New research has produced computational tools for assisting the production of antibodies which can be used to treat a wide range of diseases.
The study, published in Computers & Chemical Engineering, details a new digital tool developed at Imperial College London which can predict and correct unforeseen disturbances in the production of monoclonal antibodies (mAbs), an antibody used to treat diseases including autoimmune conditions, cancer and asthma.
The new tool, known as DigiGlyc, could benefit the pharmaceutical industry by ensuring that antibody manufacturing operates under the conditions necessary to guarantee the required product quality.
Targeted therapeutic treatments
Monoclonal antibodies are favoured as a therapeutic treatment due to their ability to provide specific targeting for the immune system, but they can be challenging to manufacture because the processes use living cells which can vary in their production performance.
"Mathematical modelling enables us to ‘correct’ unforeseen changes in manufacturing systems quickly and efficiently. In this way we have the potential to improve product quality and guarantee stable operation." Dr Maria Papathanasiou Department of Chemical Engineering
DigiGlyc is a computational tool which uses mathematical modelling to predict and correct this performance to improve the efficiency and purity of the product. It uses industry 4.0 principles, where data-driven frameworks are applied to lab-based equipment, to produce optimised processes. In the case of monoclonal antibodies, DigiGlyc can calculate the most appropriate conditions for growing the living cells, which can maximise yield and quality
Lead author Dr Maria Papathanasiou said: “Mathematical modelling enables us to ‘correct’ unforeseen changes in manufacturing systems quickly and efficiently. In this way we have the potential to improve product quality and guarantee stable operation.
The next step would be exploring how we can couple this approach with continuous production methods, where adjustments can be made without stopping production. Working with industry to adopt this hybrid approach to manufacturing is crucial for the advancement of the sector.”
Hybrid manufacturing models
Researchers emphasise the importance of taking a hybrid approach to manufacturing processes and the benefit that process systems engineering can have on wet lab experiments, by reducing the amount of time it takes to troubleshoot production problems and implement effective solutions.
DigiGlyc was developed using models previously created by the paper’s co-author Professor Cleo Kontoravdi and provides data on how much antibodies can be produced and ways of optimising the glycosylation, a particular structural characteristic that has a significant impact on the quality and stability of the drug.
Professor Kontoravdi said: “We had previously used our process models for offline optimisation, but this publication paves the way for using them online for both optimisation and control.”
The next step for this technology is to ensure that it’s user friendly so that it can be adapted by industry and exploring how to introduce continuous production to the process, where adjustments can be made during manufacturing without having to pause operations.
'DigiGlyc: A hybrid tool for reactive scheduling in cell culture systems' by Papathanasiou et. al. is available in Computers & Chemical Engineering.
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