From Animals To AI: The FDA’s New Policy Should Transform The Drug Development Process

On 10 April 2025, the U.S. Food and Drug Administration (FDA) announced a groundbreaking plan to phase out its longstanding requirement for animal testing in the development of monoclonal antibodies and other drugs.¹ Instead, the FDA will encourage the use of “more effective, human-relevant” testing methods, including AI models and lab-grown human cell systems, to evaluate drug safety and efficacy.

Innovation Leading The Way

Since 1938, U.S. Federal law has mandated animal trials in drug development.² Now, rapid advancements in AI, multiomics, and microphysiological systems have made possible—and preferable—the pursuit of more predictive pathways, as shown below in Gen 2 and Gen 3 methods. The FDA’s initiative should lower the time and money necessary to develop efficacious drugs for human trials, with positive implications for AI-forward biotech companies and the patients they seek to serve.³

The FDA’s initiative represents a transformative opportunity for biotech firms already leveraging AI in drug discovery and development. They are already using these sorts of techniques in earlier stages of the pre-clinical process and might be able to use some of those data to fulfil the new requirements. In addition, because these firms have streamlined preclinical processes, the removal of mandatory animal testing should accelerate their progression to clinical trials relative to conventional drug developers, as shown below.⁴

The FDA’s directive signals its commitment to innovative, predictive drug development methodologies. By reducing reliance on lengthy and costly animal studies, AI-powered and human-relevant approaches could lower drug development costs, accelerate time-to-market, and increase therapeutic effectiveness. ARK’s analysis, shown below, suggests that AI-developed therapeutics could boost returns on investment to more than 20%, a 5x improvement relative to the pharmaceutical industry’s current average research and development (R&D) returns of ~4%. Ultimately, the integration of AI throughout the drug pipeline process could reduce overall development costs by four-fold.^5,6

Who Are The Main Beneficiaries?

While AI-centric drug developers like AbSci, AbCellera, and Recursion Pharmaceuticals stand to benefit most immediately, the policy’s implications extend into the multiomics tools and programmatic biological space as well. Companies providing sequencing, single-cell molecular analysis, spatial transcriptomics, and other advanced multiomics tools should benefit as biopharma firms adapt workflows to accelerate and enhance AI-driven drug discovery.

The benefits should extend beyond biotech to the healthcare system at large. Currently, only ~5% of rare diseases can be treated by approved therapies.⁷ The reason is clear: high costs focus drug developers only on markets with large addressable patient populations. As AI increases the efficacy of drug development, a higher percentage of rare diseases should become addressable. ARK estimates that AI could reduce the time to market by 50% and the odds of failure by another 50% at each stage of the development process, potentially enabling biotech companies to address 75% of the rare disease patient population profitably. As a result, the quality of patient lives should increase as the need for costly hospitalisations and outpatient healthcare declines.^8,9

Conclusion

In summary, the FDA’s announcement heralds not only a regulatory shift but also the dawn of a new era for molecular medicine, one characterised by faster, more cost-effective, and more accurate drug development benefiting innovators, patients, and the healthcare system.