Luedeking-Piret regression for multi-step-ahead forecasting and clone selection in monoclonal antibodies biomanufacturing
Year: 2025
Type: article
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. Selecting the best cell lines for antibody production is a lengthy process in biomanufacturing. Penghua Wang and colleagues introduced a hybrid model that uses early-stage data to accurately predict final yields, accelerating the clone selection timeline.
Source: Communications Engineering
Institution: University of Oklahoma
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,190
Awards: G175777928, G3921413955
OpenAlex ID: W4416771869