Drug development now generates unprecedented volumes of data, prompting major pharmaceutical companies to adopt artificial intelligence, AI, for analysis and interpretation. The central question has shifted from whether AI is beneficial to how deeply it should be integrated into research and clinical workflows, to improve decision-making in trials and treatment.
AstraZeneca is internalizing AI capabilities through the acquisition of Modella AI, based in Boston. This move aims to enhance the use of AI in oncology research and clinical development. The financial details of the acquisition remain undisclosed.
This acquisition signifies a strategic shift in the pharmaceutical industry. Companies are moving away from partnerships toward acquisitions, seeking greater control over AI development, testing, and deployment, especially in regulated environments.
The Rising Importance of AI Ownership in Drug Research
Modella AI specializes in using computer analysis of pathology data, including biopsy images, and correlating it with clinical information. The company’s focus is on making pathology more quantitative, which aids researchers in identifying patterns that could indicate useful biomarkers or guide treatment options.
Modella AI stated that its AI agents and foundation models will be incorporated into AstraZeneca’s oncology research and development efforts. The emphasis will be on biomarker discovery and clinical development.
AstraZeneca’s Transition from Partnership to Integration
This acquisition is built upon an existing collaboration between AstraZeneca and Modella AI that spanned several years. This initial partnership served as a testing ground to assess the effectiveness of Modella’s tools within AstraZeneca’s research framework. AstraZeneca executives concluded that deeper integration was necessary based on this experience.
AstraZeneca Chief Financial Officer Aradhana Sarin, speaking at the J.P. Morgan Healthcare Conference, described the acquisition as a mechanism to bring more AI capabilities and data in-house.
Modella AI’s Chief Commercial Officer, Gabi Raia, noted that oncology drug development is becoming increasingly complex, data-intensive, and time-sensitive. Joining AstraZeneca would allow the company to implement its tools in global clinical settings and trials.
AI Applications in Improving Trial Decisions
Sarin stated that this deal would “supercharge” AstraZeneca’s initiatives in biomarker discovery and quantitative pathology by consolidating data, models, and teams. The primary goal is to accelerate the conversion of research data into actionable decisions impacting trial design and patient selection.
A key area where AstraZeneca anticipates AI will have a significant impact is patient selection for clinical trials. Improved patient matching could lead to better trial results and reduced costs associated with delays or unsuccessful studies.
This improvement relies less on intricate algorithms and more on consistent access to reliable data and tools integrated into existing workflows.
Bringing Talent and Tools In-House
This acquisition underscores a change in how pharmaceutical companies view AI talent. Data scientists and machine learning specialists are increasingly considered essential members of core research teams, rather than external vendors. By incorporating Modella’s staff, AstraZeneca aims to reduce reliance on external roadmaps and gain greater influence over how tools are adapted to evolving research needs.
AstraZeneca has indicated that this is the first instance of a major pharmaceutical company fully acquiring an AI firm. While collaborations between technology companies and drugmakers have become commonplace, complete acquisitions are less frequent.
AstraZeneca Enters a Competitive Arena of Pharma-AI Deals
Several new partnerships were announced at the J.P. Morgan Healthcare Conference, including a $1 billion collaboration between Nvidia and Eli Lilly to construct a new research lab utilizing Nvidia’s advanced AI chips.
These deals illustrate the increasing interest in AI across the pharmaceutical sector. They also highlight a fundamental strategic divergence, partnerships can expedite experimentation, whereas acquisitions suggest a long-term investment in developing internal capabilities. For companies operating under strict regulations, control can be as important as computing power.
AstraZeneca’s Future AI Investments
Sarin characterized the prior partnership between AstraZeneca and Modella AI as a “test drive,” emphasizing the company’s ultimate desire to integrate Modella’s data, models, and personnel within its organizational structure. The objective, she stated, is to facilitate the development of highly targeted biomarkers and therapeutics.
Looking beyond the Modella AI acquisition, Sarin indicated that AstraZeneca expects 2026 to be a significant year, with numerous late-stage trial results anticipated across various therapeutic areas. The company is also pursuing a target of achieving $80 billion in annual revenue by 2030.
The success of acquisitions such as this will depend on effective execution. Integrating AI into drug development is often complex, time-consuming, and costly. Nevertheless, AstraZeneca’s action indicates a clear conviction regarding where value resides, specifically, not in acquiring AI as a service, but in deeply embedding it into the processes of drug discovery and testing.
