A novel and systematic approach to identify the design space of pharmaceutical processes. (12th July 2018) - Bano, Gabriele; Wang, Zilong; Facco, Pierantonio; Bezzo, Fabrizio; Barolo, Massimiliano; Ierapetritou, Marianthi
Highlights: Projection on latent structures (PLS) is exploited to reduce the dimensionality of a feasibility problem. Radial Basis Function feasibility analysis is performed on a low dimensional latent representation of the input space. The methodology is used to determine the design space (DS) of a pharmaceutical process. Accurate DS identification on both mathematical test problems and integrated flowsheet models of continuous pharmaceutical processes. Abstract: Feasibility analysis is a mathematical technique that can be used to assist the identification of the design space (DS) of a pharmaceutical process, given the availability of a process model. One of its main drawbacks is that it suffers from the curse of dimensionality, i.e. simulations can potentially become computationally extremely expensive and very cumbersome when the number of input factors is large. Additionally, giving a graphical and compact representation of the high-dimensional design space is difficult. In this study, we propose a novel and systematic methodology to exploit partial least-squares (PLS) regression modelling to reduce the dimensionality of a feasibility problem. We use PLS to obtain a linear transformation between the original multidimensional input space and a lower dimensional latent space. We then apply a Radial Basis Function (RBF) adaptive sampling feasibility analysis on this lower dimensional space to identify the feasible region of the process. We assess the accuracy and robustness of the results with three metrics, and we critically discuss the criteria that should be adopted for the choice of the number of latent variables. The performance of the methodology is tested on three simulated case studies, one of which involving the continuous direct compaction of a pharmaceutical powder. In all case studies, the methodology shows to be effective in reducing the computational burden while maintaining an accurate and robust identification of the design space.