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Process characterization for troubleshooting

Finding the root cause: A model-based approach for troubleshooting in protein chromatography

Chromatographic protein purification is subject to various effects which may lead to disturbances in the process. Column aging or process variations can, for example, lead to deviations in the eluate composition and hence to inconsistencies in pool purities and yield. To avoid carrying these disturbances over into the next batch, their source should be identified and, ideally, eliminated.

Graph to the case study Process characterization for troubleshooting
Figure: Results of the stoichiometric displacement model (SDM) parameter estimation by curve fitting of calibration runs: Simulations using a mechanistic model (solid lines) are displayed in comparison to the PLS model inline peak deconvolution (dotted lines).

Making troubleshooting more efficient via mechanistic modeling

Troubleshooting during processing leads to time loss and increasing costs. Causes of deviations are hard to identify visually and manual troubleshooting approaches are both time-consuming and expensive. Especially in continuous downstream processing, automated monitoring and control strategies are therefore attracting more and more interest. In this context, mechanistic models are promising, but have not yet been applied for a process analysis of biopharmaceutical protein purification.

Combination of spectral deconvolution with inverse mechanistic modeling

In the present case study, Process Analytical Technology (PAT) for accelerated and automated troubleshooting has been considered. In a first step, a calibration of Partial Least Square (PLS) Regression was performed. By means of this PLS model, protein concentrations could be quantified selectively, based on the mid-UV absorption spectra. After importing them into ChromX, the model parameters for all the species were determined via curve fitting.

Fast and automated troubleshooting for deviating chromatograms

Once the model was calibrated, it could be applied for process monitoring and troubleshooting. In the case of a process deviation, the mechanistic model could identify the root cause of the deviation by systematically adjusting process parameters in-silico until model predictions matched observed chromatograms. By means of this approach, a valuable modeling tool for root cause investigation was established. Following the procedure, typical causes for process deviations, such as the aging of columns or process variations, can be identified rapidly via a simple examination of the chromatogram.

See full paper “Application of spectral deconvolution and inverse mechanistic modeling as a tool for root cause investigation in protein chromatography

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