PharmTech LCGC Sciex Webinar 11.10

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HTC-19 Insights: Why Develop HPLC–XRF?

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Frederic Lynen from the Separation Science Group in the Department of Organic and Macromolecular Chemistry at Ghent University, Belgium discusses why he worked with Gaëlle Spileers to develop a high performance liquid chromatography X-ray fluorescence (HPLC–XRF) technique to analyze brominated flame retardants and other pollutants.

Daniel Vik from Amgen Research Copenhagen, Denmark discusses the motivation behind applying machine learning to chromatographic retention time prediction and its growing importance in modern pharmaceutical research. He shares insights into the challenges of developing robust predictive models, their role in supporting high-throughput drug discovery workflows, and the potential of artificial intelligence to make analytical chemistry more efficient and scalable.

This week, Chromatography Online featured conversations from ASMS 2026 on untargeted MS/MS quantitation, RPLC-HRMS analysis of PLGA copolymers, and AI/ML predictions for non-targeted LC–ESI–HRMS workflows, alongside expert debate on harmonizing PFAS analytical methods and a sensor GC study examining whether water chasers affect alcohol metabolism.