Reimagining Chemical Development in Pharma: Takeda’s Digital Transformation Toward a Self-Driving Laboratory

The pharmaceutical industry is undergoing a paradigm shift in how chemical processes are designed, developed, and scaled. Aggressive sustainability goals, compressed development timelines, the drive to reduce development costs, and the desire to internalize knowledge have placed increasing pressure on traditional experimental approaches. While proven, these methods are resource-intensive, experience-driven, slow to iterate, and limited in their ability to capture the full complexity of modern synthetic molecules.

At Takeda, we are reimagining chemical development through a digital-first, in silico-driven strategy that integrates advanced modeling, automation, and artificial intelligence to accelerate innovation, reduce risk, and lower development costs. This presentation will share Takeda’s journey toward building a self-driving laboratory, where computational design and digital workflows guide experimentation, and automated platforms rapidly generate and analyze data to close the loop between prediction and practice.

We will highlight case studies demonstrating how in silico models, process analytical technologies (PAT), and machine learning are being applied to reaction and particle engineering challenges. In addition, we will discuss the organizational, cultural, and technical enablers required to embed digital-first thinking in process development, along with key lessons learned in scaling these approaches globally.

By embracing digital tools and automation, Takeda is transforming chemical development into a more agile, sustainable, and knowledge-rich discipline — paving the way for a future where self-driving laboratories are not only possible, but essential to delivering medicines faster, more reliably, and more cost-effectively to patients.