The pharmaceutical analytical laboratory is undergoing a digital transformation. This presentation explores how automation, modeling and simulation, and GenAI are reshaping drug product analytical research and development, enabling scientists to work faster, smarter, and with greater confidence.
Automation platforms, including multi-dose dissolution, robotic forced degradation, and automated sample preparation, reduce manual burden, minimize error, and enable parallel experimentation, shifting scientist focus toward data analysis and scientific reasoning.
Modeling tools such as JMP, DryLab, Fusion, and ASAP replace trial-and-error method development with statistically rigorous Design of Experiments and Prediction approaches, producing data packages that support GMP validation and regulatory decision-making. GenAI applications in data analysis, literature retrieval, and report writing are also presented, with demonstrated time savings in a regulated analytical environment.
Rather than focusing on a single test method or program, the talk emphasizes practical adoption strategies, real-world case studies, lessons learned, and criteria for selecting high impact digital use cases. Attendees will leave with a clear framework for applying digital first science to improve efficiency, quality, and reliability in pharmaceutical analysis, and ultimately benefits patients.
Learning Objectives:
Upon completion, participants will be able to: Describe a digital‑first framework for analytical development.
Identify high‑impact opportunities for automation, modeling, and artificial intelligence.
Apply a risk‑based approach to adopting digital tools in regulated workflows.
