Automated Experimentation Enables a Data-Driven Model for Palladium Removal with Aqueous Chelators

Residual metal removal can often be an inefficient, resource-heavy process, which involves multiple washing procedures. In the synthesis of Active Pharmaceutical Ingredients (APIs), transition-metal catalysis enables efficient synthesis of more complex structures; however, there is frequently a large amount of experimentation required to control residual transition metal amounts in the final products. We hypothesized that leveraging automation tools would allow generation of a data-driven, universal model for the prediction of palladium removal from typical workstreams. Novel automation methods for the preparation and generation of data were key to quickly understanding which parameters were the most important for the efficient chelation and removal of palladium. Parameters investigated included removal of both Pd(0) and Pd(II) as a function of temperature, organic solvent, pH, chelator identity, and equivalents of the chelator. The model indicates that chelator identity is the most important parameter followed by pH. This model, made available publicly, enables the prediction of the most successful conditions for palladium removal from organic reaction mixtures, reducing trial-and-error experimentation from the drug development process. Adoption of this methodology, leveraging automation and analytics, will allow for shortened development timelines when investigating cost-effective aqueous metal scavengers.