist option to narrow the search area. In his hands, the co-scientist synthesized evidence from liver biology and pharmacology, highlighted mechanisms worth focusing on, and flagged potential combination therapies his team could test.
In one iconic case, a co-scientist tackled a timely, practical question: Why does the drug resmetirome, a recently approved drug prescribed for a specific stage of MASH, only aid a petite group of eligible patients? The system generated a hypothesis pointing to the NLRP3 inflammasome as a specific molecular bridge linking inflammation and metabolism in disease – a link that had never before been pieced together into a single actionable explanation. The hypothesis, later verified experimentally, could pave the way for targeted dual therapies.