In-silico first: Model-based Product and Process Development for Accelerated and Efficient Scale-up and Tech-transfer

Bringing a molecule to market is challenging, especially under tight timelines, limited budgets, and the constraints of sensitive and costly materials. Traditional process development and scale-up often relies on manual work, consuming significant resources and offering limited insights into CMAs~CPPs~CQAs correlation. This approach results in high costs, lengthy timelines, and frequent quality issues. While the QbD framework promotes knowledge-driven development, its adoption remains limited. Developers often work with small datasets and lack mechanistic understanding, complicating scale-up processes that depend on transport phenomena and kinetics. Mechanistic modeling addresses these challenges by connecting materials, processes, and products through thermodynamics and kinetics. Extended into Digital Twins, it enables predictive analysis, optimization, and efficient scale-up.

This presentation will showcase the application of mechanistic modeling based Digital Twin, from fundamental concepts to real-world industrial case studies. We will explore its benefits, challenges, workflows, ROI, and applications in process development, scale-up, and technology transfer.