Computationally efficient identification of probabilistic design spaces through application of metamodeling and adaptive sampling

Abstract

The design space (DS) is defined as the combination of materials and process conditions which provides assurance of quality for a pharmaceutical product (e.g. purity, potency, uniformity). A model-based approach to identify a probability-based design space requires simulations across the entire process parameter space (certain) and the uncertain model parameter space and material properties space if explicitly considered by the model. This exercise is a demanding task. A novel theoretical and numerical framework for determining probabilistic DS using metamodelling and adaptive sampling is developed. Several approaches were proposed and tested among which the most efficient is a new multi-step adaptive technique based using a metamodel for a probability map as an acceptance-rejection criterion to optimize sampling to identify the DS. It is shown that application of metamodel-based filters can significantly reduce model complexity and computational costs with speed up of two orders of magnitude observed here.