Webinar: Continuous Flow Process Optimization and Control Using Multiple Orthogonal PAT
Deploy Autonomous Processes for Continuous Manufacturing
Panoramica del programma
- Learn how advanced tools such as the Multifactorial Bayesian Optimized EDBO+ and online process analytical technologies (ReactIR™, HPLC via EasyFrit™, and EasyViewer™) can streamline and optimize these processes.
- Gain insights from a real-world case study where these technologies were applied in the synthesis of Apremilast in collaboration with Amgen, highlighting the practical application and efficiency gains of these innovative techniques.
The often labor-intensive process of discovering optimal operational conditions for a continuous manufacturing process creates a bottleneck for rapidly deploying this enabling technology. Further, once a process is developed translating, these conditions to manufacturing can require operator oversight to monitor and adjust in-process controls. These factors can often lead to inefficiencies and increased resource expenditure.
In this webinar, we will introduce a modular system created using a standard Vapourtec platform coupled to online ReactIR™, online HPLC via EasyFrit™, and crystal imaging through EasyViewer™. These tools synergize with a multifactorial Bayesian algorithm called EDBO+. This tool drastically reduces the lengthy trial-and-error phase of process development and minimizes the need for continuous operator intervention.
Finally, we transition from theory to reality, highlighting a collaboration with Amgen, where we applied these technologies to the synthesis of Apremilast. The process was conducted via a plug flow reactor, leading into a set of MSMPR anti-solvent crystallizations in an EasyMax™ automated lab reactor (ALR) platform. This real-world case study showcases the remarkable efficiency gains and productivity enhancements that can be achieved through this innovative approach.
Target Audience
Those interested in deploying autonomous process optimization to continuous manufacturing.
Presenter
Jason Hein
University of British Columbia
Jason Hein is an Associate Professor of Chemistry at the University of British Columbia, an Adjunct Professor at the University of Bergen, Norway. Prof. Hein was the co-lead of Project ADA; the world's first autonomous discovery platform for thin film materials, supported by Natural Resources Canada, co-PI of the MADNESS team supported by the DARPA Accelerated Molecular Discovery Program and the UBC lead for the Acceleration Consortium CFREF spearheaded by the University of Toronto. Jason has also translated his passion for developing enabling technology by becoming the CEO and founder of Telescope Innovations; a chemical technology start-up creating AI-enabled automation solutions for process chemical development. He received his B.Sc. in Biochemistry in 2000 and Ph.D. in asymmetric reaction methodology in 2005 from the University of Manitoba (NSERC PGS-A/B, Prof. Philip G. Hultin). In 2006, he became an NSERC postdoctoral research fellow with Prof. K. Barry Sharpless and Prof. Valery V. Fokin at the Scripps Research Institute in La Jolla, CA. In 2010, he became a senior research associate with Prof. Donna G. Blackmond at the Scripps Research Institute. He began his independent career at the University of California, Merced in 2011, employing in-situ kinetic reaction analysis to rapidly profile and study complex networks of reactions. In 2015, he moved to the University of British Columbia and was promoted to Associate Professor in 2019. His research has resulted in a collection of prototype modular robotic tools and integrated analytical hardware which create the first broadly applicable automated reaction profiling toolkit geared toward enabling autonomous research and discovery.
