Drug development is generating more data than ever before, and major pharmaceutical companies like AstraZeneca are turning to AI to make sense of it. The question is no longer whether AI can help, but rather how closely it should be integrated into research and clinical operations to improve clinical trial and treatment decision-making.
This question helps explain why AstraZeneca is bringing Modella AI in-house. The company has agreed to acquire a Boston-based AI company, aiming to deepen the use of AI across oncology research and clinical development. Financial terms were not disclosed.
Rather than treating AI as a support tool, AstraZeneca is pulling Modela’s models, data and staff directly into its research organization. The move reflects broader changes in the pharmaceutical industry, with partnerships giving way to acquisitions as companies seek more control over how they build, test and use AI in regulated environments.
Why AI ownership is becoming important in drug discovery research
Modella AI focuses on using computers to analyze pathology data, such as biopsy images, and linking those findings with clinical information. Its research focuses on making pathology more quantitative, helping researchers discover patterns that may indicate useful biomarkers or guide treatment choices.
Modella said in a statement that its foundational models and AI agents will be integrated into AstraZeneca’s oncology research and development operations, which will focus on clinical development and biomarker discovery.
How AstraZeneca moved its AI partnership towards full integration
For AstraZeneca, the deal builds on a collaboration that began several years ago. This early partnership allowed both parties to test whether Modela’s tools would work within a pharmaceutical company’s research environment. AstraZeneca executives say the experience highlights the need for closer integration.
Speaking at the JPMorgan Healthcare Conference, AstraZeneca Chief Financial Officer Aradhana Sarin described the acquisition as a way to bring more data and AI capabilities into the company.
“Oncology drug development is becoming more complex, data-rich and time-sensitive,” said Gabi Raia, chief commercial officer at Modella AI, adding that joining AstraZeneca will allow the company to deploy its tools in global trials and clinical settings.
Utilize AI to improve trial decisions
Sarin said the deal “significantly strengthens” AstraZeneca’s efforts 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: to reduce the time it takes to turn research data into decisions that influence trial design and patient selection.
One area where AstraZeneca hopes AI will impact is the selection of patients for clinical trials. Better matching of patients and studies could improve trial results and reduce costs associated with study delays and failures.
This type of improvement relies less on complex algorithms and more on stable access to clean data and tools that fit into existing workflows.
People and tools moved in-house
The acquisition also highlights a shift in the way big pharmaceutical companies think about AI talent. Companies are increasingly treating data scientists and machine learning experts as part of their core research teams, rather than relying on external vendors. For AstraZeneca, bringing Modela staff in-house reduces reliance on external roadmaps and gives the company more say in how the tool is adapted as research needs change.
AstraZeneca said that while partnerships between pharmaceutical and technology companies have become common, this is the first time a major pharmaceutical company has acquired an AI company outright.
AstraZeneca enters crowded field of pharma and AI deals
At the same healthcare conference, several new partnerships were announced, including a $1 billion collaboration between Nvidia and Eli Lilly to build new research labs using Nvidia’s latest AI chips.
These deals demonstrate growing interest in AI across industries, but also highlight important differences in strategy. Partnerships can speed up experimentation, while acquisitions signal a long-term bet on building internal capabilities. For companies operating under strict regulatory rules, that control can be as important as raw computing power.
What AstraZeneca is betting next
Sarin described the initial partnership between AstraZeneca and Modela as a “test drive,” saying the company ultimately wanted Modela’s data, models and talent within its organization. The aim is to help develop “highly targeted biomarkers, and then highly targeted treatments,” she said.
Beyond the Moderna deal, Sarin said 2026 is expected to be a busy year for AstraZeneca, with several late-stage trial results expected to be announced in a variety of therapeutic areas. The company is also working toward a goal of $80 billion in annual revenue by 2030.
Whether such acquisitions help achieve these goals depends on execution. Incorporating AI into drug development is slow, expensive, and often cumbersome. Still, AstraZeneca’s move is a clear signal that it sees value in deeply embedding AI into the way it discovers and tests drugs, rather than buying it as a service.
(Photo: Mika Baumeister)
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