Drug developers are forced to gamble on broad phase III trial designs, often blind to nuanced variations in drug response among various demographic and genetic subgroups—resulting in unforeseen efficacy or safety issues that derail years of work.

This leaves both patients and investors disappointed and highlights an inefficient use of R&D resources.

The tension lies in being data-rich but insight-poor, lacking the targeted analytics and unified health data needed to foresee subgroup failures with clinical and commercial impact.

Current preclinical and early clinical trial practices rely on homogeneous cohorts and fragmented data systems, making it hard to recognize and track subtle subgroup-specific drug responses.

Privacy regulations, siloed patient data, and insufficient collaboration between clinical, genetic, and epidemiological researchers further limit the ability to deploy precision analytics before phase III trials.