Non-invasive and continuous monitoring of 3D stem cell culture in a bioreactor

Abstract

The development and discovery of new drugs and therapy products in regenerative medicine are carried out through multiple stages which involve several strict quality protocols. The use of cell-based model systems is the most relevant alternative to deviate from animal testing. However, the underlying biomanufacturing process in a 3D environment can be prone to failures or unexpected behaviors such as cell growth anomalies or inconsistencies in cell aggregation, which can seriously affect the quality of the result. Therefore, it is necessary to establish a non-invasive and continuous monitoring mechanism that minimizes contamination risks, allows taking early decisions and enables large-scale production in the long term. The results of our embedded machine learning approach show that it is feasible to train machine learning models that can operate on a resourcelimited embedded board with acceptable prediction accuracy for the estimation of the average size of growing stem cell spheroids. Further experiments are needed to investigate the full information potential of the recorded process data.

Keywords

• deep learning
• linear regression
• rotating tube
• spheroids
• hipscs
• nscs
• growth behavior
• embedded vision