Elastic net with Monte Carlo sampling for data-based modeling in biopharmaceutical manufacturing facilities

Biopharmaceutical manufacturing involves multiple process steps that can be challenging to model. Oftentimes, operating conditions are studied in bench-scale experiments and then fixed to specific values during full-scale operations. This procedure limits the opportunity to tune process variables to correct for the effects of disturbances. Generating process models has the potential to increase the flexibility and controllability of the biomanufacturing processes. This article proposes a statistical modeling methodology to predict the outputs of biopharmaceutical operations. This methodology addresses two important ata-based modeling lastic net asso onte Carlo methods iopharmaceutical manufacturing egularization methods challenging characteristics typical of data collected in the biopharmaceutical industry: limited data availability and data heterogeneity. Motivated by the final aim of control, regularization methods, specifically the elastic net, are combined with sampling techniques similar to the bootstrap to develop mathematical models that use only a small number of input variables. This methodology is evaluated on an antibody manufacturing dataset.