AI's next pharma challenge: tackling costly and risky clinical trials

AI is being eyed as a remedy for the costly and risky nature of clinical trials, a bottleneck that still stymies drug development. Developing a new drug can take about a decade and $1–2 billion, with 60–70% of that time spent in three human-testing phases. Roughly 90 of 100 drug candidates fail to win regulatory approval after trials, underscoring high risk and wasted resources. TwinEdge Bioscience’s Kevin Buyens notes that digital technologies could shift this paradigm, with AI already speeding up candidate identification by sifting vast chemical and biological data. Regulators, led by the FDA, have begun to embrace AI more cautiously, launching a pilot in April to assess AI-enabled tools in early trials while ensuring safety and quality.