Advanced score system and automated search strategies for parameter estimation in mechanistic chromatography modeling

The use of predictive models to develop chromatography-based purification processes

“…The sum of squared differences between the simulated chromatogram and the experimental chromatogram is frequently used as an objective function to assess the quality of the model prediction. This objective function is sensitive to the relative timings of the elution peaks rather than their shapes, leading to recommendations for a new objective function system specifically designed for chromatography modeling (Heymann et al, 2022). This system utilizes the Frontiers in Bioengineering and Biotechnology frontiersin.org "shape" objective function, which is measured by the maximum of the Pearson correlation coefficient between the simulated and the experimental chromatogram over a continuous range of time offsets.…”

“…Importantly, the corresponding parameters are interdependent during model calibration. For example, an inaccurately determined column porosity can distorted the value calculated for the equilibrium constant when fitting a chromatography model to a given peak ( Heymann et al, 2022 ). Various methods have been proposed to determine equilibrium parameters ( Shukla et al, 1998 ; Osberghaus et al, 2012a ; Bernau et al, 2021 ) but the experimental determination of different types of liquid volumes in columns remains challenging, especially for packed-bed chromatography using porous, spherical beads as the stationary phase.…”

“…The third option involves the use of modelling software to inversely fit parameters by incrementally adjusting model parameters within a mechanistic model simulation until the simulated chromatogram best matches the experimental target data ( Osberghaus et al, 2012a ; Heymann et al, 2022 ). This method is the most computationally expensive of the three, but it allows the creation of complete chromatogram predictions using the fitted parameter values and resulting model.…”

“…A combination of approach 2) to estimate parameters in the linear range of the isotherm and the inverse fitting 3) to estimate the non-linear parameters has been proven as robust method for model calibration ( Saleh et al, 2020 ). Parameter estimation algorithms can also get stuck in local minima and take impractically long timescales to converge, e.g., several days ( Heymann et al, 2022 ). This can be overcome by using modern genetic algorithms ( Heymann et al, 2022 ) or Bayesian inference ( Briskot et al, 2019 ; Jäpel and Buyel 2022 ).…”

“…Parameter estimation algorithms can also get stuck in local minima and take impractically long timescales to converge, e.g., several days ( Heymann et al, 2022 ). This can be overcome by using modern genetic algorithms ( Heymann et al, 2022 ) or Bayesian inference ( Briskot et al, 2019 ; Jäpel and Buyel 2022 ). The sum of squared differences between the simulated chromatogram and the experimental chromatogram is frequently used as an objective function to assess the quality of the model prediction.…”