Advanced therapy manufacturers require robust hardware and monitoring software before they can benefit from advances in artificial intelligence. That’s the view of Ioannis Papantoniou, PhD, an associate professor of tissue engineering and bioprocess development at KU Leuven in Belgium. According to Papantoniou, who spoke at the , the huge quantities of data required to train machine learning algorithms mean it’s essential to already have necessary sensor technology, automation hardware, and process control and analytics in place.
“You need to implement automation in a systematic way or else it’s just numbers—like a brain with no hands,” he explains. Papantoniou explains that among his team’s projects, they used machine learning to study the growth kinetics of donor progenitor cells in scale-down suspension bioreactor systems. However, to acquire meaningful data, they trained the AI on data collected on cells from more than two hundred donors.
“To get meaningful AI, you need more donor cells than are available to most people developing cell-based products,” he points out. “The AI has to be trained with a given number of data points, calibrated, and validated with another, independent control dataset.” At that time, he felt the advanced therapy industry was producing enough data to benefit from AI.
But now, he says, that’s beginning to change with the adoption and integration of new automated technologies. These include smart bioreactors from companies, .