AstraZeneca Banks on In-house AI for Accelerating Oncology Research
The drug development sector is currently experiencing a surge in data generation. To make sense of this data, big pharmaceutical companies like AstraZeneca are leveraging the capabilities of artificial intelligence (AI). The challenge has now shifted from whether AI can assist to how much it needs to be incorporated into research and clinical operations to enhance decisions associated with trials and treatments.
This explains why AstraZeneca is introducing Modella AI, a Boston-based AI company that it has agreed to acquire, within its organization. The goal is to expand the use of AI in oncology research and clinical development. The financial details of this transaction have not been disclosed.
AI Ownership Becoming Crucial in Drug Research
AstraZeneca is not merely considering AI as an auxiliary tool. Instead, the pharmaceutical giant is integrating Modella’s models, data, and personnel directly into its research division. This shift is representative of a broader change in the pharmaceutical industry. Partnerships are being replaced by acquisitions as companies aim to gain more control over AI’s development, testing, and application in regulated environments.
Modella AI specializes in utilizing computers to analyze pathological data, such as biopsy images, and associating these results with clinical information. Their work is aimed at making pathology more quantitative and assisting researchers in identifying patterns that could suggest useful biomarkers or guide treatment decisions. Modella has stated that its base models and AI agents will be incorporated into AstraZeneca’s oncology research and development work, focusing on clinical development and biomarker discovery.
AstraZeneca’s Progressive AI Partnership Towards Full Integration
This acquisition is a progression of a collaboration that began a few years ago between AstraZeneca and Modella. The collaboration allowed both entities to test if Modella’s tools could function within AstraZeneca’s research environment. The experience confirmed the requirement for closer integration. AstraZeneca’s CFO, Aradhana Sarin, described the acquisition as a step towards incorporating more data and AI capabilities into the company.
Gabi Raia, Modella AI’s chief commercial officer, stated that the complexity, data richness, and time sensitivity of oncology drug development are increasing. Joining AstraZeneca would allow the company to apply its tools in global trials and clinical settings.
AI’s Role in Enhancing Study Decisions
Sarin mentioned that the deal would “boost” AstraZeneca’s work in quantitative pathology and biomarker discovery by bringing data, models, and teams under one roof. The main objective is to reduce the time required to transform research data into decisions that impact study design and patient selection. AI is expected to significantly influence the selection of patients for clinical trials. Better patient allocation to trials could enhance trial outcomes and decrease costs associated with delays or failed trials.
In-house Move of Talent and Tools
The acquisition also underscores a change in how large pharmaceutical companies approach AI talent. Companies are increasingly considering data scientists and machine learning experts as part of their core research teams. For AstraZeneca, integrating Modella’s staff internally reduces its dependence on external roadmaps and provides the company more influence over how it adapts tools to evolving research needs.
AstraZeneca claimed that this is the first time a major pharmaceutical company has fully acquired an AI company. However, collaborations between drugmakers and technology companies are becoming more common.
AstraZeneca Joins the Array of Pharmaceutical AI Deals
At the same healthcare conference, other new partnerships were announced, including a $1 billion collaboration between Nvidia and Eli Lilly to create a new research lab utilizing Nvidia’s latest AI chips. These deals exhibit the growing interest in AI across the industry. However, they also highlight a strategic difference. Partnerships can expedite experimentation, while acquisitions represent a long-term investment in building internal capabilities. For companies subject to stringent regulations, this control can be just as valuable as pure computing power.
AstraZeneca’s Future Bets
Sarin described the past partnership between AstraZeneca and Modella as a “test drive”. She stated that the company ultimately wants Modella’s data, models, and people within the organization. The aim is to support the development of “very targeted biomarkers and then targeted therapeutics.” Sarin added that 2026 is projected to be a busy year for AstraZeneca with numerous late-stage trial results expected in different therapeutic areas. The company is also targeting annual sales of $80 billion by 2030.
Whether acquisitions like this contribute to achieving these goals will depend on execution. The integration of AI into drug development can be slow, costly, and often messy. However, AstraZeneca’s move signals a clear vision of where it believes the value lies: not in purchasing AI as a service, but in deeply embedding it into the process of drug discovery and testing.
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