A Scalable Deep Learning Approach for Real-Time Multivariate Monitoring of Biopharmaceutical Processes With No Prior Product-Specific History.
Journal:
Biotechnology and bioengineering
Published Date:
Jun 2, 2025
Abstract
Real-time multivariate statistical process monitoring (RT-MSPM) is essential to monitor health of bio-pharmaceutical processes and detect anomalies and faults early in the process. RT-MSPM methods are commonly used to monitor cell culture process operations in biologics drug substance manufacturing. Batch evolution models (BEMs) are among common RT-MSPM methods. As an alternative to BEMs, it is possible to develop multiple models to monitor different phases of a batch process. If certain statistical properties are satisfied, a multistage algorithm can be leveraged to detect steady state operation of a batch and process the corresponding time-series in a manner to leverage data from other product recipes to monitor a new product with no prior history. This is specifically useful in modern biopharmaceutical manufacturing facilities, which frequently switch from producing one medicine to another. In this article, a novel real-time deep learning framework to monitor the health of biopharmaceutical processes with no prior product-specific history is proposed. Autoencoders (AEs), in conjunction with a multistage real-time data processing algorithm, are leveraged to detect, prevent and identify the root causes of potential anomalies and faults in cell culture manufacturing processes to produce monoclonal antibodies with no prior history. A novel algorithm for real-time root cause identification of anomalies is developed to generate real-time contribution charts for AEs. The performance of the new fault detection and isolation strategy is compared with conventional methods. Given the nonlinear architecture of AEs in comparison to conventional linear methods, AEs consistently provide more robust and stronger evidence for anomalous patterns using a combination of information in residuals and latent space. The proposed framework is successfully tested within a scalable software product for real-time monitoring of manufacturing cell culture bioreactors.
