Characterisation, modelling and prediction of fouling behaviour in virus filtration in mAb processes

Ensuring viral safety in biomanufacturing is crucial for patient safety and compliance with regulatory standards, particularly in the production of monoclonal antibodies (mAbs) through mammalian cell culture. Failures in virus filtration, whether due to filter fouling or breaches, can lead to reduced product yields and costly batch losses, affecting both process efficiency and sustainability. Current methods to predict virus filtration performance have limitations, particularly as they do not fully account for the complexities of real bioprocess materials.

To address this, UCL is leading research to develop innovative scale-down tools and modelling techniques specifically tailored for virus filtration. This approach will help optimize process performance by enhancing understanding of how filtration is impacted by different material characteristics.

The project will establish a comprehensive, data-driven framework to predict filter fouling behaviour and determine ideal filtration conditions. By combining experimental insights with machine learning and mechanistic modelling, this research aims to create a hybrid model that adapts to diverse filtration scenarios, ensuring robust performance and maximizing efficiency. Additionally, this work aligns with sustainability objectives by identifying optimal conditions that balance fouling management with environmental impact considerations.

Application deadline is on 28th January 2025 at 13:00 UK time.

Guidance how to apply can be found here, and only applications submitted via the official portal will be considered for interviews:

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