Following a model-centric strategy in the development of a manufacturing process for a new medicine empowers the simultaneous study of a large number of process parameters, which is large enough to exceed the capability of a graphic representation of the interactions across them. This work presents a discussion regarding the identification, description, and communication of multidimensional design spaces of high order. It introduces the reader to mathematical tools developed by the process systems engineering community that become relevant in the challenge to replace graphics as a means to describe and communicate a design space. Concepts like process f lexibility are discussed and illustrated. The paper also introduces geometric projection as a way to capture and describe the shape of the design space in an easier form (than that of the complete mechanistic model) that can be communicated to the regulator. An assessment is presented regarding the key elements communicated by a graphical representation of a design space, and alternate ways of conveying the same information using mathematics are suggested. These ideas are illustrated by applying them to the identification and definition of a design space for a chemical reaction step and the digital risk assessment for a packed bed adsorption step.

Due to the importance of the Gibbs free energy of solvation in understanding many physicochemical phenomena, including lipophilicity, phase equilibria and liquid-phase reaction equilibrium and kinetics, there is a need for predictive models that can be applied across large sets of solvents and solutes. In this paper, we propose two quantitative structure property relationships (QSPRs) to predict the Gibbs free energy of solvation, developed using partial least squares (PLS) and multivariate linear regression (MLR) methods for 295 solutes in 210 solvents with total number of data points of 1777. Unlike other QSPR models, the proposed models are not restricted to a specific solvent or solute. Furthermore, while most QSPR models include either experimental or quantum mechanical descriptors, the proposed models combine both, using experimental descriptors to represent the solvent and quantum mechanical descriptors to represent the solute. Up to twelve experimental descriptors and nine quantum mechanical descriptors are considered in the proposed models. Extensive internal and external validation is undertaken to assess model accuracy in predicting the Gibbs free energy of solvation for a large number of solute/solvent pairs. The best MLR model, which includes three solute descriptors and two solvent properties, yields a coefficient of determination (R 2 ) of 0.88 and a root mean squared error (RMSE) of 0.59 kcal mol À1 for the training set. The best PLS model includes six latent variables, and has an R 2 value of 0.91 and a RMSE of 0.52 kcal mol À1 . The proposed models are compared to selected results based on continuum solvation quantum chemistry calculations. They enable the fast prediction of the Gibbs free energy of solvation of a wide range of solutes in different solvents.

This work describes a modeling-aided approach to scale-up high-shear rotor−stator wet milling processes for pharmaceutical applications. A population balance equation was used that applies known breakage distribution functions and specific breakage rate to provide valuable insight into the significance of different scale-up factors to predict milling performance as well as the importance of accounting for flow-induced breakage in recirculation configurations. Case studies involved the size reduction of platelike and rodlike organic crystalline compounds.