Bioprocessing automation in cell therapy manufacturing: Outcomes of special interest group automation workshop.

Autor: Ball O; Biolacuna, Oxford, UK. Electronic address: oliver.ball@biolacuna.com., Robinson S; Biolacuna, Oxford, UK., Bure K; AveriCELL, Northborough, MA, USA., Brindley DA; Department of Paediatrics, University of Oxford, Oxford, UK. Electronic address: david.brindley@paediatrics.ox.ac.uk., Mccall D; Phacilitate, London, UK.
Jazyk: angličtina
Zdroj: Cytotherapy [Cytotherapy] 2018 Apr; Vol. 20 (4), pp. 592-599. Date of Electronic Publication: 2018 Feb 13.
DOI: 10.1016/j.jcyt.2018.01.005
Abstrakt: Phacilitate held a Special Interest Group workshop event in Edinburgh, UK, in May 2017. The event brought together leading stakeholders in the cell therapy bioprocessing field to identify present and future challenges and propose potential solutions to automation in cell therapy bioprocessing. Here, we review and summarize discussions from the event. Deep biological understanding of a product, its mechanism of action and indication pathogenesis underpin many factors relating to bioprocessing and automation. To fully exploit the opportunities of bioprocess automation, therapeutics developers must closely consider whether an automation strategy is applicable, how to design an 'automatable' bioprocess and how to implement process modifications with minimal disruption. Major decisions around bioprocess automation strategy should involve all relevant stakeholders; communication between technical and business strategy decision-makers is of particular importance. Developers should leverage automation to implement in-process testing, in turn applicable to process optimization, quality assurance (QA)/ quality control (QC), batch failure control, adaptive manufacturing and regulatory demands, but a lack of precedent and technical opportunities can complicate such efforts. Sparse standardization across product characterization, hardware components and software platforms is perceived to complicate efforts to implement automation. The use of advanced algorithmic approaches such as machine learning may have application to bioprocess and supply chain optimization. Automation can substantially de-risk the wider supply chain, including tracking and traceability, cryopreservation and thawing and logistics. The regulatory implications of automation are currently unclear because few hardware options exist and novel solutions require case-by-case validation, but automation can present attractive regulatory incentives.
(Copyright © 2018 International Society for Cellular Therapy. Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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