6thWave: AI News Hub

The integration of artificial intelligence (AI) in advanced therapy manufacturing is heavily reliant on the availability of robust hardware and monitoring software. According to Ioannis Papantoniou, an associate professor of tissue engineering and bioprocess development at KU Leuven in Belgium, the huge quantities of data required to train machine learning algorithms necessitate the implementation of automation in a systematic way. This includes the use of sensor technology, automation hardware, and process control and analytics. Papantoniou’s team has utilized machine learning to study the growth kinetics of donor progenitor cells, but emphasizes that acquiring meaningful data requires a large amount of donor cells, which is often a challenge for most people developing cell-based products. The adoption of new automated technologies, such as smart bioreactors and biosensors, is expected to expand the range of critical quality attributes that can be measured, making it more feasible to use AI for predicting complex cell behavior while controlling manufacturing processes. Papantoniou’s team is currently working on building a small factory to manufacture organoids, which will utilize AI to monitor and control the manufacturing process. Looking ahead, AI may even be used for managing decentralized manufacturing at sites based in different countries.

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