Smart Process Analytics for the End-to-End Batch Manufacturing of
Monoclonal Antibodies
Abstract
For many modern biopharmaceutical processes, manufacturers develop
data-driven models using data analytics/machine learning (DA/ML)
methods. The challenge is how to select the best methods for a specific
dataset to construct the most accurate and reliable model. This article
describes the application of smart process data analytics software to
industrial end-to-end biomanufacturing datasets for monoclonal antibody
production to automate the determination of the best DA/ML tools for
model construction and process understanding. The application
demonstrates that smart process data analytics software captures
product- and process-specific characteristics for two different
monoclonal antibody productions. This study provides tools that can be
widely applied to biomanufacturing processes for root cause analysis,
prediction, and control.