Model-based design of pharmaceutical manufacturing processes has received much interest in academia and industry. Model parameter uncertainties, however, might deteriorate the predicted process performance. Probability-based robust process design concepts as a countermeasure against uncertainties might be implemented. Here, parameter uncertainties are typically limited to Gaussian parameter distributions. However, parameter uncertainties derived with experimental data can be correlated and arbitrarily distributed. In our previous work, transformation techniques were combined with the point estimate method (PEM) to address non-Gaussian and correlated parameter distributions, but at the cost of additional nonlinearities and approximation errors. In this work, we take advantage of Gaussian mixture distributions (GMD) and decompose the parameter distribution into a finite set of Gaussian distributions using the Expectation-Maximization approach. Combining the GMD with the PEM ensures a proper and effective uncertainty quantification. The improved PEM algorithm is applied to a freezedrying process (lyophilization) aiming for high-quality products with minimum processing time. Results obtained suggest that the novel GMD-PEM algorithm has the potential to outperform conventional robustification concepts regarding credibility and efficiency. (C) 2019 Elsevier Ltd. All rights reserved.