Comprehensive Sensitivity Analysis and Process Risk Assessment of Large Scale Pharmaceutical Crystallization Processes Abstract   This work presents an in silico tool that supports crystallization process development and optimization studies by means of mechanistic modeling, uncertainty identification, comprehensive sensitivity analysis and a quantified process risk assessment. Kinetic model parameters and operation design parameters are considered as a source of uncertainty and variation. Monte Carlo simulations are performed to propagate input uncertaintyvariation to model output in terms of process yield, mean crystal diameter and size distribution. To quantify the individual effects and importance of these parameters, global sensitivity analysis e.g. Morris Screening and variancebased decomposition, is performed. The process risk is defined as failure to reach target product specifications and its consequences for the given design space is quantified. This promising study shows, that global uncertainty and sensitivity analysis coupled with the quantification of process risk assessment is a powerful tool and should be of interest to those participating in effective and efficient crystallization process development.

Optimization Of Pharmaceutical Reactions Using The Dynamic Response Surface Methodology Abstract   Design of Dynamic Experiments (DoDE, Georgakis, 2013) and Dynamic Response Surface Methodology (DRSM, Klebanov and Georgakis, 2016; Wang and Georgakis, 2017) are two recent extensions to the Design of Experiments (DoE) Methodology for datadriven modeling. The DRSM models are used here for the optimization of the operating conditions of organic reactions in pharmaceutical process development. In particular, we investigate the problem of maximizing the operating time window, during which the concentrations of different species satisfy their preset constraints. This ensures the existence of a time window that is wide enough for a laboratory confirmation of acceptable compositional ranges in the final reaction mixture. We also consider the robustness of these optimization results with respect to deviations of the operating conditions and the inherent uncertainties in the DRSM model. Two case studies are examined using either simulated data from a complex reaction network or experimental data from an industrial example. In the latter case, we note the possibility of an optimization pitfall that a fractional factorial design can lead us to, when all the factors are significant and additional experimental data to improve the designu0027s resolution are not available.