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Digital process design to define and deliver pharmaceutical particle attributes

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journal contribution
posted on 2023-07-25, 15:57 authored by Stephanie J Urwin, Magdalene WS Chong, Wei LiWei Li, John McGinty, Bhavik Mehta, Sara Ottoboni, Momina Pathan, Elke Prasad, Murray Robertson, Mark McGowan, Mais al-Attili, Ekaterina Gramadnikova, Mariam Siddique, Ian Houson, Helen Feilden, Brahim BenyahiaBrahim Benyahia, Cameron J Brown, Gavin W Halbert, Blair Johnston, Alison Nordon, Chris J Price, Chris RiellyChris Rielly, Jan Sefcik, Alastair J Florence

A digital-first approach to produce quality particles of an active pharmaceutical ingredient across crystallisation, washing and drying is presented, minimising material requirements and experimental burden during development. To demonstrate current predictive modelling capabilities, the production of two particle sizes (D90 = 42 and 120 µm) via crystallisation was targeted to deliver a predicted, measurable difference in in vitro dissolution performance. A parameterised population balance model considering primary nucleation, secondary nucleation, and crystal growth was used to select the modes of production for the different particle size batches. Solubility prediction aided solvent selection steps which also considered manufacturability and safety selection criteria. A wet milling model was parameterised and used to successfully produce a 90 g product batch with a particle size D90 of 49.3 µm, which was then used as the seeds for cooling crystallisation. A rigorous approach to minimising physical phenomena observed experimentally was implemented, successfully predicted the required conditions to produce material satisfying the particle size design objective of D90 of 120 µm in a seeded cooling crystallisation using a 5-stage MSMPR cascade. Product material was isolated using the filtration and washing processes designed, producing 71.2 g of agglomerated product with a primary particle D90 of 128 µm. Based on experimental observations, the population balance model was reparametrised to increase accuracy by inclusion of an agglomeration terms for the continuous cooling crystallisation. The dissolution performance for the two crystallised products is also demonstrated, and after 45 minutes 104.0 mg of the D90 of 49.3 µm material had dissolved, compared with 90.5 mg of the agglomerated material with D90 of 128 µm. Overall, 1513 g of the model compound was used to develop and demonstrate two laboratory scale manufacturing processes with specific particle size targets. This work highlights the challenges associated with a digitalfirst approach and limitations in current first-principles models are discussed that include dealing ab initio with encrustation, fouling or factors that affect dissolution other than particle size. 

Funding

Future Continuous Manufacturing and Advanced Crystallisation Research Hub

Engineering and Physical Sciences Research Council

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UKRPIF (UK Research Partnership Institute Fund) capital award, Scottish Funding Council ref. H13054, from the Higher Education Funding Council for England (HEFCE)

EPSRC and Innovate UK for funding a partnership between the University of Strathclyde and Siemens Process Systems Engineering Ltd (KTP 11937)

ARTICULAR: ARtificial inTelligence for Integrated ICT-enabled pharmaceUticaL mAnufactuRing

Engineering and Physical Sciences Research Council

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History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Chemical Engineering

Published in

Chemical Engineering Research and Design

Volume

196

Pages

726-749

Publisher

Elsevier

Version

  • VoR (Version of Record)

Rights holder

© The Author(s)

Publisher statement

This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

2023-07-03

Publication date

2023-07-06

Copyright date

2023

ISSN

0263-8762

eISSN

1744-3563

Language

  • en

Depositor

Prof Chris Rielly. Deposit date: 4 July 2023

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