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

Tools

Pickles, Thomas and Mustoe, Chantal and Brown, Cameron and Florence, Alastair (2022) Autonomous DataFactory : high-throughput screening for large-scale data collection to inform medicine manufacture. British Journal of Pharmacy, 7 (2). ISSN 2058-8356 (https://doi.org/10.5920/bjpharm.1128)

[thumbnail of Pickles-etal-BJP-2022-Autonomous-DataFactory-high-throughput-screening-for-large-scale-data-collection]
Preview
 Text. Filename: Pickles_etal_BJP_2022_Autonomous_DataFactory_high_throughput_screening_for_large_scale_data_collection.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (413kB)| Preview

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.

ORCID iDs

Pickles, Thomas, Mustoe, Chantal, Brown, Cameron ORCID logoORCID: https://orcid.org/0000-0001-7091-1721 and Florence, Alastair ORCID logoORCID: https://orcid.org/0000-0002-9706-8364;
CORE (COnnecting REpositories)