An approach to small-model generation and calibrating small-scale models to reliably predict performance at scale is presented.
Approaches to the generation of process models, optimization techniques, and application of a design space are explored.
Care needs to be made to match the method of limit determination to the analytical method.
Understanding the influence of change events on product performance is a necessity to routine drug development, transfer, and validation.
The ability to define a scientifically justified and statistically sound sampling procedure is a fundamental skill in modern systematic drug development.
Design space generation is encouraged in new product development.
Characterization of stability performance provides a clear, statistically defendable method for determining accelerated stability.
Design of experiment is a powerful development tool for method characterization and method validation.
Variation understanding and modeling is a core component of modern drug development.
Knowledge of product or process acceptance criterion is crucial in design space.