Luedeking-Piret regression for multi-step-ahead forecasting and clone selection in monoclonal antibodies biomanufacturing

Abstract

Efficient production of monoclonal antibodies (mAb) using Chinese Hamster Ovary (CHO) cells is central to pharmaceutical biomanufacturing. The clone selection process traditionally requires lengthy 7-to-14-day assessments to evaluate performance, which extends development timelines. Here we introduce a hybrid Luedeking-Piret Regression model that integrates mechanistic insights with machine learning to more accurately predict mAb yields in fed-batch CHO cultures. Using experimental data from the early growth stages (up to day 9) of seven (n=7) distinct CHO cultures, the model performed multi-step-ahead forecasting to predict final production. The model predicted monoclonal antibody titers on day 16 with a mean percentage error of 5.85%, correctly selected higher-performing clones in 76.2% of trials from leave-two-out cross-validation and accurately forecasted daily production trajectories from day 10 to day 16. The model’s multi-step-ahead forecasting capabilities have the potential to accelerate clone selection, providing the biomanufacturing community with a computationally straightforward algorithm for predicting production yields.