From Fringe to Frontier: AI as a Core Tool
Dr. Lynda Stuart, CEO of the Fund for Science and Technology (FFST), has witnessed AI evolve from a theoretical concept to a practical, real-world tool in biotech. “It’s an interesting time for the AI field in biotech because there’s a massive transition from a nascent technology to being a real tool that you can use,” Stuart notes. This shift offers a significant opportunity to define responsible use and mitigate risks.
Stuart, who collaborated with Nobel laureate David Baker (whose protein design ideas were once considered “lunatic fringe”), emphasizes the need to balance rapid innovation with cautious regulation. The FFST plans to invest $150 million in AI-focused grants by next year, aiming to find the right equilibrium between pushing boundaries and managing excessive hype. They propose an “if-then” framework: “if certain capabilities are reached, then you deal with them — but you shouldn’t get all panicked about something that’s 10 years away.”
Overcoming Resistance to Progress and Embracing Data
While cutting-edge science drives pharma, Dr. Mathai Mammen, former R&D head at Johnson & Johnson and now CEO of Parabilis Medicines, recalls significant resistance to AI adoption. Even with broad conceptual support, fully integrating AI was a challenge. “For some people, there’s a tried-and-true approach, and they feel there’s too much company and personal risk in rationalizing trying something new,” Mammen explains.
Mammen overcame this by focusing on immediate, solvable problems and empowering data science teams with real authority. This strategic shift led to J&J becoming a leader in pharmaceutical data science, demonstrating how early AI adoption can drive substantial success within a portfolio. The key was a gradual, data-centric approach rather than a full-scale, premature tech overhaul.
Building for the Future: Speed, Ethics, and Human Insight
Companies like Sanofi are designing facilities with future AI integration in mind. Matt Truppo, Global Head of Research Platforms and Computational R&D at Sanofi, highlights how AI, leveraging massive datasets, has drastically cut down research times—from months to performing “multiple structures a day.”
Sanofi’s new campus fosters collaboration and quickly evaluates new AI models, but Truppo stresses the importance of human intuition. The industry must maintain the human aspect and traditional scientific process as AI becomes more widely used.
The Broader Impact: Efficiency, Ethics, and Evolution
AI’s integration is fundamentally changing how drugs are discovered, developed, and delivered. In drug discovery, AI accelerates the identification of potential drug candidates, optimizes molecular design, and predicts efficacy and safety profiles, significantly shortening timelines. For clinical trials, AI is revolutionizing patient recruitment, trial design, and real-time data analysis. Algorithms can quickly identify suitable candidates from vast datasets, optimize protocols based on historical data, and even predict patient dropouts, saving significant time and cost.
However, this rapid advancement brings critical ethical considerations. Ensuring data privacy and security, addressing algorithmic bias (which can arise from biased training data), and promoting transparency in “black box” AI models are paramount. Regulatory bodies are grappling with how to oversee AI in drug development, emphasizing the need for human oversight and accountability in AI-driven decisions.
The biopharma industry is moving toward more personalized medicine, faster approvals, and more efficient operations, all powered by AI. As this “massive transition” continues, leaders must not only embrace the technological advancements but also meticulously address the ethical implications, ensuring AI serves to improve health responsibly and equitably. The future of drugmaking will be defined by how well human expertise and AI’s capabilities are integrated, always grounded in the principles of sound science and patient well-being.
Source: Pharma Voice | July 1, 2025
