Hybrid Modeling of the Reversed‐Phase Chromatographic Purification of an Oligonucleotide: Few‐Shot Learning From Differentiable Physics Solver‐in‐the‐Loop

Abstract

Hybrid models integrate mechanistic and data‐driven components, effectively addressing the challenges of limited process understanding and data availability typical of biopharmaceutical processes. In this study, we applied a hybrid modeling framework named differentiable physics solver‐in‐the‐loop (DP‐SOL) to describe the reversed‐phase chromatographic purification of an oligonucleotide, overcoming the mentioned limitations of purely mechanistic and data‐driven models. The framework establishes a connection between neural networks (NNs) and mechanistic models through differentiable physical operators and their gradients. We first collected a data set comprising six linear gradient elution experiments at different resin loadings and gradient slopes, split in three experiments each for training and testing, for few‐shot learning. The hyperparameters were determined through a grid search, resulting in a NN with two hidden layers and 14 nodes. Compared to a calibrated mechanistic model used for initialization of NN, the DP‐SOL hybrid model showed significant performance improvement on both training and testing sets, with 0.97 for the former. The good predictivity of DP‐SOL is attributed to the combination of mechanistic models and NNs at the solver level. As a novel and versatile hybrid modeling paradigm, DP‐SOL has the potential to significantly impact modeling approaches in the downstream processing field and the broader biopharmaceutical sector.