The FDA wants pharmaceutical companies to unlock a goldmine of existing drugs by using artificial intelligence to analyze decades of untapped clinical data and identify new medical uses. The regulatory push comes as AI tools become capable of spotting patterns across millions of data points that traditional analysis would miss, potentially accelerating drug development and bringing therapies to patients faster without expensive new trials.

Why Are Regulators Suddenly Interested in Drug Repurposing?

Drug repurposing is not new. Aspirin started as a painkiller before becoming a cardiovascular medication, and Viagra was originally developed for heart conditions before finding its famous second act. But the FDA's recent push signals a shift in how the agency views this strategy. Earlier this month, the regulator announced it is seeking input from industry stakeholders on how to identify drugs already on the market that might work for entirely different conditions.

The opportunity is enormous. A single Phase 3 clinical trial can generate upward of 6 million data points, yet much of this information sits in fragmented databases and systems, rarely analyzed with modern AI capabilities.

"Many companies have years of data that have never been analyzed with today's AI and machine learning capabilities," explained Raj Indupuri, CEO and co-founder of eClinical Solutions.

Raj Indupuri, CEO and co-founder of eClinical Solutions

The FDA is specifically interested in two categories of candidates: drugs that might already meet evidence standards for new uses without additional trials, and those with early clinical or preclinical results that warrant further investigation. This represents a broader shift toward faster, more agile drug development that could reduce timelines and lower costs for companies with older drugs in their portfolios.

How Can AI Actually Help Find Hidden Drug Uses?

AI and machine learning excel at finding relationships across massive datasets that human analysts would struggle to spot. Trial information is often scattered across electronic data capture systems, laboratory platforms, safety databases, and real-world evidence sources, sometimes managed by external vendors or contract research organizations. AI can integrate these fragmented sources and uncover signals that suggest a drug might work for a different patient population or condition.

Specific applications include identifying patient subpopulations that responded differently than expected, discovering biomarker correlations, tracking longitudinal treatment patterns, and generating new hypotheses based on real-world outcomes. For example, AI might reveal that a drug designed for one disease actually showed unexpected benefits in a subset of patients with a completely different condition, opening the door to new clinical investigations.

What's Holding Back Industry Adoption?

Despite the FDA's enthusiasm, adoption remains uneven. A poll of clinical trial professionals conducted at the Clinical Trials Technology Congress in London found that half of respondents, 50%, cite trust and regulatory uncertainty as the biggest barriers to AI adoption in clinical trials. This hesitation reflects broader concerns about how AI decisions are made and whether regulators will accept them.

The good news is that regulators are signaling openness. Panelists from the UK's Medicines and Healthcare products Regulatory Agency (MHRA), the Danish Medicines Agency, and the Swedish Medical Products Agency emphasized that they are ready to embrace AI and want pharma companies to engage early in the process. The message is clear: speed without control is not acceptable when patient safety is at stake, but validated, auditable, and explainable AI approaches can move forward.

Steps to Prepare for AI-Driven Drug Repurposing

Companies looking to capitalize on the FDA's repurposing initiative should focus on four key areas, according to industry experts:

• Data Readiness: Organize and standardize clinical and real-world data across fragmented systems so AI tools can access and analyze it effectively.
• AI Governance: Establish clear frameworks for how AI models are developed, validated, and audited to ensure regulatory compliance and transparency.
• Operational Alignment: Align internal teams, from data management to clinical development, around AI-enabled workflows and decision-making processes.
• Asset Identification: Catalog underutilized compounds in your portfolio that have established safety profiles, manufacturing processes, and long-term evidence but have never been analyzed with modern AI tools.

For large pharmaceutical companies with older drugs, the potential value is substantial. Many approved compounds already have years of safety data and manufacturing infrastructure in place. Revisiting these assets with modern data infrastructure, agentic AI systems, integrated analytics, and digital twin strategies could unlock new indications without starting from scratch.

What Does This Mean for Drug Development Speed?

The FDA's push for drug repurposing reflects a broader evolution in how regulators approach clinical development. Agencies are increasingly encouraging continuous, data-driven approaches rather than waiting for formal trials to conclude before analyzing results. This shift sends a powerful signal that the pharmaceutical industry can become more agile and responsive to patient needs.

Early signs suggest AI is already delivering value. Among clinical trial professionals surveyed, 42% reported seeing early signs of return on investment from AI tools, with another 23% expecting ROI but not yet realizing it. Over the next three to five years, respondents believe AI will have the most impact on data cleaning, analysis, and insight generation, cited by 48% of professionals, as well as on sourcing and engaging patients, mentioned by 22%.

The FDA is accepting input from industry stakeholders, clinicians, researchers, and patients on how to identify drugs for repurposing through June 11, 2026. This represents a concrete opportunity for companies to shape how the regulatory framework evolves around AI-assisted drug discovery and repurposing. The window to influence this process is narrow, but the potential payoff for companies that move quickly is substantial.