Shane Grosser discusses how Merck's Process Development Intensification Laboratory develops new tools and methods to increase the speed and decrease the cost of chemical process development.
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Process Fingerprinting in the Development of an Alkylation Reaction
Kevin Stone discusses how Merck Chemical Engineering Research & Development (CERD) leverages process fingerprinting tools in the development of an Alkylation Reaction. This presentation highlights how Merck utilizes rapid, data-driven decision making throughout chemical process development.
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The final chemical transformation in the synthesis of doravirine (an NNRTI drug intended for the treatment of HIV) is an alkylation reaction with several key process variables and interactions that had historically produced high sensitivities in reaction performance. In this work, high-throughput experimentation and data-intensive Process Analytical Technology (PAT) tools are applied to characterize and optimize this reaction for improved performance. With the use of automated reactors and sampling devices, large data sets are produced to:
By measuring solubility and addressing scale-up effects, the multi-phase reaction mixture is characterized. This case study highlights the ability to enable rapid, data-driven decision making in various phases of chemical process development.
Who Should Attend?
Scientists interested in process scale-up in the Pharmaceutical, Chemical, and Petrochemical Industries as well as Academia.
Presenter: Kevin Stone
Kevin Stone earned his Bachelors in Chemical Engineering from the University of Delaware. While obtaining his degree, he worked on various engineering projects with BASF and General Electric. Kevin has been working for Merck in the Department of Chemical Engineering Research & Development (CERD). He has supported several steps in the synthesis of an HIV NNRTI compound, doravirine, with a focus on the Active Pharmaceutical Ingredient (API) reaction and crystallization. He is also involved in a Merck Process Research & Development initiative to advance Data Rich Experimentation.