Division/Committee: [BIOT] Division of Biochemical Technology

Mechanistic models capable of describing bioseparations have long been available; however, their implementation into mainstream biopharmaceutical development has been limited. This is rapidly changing as the digital revolution is sweeping through the biopharmaceutical industry, resulting in new computational workflows that can be readily integrated with modeling to achieve intelligent and disruptive downstream process development. The need for process intensification, increased productivity, reduced costs, and bringing products to market faster has further necessitated the use of modeling in all stages of biopharmaceutical development.The session invites speakers from all fields to share their research and case studies in the modeling of bioseparations. In particular, we invite speakers to share their research in emerging modeling methods for all modalities (mAbs, therapeutic proteins, peptides, viral vectors, mRNA, etc.) and all separation methods (chromatography, filtration, viral clearance, etc.), and multi-step modeling approaches. We also encourage the submission of research involving hybrid modeling approaches such as combined statistical and mechanistic models, models that leverage protein sequence/structure, models that utilize developability/manufacturability data, and models based on molecular-scale simulations (fundamental studies, predictive models, or hybrid molecular/mechanistic models). We invite research that addresses key practical considerations for mechanistic models in bioseparations, including minimizing mechanistic modeling hurdles (resources, time, complexity, or analytical burden), assessing and improving model accuracy, transferring models as part of tech transfer and scale-up, or using models for regulatory filings or lifecycle management. Case studies about the implementation of in silico and mechanistic modeling in clinical and commercial manufacturing for process control, deviation management, process optimization, scale-up, process transfer, real-time lot release, etc., are also encouraged.

Marcel Ottens

Delft University of Technology

Appearances