Ep. 46: Live from Pittcon with Award Winners Bob Pirok and Justin Godinho

In this episode, podcast co-hosts Dr. Dwight Stoll and Dr. James Grinias talk with two recent award winners live from the Pittcon conference in San Antonio, Texas: Dr. Bob Pirok and Dr. Justin Godinho. Dr. Bob Pirok is an Associate Professor at the University of Amsterdam in the Netherlands, where he also completed his Ph.D. under the direction of Professor Peter Schoenmakers. He is an expert in multi-dimensional chromatography and the application of machine learning within the field. At Pittcon we celebrated his receipt of the Emerging Leader in Chromatography Award, presented by LCGC International Magazine. Dr. Justin Godinho is a Senior Principal Scientist at GlaxoSmithKline (GSK) in Upper Providence, Pennsylvania. He completed his undergraduate work at Davidson College and his Ph.D. under the direction of Professor Jim Jorgenson at the University of North Carolina at Chapel Hill. Before joining GSK, he worked in the column manufacturing business with Advanced Materials Technology. He is a recognized expert in column technology at both capillary and analytical scales, as well as the analysis of small and biotherapeutic molecules. At this year's conference, he received the Young Investigator in Separation Science Award, sponsored by the ACS Division of Analytical Chemistry.
In the conversation, we discuss the recent award sessions for both guests and the evolution of their respective careers. We explore Justin’s transition from fundamental column packing research to biopharmaceutical applications at GSK, touching on the ongoing need for protein-suitable columns, capillary-scale techniques, and the Hydrophobic Subtraction Model column database. We then shift to Bob’s research on automated method development discussed at the meeting. This includes a deep dive into data-driven chromatography, the complexities involved in establishing effective Chromatographic Response Functions (CRFs), and why setting realistic expectations for artificial intelligence and automated method development is critical to avoiding a future "AI winter."