A digital tool for the automatic identification of anomalous cell cultures in biopharmaceutical process development

The development of new monoclonal antibodies (mAb) is a long-lasting and expensive procedure. Digital models can be adopted to reduce research costs and accelerate timelines. During mAb development, Ambr®15 is a small-scale, multi-parallel bioreactor platform used to assess performance of different cell lines and find the most productive and stable ones. Many factors affect the culture performance variability and often determine anomalies in the experimental batches. Those anomalies are neither easy, nor fast to be identified even by expert scientists. In this work, a tool for the automatic identification of cell culture anomalies and outlier experimental batches in Ambr®15 scale is presented. The software, calibrated on historical data of the experimental batches, effectively identifies through assumption-free modeling anomalies and diagnoses the root cause of cell lines non-standard behavior, representing also the first application of these methodologies for the development of mAbs. Accordingly, it represents a tool of invaluable importance to speed-up analysis of experimental data and reduce the effort of operators, thus reducing development timeline and costs.