Since its inception in early 2014, the Process Development Intensification Laboratory (iLab) within Chemical Process Development and Commercialization at Merck has been focused on the development of tools and techniques to enable acquisition of deeper, more fundamental process understanding in a resource sparing manner. This is predominantly centered around the concept of data intensification, specifically, around enabling more efficient data capture, data reduction and data analysis.
In this submission, we highlight the development and demonstration of novel, enabling technologies such as process modeling, bench-top automation, parallel processing, process analytical technologies and statistically designed experimentation. Furthermore, this submission uses a saponification deprotection reaction as a case study, highlighting the synergistic effects which can be realized through the application of many of the developed tools.
Specifically, we highlight the use of many of the tools the iLab has been employing, beginning with traditional DOE for selection of preliminary experimental conditions. We go on to discuss some of its limitations when the number of factors for investigation is high, prompting application of kinetic modeling for acquisition of deeper, more mechanistic process understanding. From there, it becomes apparent that alternative experimental techniques, specifically use of the Amigochem (a bench top automation tool combining a liquid handling robot for LC sampling and dilution with a parallel reaction station) offers an opportunity for building information-rich data sets rapidly. This enables kinetic profiling of the aforementioned DOE with minimal user intervention. We also demonstrated the use of probe-based sampling and dilution for unattended sampling of automated lab reactors. When coupled with online FTIR spectroscopy and chemometric techniques such as partial least squares regression, it becomes possible to gather compositional information and very detailed kinetic profiles rapidly in a wide variety of equipment types.
Simply collecting significant quantities of data is not always fruitful, however, unless that data can be transformed in some way to intimate process knowledge. We have also been developing means by which collected data can be used to build process models, and more importantly, how those process models can be used to drive future experimental design decisions. We have demonstrated two separate mathematical algorithms, one geared towards selection of optimal experiments to aid in model discrimination, and the other geared at selection of optimal experiments for kinetic parameter refinement.
Separately, many of the technologies developed by the iLab represent significant achievements in data collection, analysis and application in the development space. When combined as a holistic experimental design paradigm, there is significant synergy realized, and the complete toolkit stands to be truly transformative in the way in which process development is conducted in our area moving forward.
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