Root cause investigation of deviations in protein chromatography based on mechanistic models and artificial neural networks

- Simulations of potential process deviations based on a mechanistic model were used to construct an artificial neural network model.
- Experimental errors in ionic capacity and salt gradient length were imitated simultaneously to prove the practical capability of this method.
- By this approach, the time-consuming root cause investigation itself could be reduced down to milliseconds.
- This approach could be a building block for real-time root cause investigation in chromatography processes.

In protein chromatography, process variations, such as aging of column or process errors, can result in deviations of the product and impurity levels. Consequently, the process performance described by purity, yield, or production rate may decrease. Based on visual inspection of the UV signal, it is hard to identify the source of the error and almost unfeasible to determine the quantity of deviation. The problem becomes even more pronounced, if multiple root causes of the deviation are interconnected and lead to an observable deviation. In the presented work, a novel method based on the combination of mechanistic chromatography models and the artificial neural networks is suggested to solve this problem. In a case study using a model protein mixture, the determination of deviations in column capacity and elution gradient length was shown. Maximal errors of 1.5% and 4.90% for the prediction of deviation in column capacity and elution gradient length respectively demonstrated the capability of this method for root cause investigation.