Takeda has spent the last years significantly building up its high-throughput (HTE) and automation capabilities around the Unchained Labs’ family of tools. Nevertheless, while we are investing in a variety of different devices and approaches, our ultimate vision is to design and implement self-driving labs (SDLs) to carry out self-optimizing workflows. Process development and optimization frequently involves exploring wide parameter spaces, making these iterative algorithm-guided SDLs ideal for such optimizations. As such, we herein present our vision for fully self-optimizing HTE workflows, with the ultimate goal of creating SDLs with workflows optimized for a variety of general applications needed in synthetic molecule drug development. To illustrate this process, case studies will be presented, highlighting both their successes, as well as the limitations of our current hardware and software tools.
Key Learning Objectives:
- Strategies for deploying automation in a synthetic chemistry process research and development lab
- Development of workflows for self-driving labs
- Example case studies to show productivity gains and portfolio impact through automation
Who Should Attend:
- Laboratory managers/directors
- Synthetic chemists
- Self-driving lab enthusiasts