To combat escalating R&D costs, high clinical attrition and stringent global pricing pressures, the biopharmaceutical industry is on a quest to find the best uses for AI and computational modelling. Increasingly, Bayesian digital twins and in silico trials are used to expedite and de-risk drug development. By combining multi-omic and real-world data, these computational models generate next-gen Digital Controls capable of managing biological uncertainty, reducing reliance on human subjects and streamlining trial design, particularly for rare diseases. This paradigm shift not only compresses development timelines to safeguard pipeline economics against policies like the US MFN but also delivers the robust, patient-level evidence necessary to satisfy modern regulatory and HTA mandates, including the FDA Modernization Act 2.0 and the EU Joint Clinical Assessment.

R&D Productivity Crisis

The biopharmaceutical industry is intimately familiar with “Eroom’s Law”, the opposite of Moore’s Law, where drug discovery is becoming exponentially slower and more expensive over time, despite remarkable, concurrent improvements in technology and an increase in the volume of research data. The financial reality of bringing a novel therapeutic to market is staggering; costs frequently exceed $2.5 billion, and the development timeline routinely stretches over a decade. This crisis is most acutely felt in complex disease areas like oncology, where the clinical trial failure rate for novel therapeutics routinely hovers above 95%.