In a bid to optimise the use of AI in drug development, US and European regulators have banded together to advise industry members on how best to incorporate the technology into their workflow.
The new joint guidance, which debuted on 14 January by the European Medicines Agency (EMA) and the US Food and Drug Administration (FDA), recommends ten key principles, which the regulators say will “lay the foundation for developing good practice” when using AI while helping to guide its long-term growth.
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When developing AI technologies, the agencies suggest that companies design models to be high quality, human-centric and compliant, which will allow them to align with tightening ethical, legal, scientific, regulatory and cybersecurity standards.
To ensure these are upheld over the long-term, drug developers are encouraged to keep detailed and traceable records on the data sources used to train their AI models, as well as the steps taken to process this data. This will help companies stay in line with Good Practice (GxP) requirements.
The agencies also place a key focus on clarity, with their recommendations stating that AI technologies should have a well-defined role and scope for use. The information presented to an end user should also be easily digestible, accessible and relevant to the target audience – underpinned by data that is fit-for-use.
Drug developers are also advised to validate and account for risks when employing AI, while ensuring that risk-based performance assessments investigate the complete system – including human-AI interactions – using metrics and data relevant to the context in which it is being used. These evaluations should be regularly performed, facilitating the monitoring and troubleshooting of issues with an AI technology throughout its lifecycle.
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By GlobalDataThese pillars of AI best practice were written with contributions from the FDA’s drug and biologics evaluation and research divisions, which the agency says will help to “fully realise the potential of AI,” while championing the technology’s reliability and positive impact.
AI becomes major drug development tool
As the pharma industry progresses further into the digital age, technologies such as AI are becoming a core part of the modern workflow. This is especially true in drug development, as AI has been used to develop or repurpose more than 3,000 drugs as of November 2024, according to the GlobalData Drugs database.
While AI is becoming fundamental for many, the method in which it is employed can vary. While some choose to develop models internally, others bank on external partnerships or a hybrid approach.
At the ongoing 2026 JP Morgan Healthcare Conference, Tony Wood, CSO of UK-based big pharma GSK, noted that its AI collaborations will help to stave off the impact of multiple patent expiries by strengthening its early-stage R&D pipeline during a presentation at the event.
Eli Lilly is also hedging its bets on AI to drive its continued success, as the company has inked several AI-based deals with tech giant NVIDIA. These agreements will see the pair create pharma’s “most powerful” supercomputer and build an AI-driven co-innovation lab – both to support future drug development.
Also at the 2026 JP Morgan Healthcare Conference, AstraZeneca announced it would be acquiring Modella AI, which the company hopes will expedite drug development within its oncology pipeline. The pair initially joined forces in July 2025 through a research agreement.
According to GlobalData, parent company of Clinical Trials Arena, venture financing deals involving AI have experienced more than a 400% increase between 2014 and 2024, highlighting the burgeoning interest in this technology.
