A novel integrated workflow for isolation solvent selection using prediction and modelling

Sara Ottoboni, Bruce Wareham, Antony Vassileiou, Murray Robertson, Cameron J. Brown, Blair Johnston, Chris J. Price

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
61 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)1143-1159
Number of pages17
JournalOrganic Process Research and Development
Issue number5
Early online date5 May 2021
Publication statusPublished - 21 May 2021


  • solvent selection
  • purification
  • solubility prediction
  • workflow procedure
  • crystallisation
  • isolation
  • Machine Learning


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