Team Leader, Process Analytics GSK Philadelphia, Pennsylvania
Data driven and hybrid models are routinely being used by pharmaceutical companies to assist different aspects of product and process development and manufacturing. Standard applications include model-based experimental design, in-silico control strategy definition, process optimization and process monitoring/control. In this presentation, emerging applications such as hybrid models for end-to-end process simulation of biologics manufacturing processes or hybrid computational fluid dynamics (CFD)/surrogate models for mixing studies will be presented.
Learning Objectives:
Upon completion, participants will be able to understand the definition of data driven and hybrid models in a pharmaceutical context.
Upon completion, participants will be able to identify the key applications of data driven and hybrid models for product and process development in a pharmaceutical context.
Upon completion, participants will be able to identify emerging applications within the pharmaceutical industry of data driven and hybrid models.
Upon completion, participants will be able to familiarize with some case studies of application of data driven and hybrid models in a pharmaceutical context.
Upon completion, participants will be able to internalize the learnings from this presentation into their daily product and process development activities.