Development of new, complex drug molecules is becoming more expensive and time consuming. A common way to increase development speed is by employing kinetic models. However, these typically consume significant amounts of material because they require data-rich experimentation and are therefore built later in the development process.

This application note presents recent research carried out by Argüelles et al. from Eli Lilly and Company, demonstrating how to combine automated laboratory platforms with advanced data analysis and modelling methods. This approach enables researchers to efficiently generate high-quality kinetic data and translate it into reliable reaction models at earlier stages of development. The models provide deeper insight into reaction me chanisms and kinetics, supporting model-based development and optimization in chemical and pharmaceutical processes.

The application note outlines an innovative workflow for integrating automated medium-throughput experimentation with kinetic model development. It demonstrates how this strategy can accelerate process understanding and reveal mechanistic insights while reducing experimental effort. It also highlights how scalable kinetic models can strengthen decision-making confidence during process development and scale-up.