Hyperbox Mixture Regression for Process Performance Prediction in Antibody Production

TL;DR

This paper introduces a Hyperbox Mixture Regression model that improves prediction accuracy and efficiency in bioprocess performance, specifically in monoclonal antibody production, by effectively handling complex, high-dimensional time-series data.

Contribution

The paper presents a novel hyperbox-based regression model that dynamically partitions input space, enhancing predictive accuracy and computational efficiency in bioprocess data analysis.

Findings

HMR outperforms existing models in accuracy and speed.

HMR maintains robustness under uncertain bioprocess conditions.

HMR demonstrates interpretability in complex bioprocess datasets.

Abstract

This paper addresses the challenges of predicting bioprocess performance, particularly in monoclonal antibody (mAb) production, where conventional statistical methods often fall short due to time-series data's complexity and high dimensionality. We propose a novel Hyperbox Mixture Regression (HMR) model which employs hyperbox-based input space partitioning to enhance predictive accuracy while managing uncertainty inherent in bioprocess data. The HMR model is designed to dynamically generate hyperboxes for input samples in a single-pass process, thereby improving learning speed and reducing computational complexity. Our experimental study utilizes a dataset that contains 106 bioreactors. This study evaluates the model's performance in predicting critical quality attributes in monoclonal antibody manufacturing over a 15-day cultivation period. The results demonstrate that the HMR model outperforms comparable approximators in accuracy and learning speed and maintains interpretability and robustness under uncertain conditions. These findings underscore the potential of HMR as a powerful tool for enhancing predictive analytics in bioprocessing applications.