Understanding the effects of system differences for parameter estimation and scale-up of high throughput chromatographic data.

ABSTRACT

In this paper, we evaluate how employing fraction collection and multistep gradients with RoboColumns® (Repligen, formally Atoll) affects both comparison to benchtop experimental data and column simulation parameter estimation. These operational differences arise from the RoboColumn® system (operated on an automated liquid handling device) requiring offline analysis for determination of elution profiles rather than the continuous in-line UV curves obtained with larger scale systems. In addition, multistep gradients are used to model the smooth linear gradients of larger scale systems because sequential injections are used to provide liquid flow. Comparisons of two sets of column simulations was first carried out to demonstrate that fraction collection reduced the first moments of the elution peaks by 1/2 of the fraction volumes. Additional column simulations determined that the effect of a multistep gradient approximation on retention volume was dependent upon the gradient step length. An empirical transformation was then developed to correct the first moments obtained from gradient experimental data using the RoboColumn® system. These corrected values provided a more direct comparison of the experimental data at different scales and resulted in a significant improvement in agreement with results obtained using a 20 mL benchtop column. Linear steric mass-action (SMA) parameters were then estimated using the corrected values and employed to successfully predict the performance of the benchtop system data. Finally, these parameters were demonstrated to be well suited for modeling the RoboColumn® gradient data when properly accounting for multistep gradients and fraction collection. This work continues previous investigations into understanding system differences associated with robotic liquid handling devices and proposes a methodology for properly accounting for operational differences to predict operation at larger scales using conventional chromatography systems.