Somewhere between assessing safety in healthy volunteers and testing effectiveness in hundreds to thousands of patients, most drugs in clinical trials fail. Pradipta Ghosh and her colleagues at the University of California, San Diego (UCSD) School of Medicine have developed a blueprint that they hope will end this disheartening phenomenon.
Ghosh and her team used artificial intelligence (AI) to discern patterns in gene expression datasets that apply to all patients with inflammatory bowel disease (IBD) and to identify clinically actionable drug targets. After testing drug candidates in mouse models, typically the final step before clinical trials, the researchers conducted a “Phase 0” clinical trial. Using patient-derived organoid models, they determined whether any observed therapeutic effect resulted from the candidate drug or confounding variables.
By combining AI and testing patient-derived organoids, Ghosh and her colleagues identified a drug that repaired the leaky gut barrier. The approach also distinguished drugs that succeeded ...
