Using multiomic data from tissue, blood, and urine samples collected from patients in a Phase I trial, NAi can help trial sponsors map patient biology and better understand the mechanism of action and safety—not just for the population as a whole, but also how the drug interacts with patients with specific germline or somatic genomic profiles.

As an example, BPGbio team tested our BPM31510 compound with a broad mechanism that targeted the mitochondria in a large Phase I trial with 104 patients with different cancers. Leveraging our multiomic data we could better understand how BPM31510 induced a shift in metabolism and see that its effect was limited to those with more aggressive tumors. Knowing this helped us prioritize future studies to more narrow clinical indications, select patients with these types of cancer, and better understand the pharmacodynamic biomarkers predictive of success before beginning Phase II studies.

NAi can also help scientists understand the relationship between patient biology and adverse events and modify clinical protocols to limit toxicities and optimize trials for patient safety.

In one of our trials, our AI modeling identified toxicity markers for patients with previously unknown bleeding and clotting abnormalities leading us to add dosing of vitamin K to the clinical trial protocol in a subsequent trial.

NAi increases the predictability of clinical trial outcomes. With NAi, BPGbio has run three successful trials in oncology and two in rare disease.