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News|Webcasts|July 9, 2026
Evidence at the Speed of Science: How Leading Pharma Is Putting AI to Work in R&D
Author(s)HealthVerity
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Webinar Date/Time: Thu, Jul 30, 2026 11:00 AM EDT
Takeda's Head of Computational & Systems Biology joins Causaly to share how AI evidence synthesis is compressing R&D timelines from indication expansion to Phase 3 decisions. Hear the real use cases, real results, and real lessons from the frontline of pharma AI adoption.
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Event Overview:
Takeda's Head of Computational & Systems Biology joins Causaly to share how AI evidence synthesis is compressing R&D timelines from indication expansion to Phase 3 decisions. Hear the real use cases, real results, and real lessons from the frontline of pharma AI adoption.
Key Learning Objectives
- Understand why purpose-built scientific AI outperforms general LLMs for R&D
- Gain insight from real use cases: indication expansion, MoA analysis, Phase 3 support
- Know how to scale AI adoption from early pilots to team-wide practice
Who Should Attend
- R&D Scientists, Computational Biologists, Heads of Drug Discovery, Medical Affairs Leaders, Heads of R&D, Chief Scientific Officers
- Translational Scientists, R&D VPs and CSOs, Regulatory Affairs, Clinical Operations, Bioinformaticians
Speakers:
Joe Gigliotti, MD, MBA
Strategic Client Partner
Causaly
Joe Gigliotti, MD, MBA is Strategic Client Partner at Causaly, the agentic AI platform for life sciences. A former Partner at Boston Consulting Group and graduate of Geisel School of Medicine at Dartmouth, Joe brings deep clinical and strategic expertise to his work with enterprise R&D teams. At Causaly, he focuses on helping organizations move from isolated AI pilots to durable, team-wide scientific capability.
Vinayagam Arunachalam
Senior Director, Head of Computational & Systems Biology
Takeda
Vinayagam Arunachalam is Senior Director and Head of Computational & Systems Biology in Takeda's GI² Early Clinical Development unit. With 18+ years spanning Pfizer, Harvard Medical School, and the German Cancer Research Center, Vinu leads strategies for target identification, biomarker discovery, and disease pathway analysis. He has published 30+ peer-reviewed articles and authored a book on AI/ML in life sciences.
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