01-21-2025
Both in and out of the pharmaceutical industry, Eli Lilly and Company has made some of the most substantial investments in artificial intelligence (AI) innovation to accelerate research and development (R&D). Ramesh Durvasula, senior vice president of R&D IT, is leading the digital transformation at Lilly by uniting science with technology.
A member of the Advisory Board for the Master of Business and Technology, the Daniels School’s technology-centric business degree program, Durvasula recently shared use cases where Lilly implements AI not just in augmenting human capabilities, but in drug discovery, clinical trials, process optimization and data integration.
Lilly committed decades ago to being an early adopter of new technology, Durvasula said, creating a culture that welcomes new possibilities. The company sees AI as a powerful tool to augment human scientists and decision-makers rather than replace them. Now that generative AI has become an accessible and scalable tool, they cultivate a culture affirming that scientists who use AI will outperform those who don't.
Lilly is exploring ways to use AI not just to incrementally improve existing processes, but to fundamentally reinvent them for 10-times the number of improvements. Durvasula noted that Lilly is seeking an "Uber moment" for the pharmaceutical industry, integrating multiple disciplines to create new business models.
Lilly employs AI in drug discovery, using it to model new molecules faster and more efficiently. They integrate AI into the workflow of chemists rather than treating it as a separate tool. The company brings together chemistry with data science to optimize the use of AI for more successful AI-driven drug discovery compared to some competitors and biotech startups.
Early on, Lilly realized AI could be a useful tool in one of the pharmaceutical industry’s biggest challenges, setting up clinical trials. They’re using AI to identify and enroll patients in clinical trials more effectively. The technology helps determine optimal locations for clinical trial sites to attract more participants, which accelerates the trial process, allowing for faster regulatory submissions and getting medicines to patients sooner.
When it comes to data integration, Lilly focuses on following scientific questions for specific uses. It is working on merging various types of data, including genomics, digital health streaming data, medical records, and imaging data. Lilly's approach emphasizes the integration of AI across multiple domains, including science, economics, technology, and business strategy, to maximize its impact on pharmaceutical innovation and patient care.
One of the most significant concerns for AI is data use, integration and privacy.
Lilly scrutinizes its uses for data and the vendors that approach the company claiming to have new and more powerful algorithms. The company's approach is summed up by the phrase "So you got an algorithm? That don't impress me much." It sets a high bar for due diligence in employing AI.
Lilly conducts thorough investigations into AI companies, examining the company's hiring practices and board composition, meeting with the chief data scientist to assess expertise, and inquiring about the data used to develop their algorithm.
Durvasula says Lilly prefers AI companies that can demonstrate the effectiveness of their algorithms without requiring access to Lilly's proprietary data. It is wary of vendors who claim to have a model but need Lilly's data to make it work. This is a red flag. Knowing that high-quality data leads to better models, Lilly is protective of its data, considering it a valuable asset and potential competitive advantage. As a result, Lilly's approach is to test AI models themselves rather than sharing data. They prefer to say, "Give me your model — I'll tell you if it worked or not."
Lilly values AI companies that demonstrate a deep understanding of both the scientific and business aspects of the pharmaceutical industry. They are cautious of companies in the venture capital and startup space that may not fully grasp the complexities of the science involved. These principles help Lilly leverage AI capabilities to drive innovation in drug discovery and development without compromising the scientific process, their business success and human privacy.