A predictive tool
was developed to aid process design and to rationally
select optimal solvents for isolation of active pharmaceutical ingredients.
The objective was to minimize the experimental work required to design
a purification process by (i) starting from a rationally selected
crystallization solvent based on maximizing yield and minimizing solvent
consumption (with the constraint of maintaining a suspension density
which allows crystal suspension); (ii) for the crystallization solvent
identified from step 1, a list of potential isolation solvents (selected
based on a series of constraints) is ranked, based on thermodynamic
consideration of yield and predicted purity using a mass balance model;
and (iii) the most promising of the predicted combinations is verified
experimentally, and the process conditions are adjusted to maximize
impurity removal and maximize yield, taking into account mass transport
and kinetic considerations. Here, we present a solvent selection workflow
based on logical solvent ranking supported by solubility predictions,
coupled with digital tools to transfer material property information
between operations to predict the optimal purification strategy. This
approach addresses isolation, preserving the particle attributes generated
during crystallization, taking account of the risks of product precipitation
and particle dissolution during washing, and the selection of solvents,
which are favorable for drying.