Autonomous DataFactory: High-throughput screening for large-scale data collection to inform medicine manufacture

Abstract

Using small-scale crystallisation to inform downstream processes, we can reduce time and material costs in medicine manufacturing. This work introduces a preliminary workflow for information-rich data collection of crystallisation parameters including solubility, induction time, growth rate, secondary nucleation rate, particle shape and size. Large-scale data collection was achieved for 6 active pharmaceutical ingredients (APIs) in 31 solvents in less than 9 months with the results for aspirin presented here. Highlights include the identification of 24 potential alternative crystallisation solvents for manufacturing aspirin, all of which yield the biorelevant polymorph. Automation of this workflow will enable the use of robotics to further reduce time and material usage when conducting crystallisation experiments for future APIs.

Keywords

aspirin, solubility, workflow, crystallisation

How to Cite

Pickles, T., Mustoe, C., Brown, C. & Florence, A., (2022) “Autonomous DataFactory: High-throughput screening for large-scale data collection to inform medicine manufacture”, British Journal of Pharmacy 7(2). doi: https://doi.org/10.5920/bjpharm.1128

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Authors

Thomas Pickles (University of Strathclyde)
Chantal Mustoe (CMAC)
Cameron Brown (CMAC)
Alastair Florence (CMAC)

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Creative Commons Attribution 4.0

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