AstraZeneca bets on in-house AI to speed up oncology research

Drug development is producing more data than ever, and large pharmaceutical companies like AstraZeneca are turning to AI to make sense of it. The challenge is no longer whether AI can help, but how tightly it needs to be built into research and clinical work to improve decisions around trials and treatment.

That question helps explain why AstraZeneca is bringing Modella AI in-house. The company has agreed to acquire the Boston-based AI firm as it looks to deepen its use of AI across oncology research and clinical development. Financial terms were not disclosed.

Rather than treating AI as a supporting tool, AstraZeneca is pulling Modella’s models, data, and staff directly into its research organisation. The move reflects a broader shift in the drug industry, where partnerships are giving way to acquisitions as companies try to gain more control over how AI is built, tested, and used in regulated settings.

Why AI ownership is starting to matter in drug research

Modella AI focuses on using computers to analyse pathology data, such as biopsy images, and link those findings with clinical information. Its work centres on making pathology more quantitative, helping researchers spot patterns that may point to useful biomarkers or guide treatment choices.

In a statement, Modella said its foundation models and AI agents would be integrated into AstraZeneca’s oncology research and development work, with a focus on clinical development and biomarker discovery.

How AstraZeneca moved its AI partnership toward full integration

For AstraZeneca, the deal builds on a collaboration that began several years ago. That earlier partnership allowed both sides to test whether Modella’s tools could work within the drugmaker’s research environment. According to AstraZeneca executives, the experience made it clear that closer integration was needed.

Speaking at the J.P. Morgan Healthcare Conference, AstraZeneca Chief Financial Officer Aradhana Sarin described the acquisition as a way to bring more data and AI capability inside the company.

“Oncology drug development is becoming more complex, more data-rich and more time-sensitive,” said Gabi Raia, Modella AI’s chief commercial officer, adding that joining AstraZeneca would allow the company to deploy its tools across global trials and clinical settings.

Using AI to improve trial decisions

Sarin said the deal would “supercharge” AstraZeneca’s work in quantitative pathology and biomarker discovery by combining data, models, and teams under one roof. While such language reflects ambition, the practical goal is more grounded: shortening the time it takes to turn research data into decisions that affect trial design and patient selection.

One area where AstraZeneca expects AI to have an impact is in choosing patients for clinical trials. Better matching patients to studies could improve trial outcomes and reduce costs tied to delays or failed studies.

That kind of improvement depends less on complex algorithms and more on steady access to clean data and tools that fit into existing workflows.

Talent and tools move in-house

The acquisition also highlights a change in how large pharmaceutical firms think about AI talent. Rather than relying on outside vendors, companies are increasingly treating data scientists and machine learning experts as part of their core research teams. For AstraZeneca, bringing Modella’s staff in-house reduces dependence on external roadmaps and gives the company more say over how tools are adapted as research needs change.

AstraZeneca said this is the first time a major pharmaceutical company has acquired an AI firm outright, though collaborations between drugmakers and technology companies have become common.

AstraZeneca joins a crowded field of pharma–AI deals

At the same healthcare conference, several new partnerships were announced, including a $1 billion collaboration between Nvidia and Eli Lilly to build a new research lab using Nvidia’s latest AI chips.

Those deals point to growing interest in AI across the sector, but they also underline a key difference in strategy. Partnerships can speed up experimentation, while acquisitions suggest a longer-term bet on building internal capability. For companies operating under strict regulatory rules, that control can matter as much as raw computing power.

What AstraZeneca is betting on next

Sarin described the earlier AstraZeneca–Modella partnership as a “test drive,” saying the company ultimately wanted Modella’s data, models, and people inside the organisation. The aim, she said, is to support the development of “highly targeted biomarkers and then highly targeted therapeutics.”

Beyond the Modella deal, Sarin said 2026 is expected to be a busy year for AstraZeneca, with several late-stage trial results due across different therapy areas. The company is also working toward a target of $80 billion in annual revenue by 2030.

Whether acquisitions like this help meet those goals will depend on execution. Integrating AI into drug development is slow, expensive, and often messy. Still, AstraZeneca’s move signals a clear view of where it thinks the value lies: not in buying AI as a service, but in embedding it deeply into how medicines are discovered and tested.

(Photo by Mika Baumeister)

See also: Allister Frost: Tackling workforce anxiety for AI integration success

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