Accelerating the vial-to-plant timeline with high-throughput experimentation

The increasing availability of automation, high-throughput experimentation (HTE), advanced analytics and machine learning is transforming the way chemical processes are designed and optimised. Rather than viewing these technologies as isolated tools, they can be integrated into data-centric workflows that accelerate decision-making from route selection through to manufacturing-ready processes.

 This webinar will explore how modern synthetic technologies can be combined with statistical experimental design, predictive modelling and process science to generate high-quality data that drives robust process development. Drawing on industrial case studies, examples will include high-throughput salt and solid-form screening, reaction optimisation using chemical space-filling experimental designs, regression modelling, design of experiments (DoE) and machine learning-assisted workflow development. The presentation will demonstrate how these approaches enable rapid identification of robust operating windows, improve process understanding, and support scale-up with greater confidence while reducing material consumption, development timelines and overall process mass intensity.