Integrating Functional Principal Component Analysis with Data-Rich Experimentation for Enhanced Drug Substance Development

The combination of laboratory automation and data-rich experimentation has led to significant improvements in experimental efficiency, reproducibility, and throughput compared to conventional methods. These transformative technologies have proven particularly valuable in drug substance process characterization studies, where the ability to rapidly generate volumes of reaction data to maximize process understanding for ever-increasing regulatory expectations is of paramount importance. These highly dense data sets offer significant potential when coupled with data-driven modeling to quantify reaction dynamics and sensitivities for process knowledge and optimization. To ensure that this analysis is applied throughout drug substance reaction development, identifying a data-driven modeling approach capable of describing diverse reaction trends is of critical need. In this study, functional principal component analysis for data-driven reaction modeling was performed to highlight its applicability for drug substance development. To demonstrate this methodology, we employed automated reactor and sampling technologies in the process characterization studies of a heterogeneous fluorination reaction using gaseous trimethylamine. These data-rich experiments were structured according to a 24 full factorial design of experiment, each comprising at least 12 reaction time samples. By applying functional principal component analysis to various reaction responses, the optimal design space for manufacturing operations was easily identified.