Tensorial approaches combining time series and batch data for the end-to-end batch manufacturing of monoclonal antibodies

The product quality for biopharmaceutical production processes is characterized in terms of critical quality attributes. Some quality attributes, however, cannot be easily measured in a timely fashion to support effective intervention and mitigation, particularly for assays that are time-consuming and/or late in the purification process. This article describes the application of predictive modeling techniques to industrial end-to-end biomanufacturing datasets for two monoclonal antibodies to predict critical quality attributes. Methods are proposed for combining time series and batch data that significantly improve the accuracy of the model predictions. These tools are able to take batch-to-batch correlations into account to construct more accurate predictive models from biomanufacturing datasets.