🖥️ DMTA Innovation at Takeda – Digitalized, AI-Augmented, Autonomous

Optimization of chemical process development means exploring complex parameter spaces. Traditional manual approaches are time-consuming, tedious and low throughput. 

Discover the next generation of AI-driven, digitalized design, make, test, analyze (DMTA) cycles in pharmaceutical R&D.

Enabling Self-Driving Labs for Synthetic Molecule Process Development

Takeda has invested significant time and effort in digitalizing and automating R&D. In synthetic molecule process development (SMPD) this involved building up high-throughput experimentation (HTE) and automation capabilities to accelerate process optimization.

Join Adrian Ramirez Galilea, Associate Director of Automation & HTE at Takeda, as he shares his experience and insights. He will discuss:

• How experiment time has been reduced from weeks to days (or minutes) with integrated systems that enable data flow and experiment execution with minimal human intervention.
• The importance of automating analytical data processing for a seamless feedback loop.
• Generating high-quality, reproducible, AI-ready, FAIR data from consistent, repeatable experiments.
• Designing standardized workflows suited for self-driving labs.
• Takeda’s vision and progress towards a 24/7 autonomous lab.

An Integrated Environment for AI-enabled, Autonomous HTE     

Atinary Technologies’ SDLabs® (Self-Driving Labs) is a no-code platform for AI-driven experimentation to accelerate R&D. Katalyst D2D^® software, from ACD/Labs, offers end-to-end workflow and data management for high throughput experimentation (HTE). Learn how integration of these informatics solutions is helping make autonomous labs a reality.