A comprehensive assessment of the applicability of RoboColumn as a chromatography scale-down model for use in biopharmaceutical process validation.

High-throughput process development has become a standard practice in the biopharmaceutical industry to enable time, cost, and material savings. In downstream biopharmaceutical process development, miniaturized, parallelized chromatography columns, known as RoboColumn, have become the standard for process development, as RoboColumn have shown generally comparable performance to bench and manufacturing scale columns. However, RoboColumn have yet to be widely implemented in process validation and characterization, where many multifactor experiments are typically executed, and there is a strong value proposition for performing high-throughput experiments. The hesitancy to utilize RoboColumn in process validation arises from scale differences that result in exacerbated peak broadening at RoboColumn scale relative to traditional bench or manufacturing scales. Thus, to support reliable application of RoboColumn in process validation, the present study provides a comprehensive investigation to understand how scale differences affect chromatographic performance by comparing RoboColumn, bench, and manufacturing scales using seven different production processes covering three different antibody formats, five different resin types, and three chromatographic modes of operation. RoboColumn chromatographic performance was compared at target and off-target conditions to emulate scale-down model qualification and multifactor studies, respectively. RoboColumn demonstrated good comparability at both target and off-target process conditions. To further demonstrate an understanding of comparability, a study was performed to show a rare case in which product quality offsets may occur as a result RoboColumn scale differences. By showing scale comparability and an understanding of potential offsets, this work demonstrates that RoboColumn can be used in any stage of process development, including process validation and characterization.

Prediction uncertainty assessment of chromatography models using Bayesian inference.

Mechanistic modeling of chromatography has been around in academia for decades and has gained increased support in pharmaceutical companies in recent years. Despite the large number of published successful applications, process development in the pharmaceutical industry today still does not fully benefit from a systematic mechanistic model-based approach. The hesitation on the part of industry to systematically apply mechanistic models can often be attributed to the absence of a general approach for determining if a model is qualified to support decision making in process development. In this work a Bayesian framework for the calibration and quality assessment of mechanistic chromatography models is introduced. Bayesian Markov Chain Monte Carlo is used to assess parameter uncertainty by generating samples from the parameter posterior distribution. Once the parameter posterior distribution has been estimated, it can be used to propagate the parameter uncertainty to model predictions, allowing a prediction-based uncertainty assessment of the model. The benefit of this uncertainty assessment is demonstrated using the example of a mechanistic model describing the separation of an antibody from its impurities on a strong cation exchanger. The mechanistic model was calibrated at moderate column load density and used to make extrapolations at high load conditions. Using the Bayesian framework, it could be shown that despite significant parameter uncertainty, the model can extrapolate beyond observed process conditions with high accuracy and is qualified to support process development.