Data intelligence for process performance prediction in biologics manufacturing.

• Time-series ML pipeline for predicting process performance in biologics manufacturing. • Distinguish poor-, moderate, and high-performing processes in first half of operation. • Identify "grey zones" of performance where decision should be delayed until later. • Automation for bioprocess QbD compliance to ensure reproducibility in manufacturing. Despite the availability of large amount of data in bioprocess databases, little has been done for its retrospective analysis for process improvement. Historic bioprocess data is multivariate time-series, and due to its inherent nature, is incompatible with a variety of statistical methods employed in data analysis resulting in the lack of a tailored methodology. We present here an integrative framework of knowledge discovery tailored for handling historical bioprocess datasets. The pipeline successfully predicts process performance at harvest from an early time point, and robustly identifies the most relevant process parameters to model process performance. We present the utility of this pipeline on biologics manufacturing data from upstream bioprocess development for antibody production by mammalian cells. The proposed multi-model system that employs machine learning can predict performance at harvest after two weeks of operation with satisfactory accuracy employing data generated as early as on the sixth day of the culture. [ABSTRACT FROM AUTHOR]