Artificial intelligence is accelerating drug repurposing ??? the process of identifying new therapeutic uses for existing approved drugs ??? and regulatory agencies are starting to take notice. In 2025, the EMA and FDA both issued guidance documents acknowledging AI/ML-derived evidence as supportive data in supplemental new drug applications.
Several AI-driven repurposing candidates have entered Phase II clinical trials, with timelines compressed by 60-70% compared to traditional drug discovery pathways. By leveraging large-scale molecular interaction databases, electronic health records, and published literature, machine learning models can identify promising compound-indication pairs that would take human researchers years to hypothesize.
The business implications are substantial. Drug repurposing drastically reduces development costs ??? from an average of $2.6 billion for a new molecular entity to roughly $300 million for a repurposed drug. For pharmaceutical companies with mature compound libraries, this represents an enormous untapped value pool.
For the B2B ecosystem, AI-powered repurposing creates demand for specialized services: computational chemistry platforms, real-world evidence analytics, adaptive clinical trial design, and regulatory consulting for AI-generated submissions.