Course Description:
Digitallization and modeling and simulation have transformed how biopharmaceutical companies design, transfer, and scale complex processes from lab to commercial manufacturing. Building on the same Quality by Design and risk based principles that underpin modern control strategies, modeling can act as a surrogate for classical process development by using in silico experimentation to define design spaces, identify critical parameters, and predict performance at scale before expensive engineering and cGMP runs. This webinar will introduce practical modeling frameworks for large and small molecule drug modalities and discuss how to integrate models with existing CMC and Quality Management Systems, while highlighting regulatory expectations for using modeling evidence to support filings. Attendees will see how an in-silico approach can address typical challenges such as raw material variability, complex analytics, automation, scale-up, and tech transfer, ultimately shortening development timelines while increasing confidence in commercial process robustness. This discussion will:
- Position modeling as an enabler of CMC strategy, analogous to how control strategies structure risk management across the lifecycle.
- Explain how mechanistic, data driven, and hybrid models can reduce the number of wet lab experiments across development, scale up, and tech transfer while preserving compliance with cGMP expectations.
- Illustrate the lifecycle of a model: concept, model development, verification/validation, maintenance, and integration into routine decision making and documentation (e.g., BLA/MAA support).
Key Topics:
Modeling as a virtual development lab
- Using models to develop design space and parameter interactions instead of extensive factorial DoE campaigns.
- Applying modeling to characterize robustness, define proven acceptable ranges, and support control strategy justification.
Modeling for scale up and tech transfer
- Translating lab scale understanding into pilot and commercial scale using transport phenomena, kinetics, and scale-dependent models.
- Using modeling outputs to structure tech transfer packages, set scale dependent limits, and anticipate failure modes at receiving sites.
Integrating modeling with automation and developing true Digital Twin
- Connecting PAT, historians, and MES/LIMS data streams to maintain and refine models over time.
- Enabling soft sensors, digital twins, and advanced process control elements that align with the formal control s