(234c) Predictive in silico Models for Cell Culture Process Development for Biologics Manufacturing
AIChE Annual Meeting
2021
2021 Annual Meeting
Food, Pharmaceutical & Bioengineering Division
Biomanufacturing with Advanced Mammalian Cell Culture Technologies
Tuesday, November 9, 2021 - 8:42am to 9:03am
In this study, we describe the use of statistical learning techniques to predict cell culture performance attributes and drug quantity and quality metrics on two process types: a fed batch process for a monoclonal antibody (mAb) product and a perfusion process for an enzyme product. We compare the performance of partial least squares regression (PLS-R), random forests (RF), extreme gradient boosting (XGBoost) and long short-term memory (LSTM) as modeling methods to achieve this aim. We use data collected from offline and online monitoring sources within laboratory-scale experiments, including online pH, temperature and dissolved oxygen probes as well as offline metabolite and nutrient concentrations of species such as glutamine, glucose, and lactate. We assess our models based on their ability to predict cell culture performance, product quantity and product quality multiple days into the future. We predict viable cell density (diagnostic of cell culture performance), product titers (measuring product quantity), and enzyme activity or monomer species (mAb), a measure of product purity (measuring one product quality attribute for the enzyme and mAb product) with reasonable accuracy (<=20% mean relative error) up to several days in advance. We find that XGBoost achieves the most consistent performance, followed closely by RF and LSTM models. PLS-R achieves comparatively worse performance on all tasks except VCD prediction. The strengths and weaknesses of these modeling approaches will be discussed.
We also interrogate our models to assess the physical significance of their predictions by performing a sensitivity analysis using descriptor ablation studies. Initial results suggest that the models developed are only weakly reliant on any single descriptor, likely due to correlation with other descriptors. For example, the lowest R2 (0.847) and highest MAE (9.11) for VCD prediction on the fed-batch process was found when the descriptor measuring the partial pressure of carbon dioxide was removed, compared to a baseline performance of 0.86 R2 and 8.75 MAE for the model with all descriptors included.
Our study provides a framework that will ultimately be used to shorten process development timelines and identify optimal, robust operating conditions. In turn, this has the potential to reduce R&D expenditures and improve the availability of life-saving medicines in a timely manner for patients worldwide.
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