Probabilistic pressure-flow operating space for chromatographic resins using mechanistic modeling.

Pressure drop across chromatography columns at constant bed height is affected by column diameter and can be difficult to predict without large-scale data. Modern resin engineering has decreased the focus on pressure drop due to the widespread use of large beads and rigid base matrices. However, with the recent development of several small-bead resins optimized for bioprocessing entering the market, pressure drop has justified a regain of attention. This work seeks to develop and apply a mechanistic model based on force balances for predicting pressure drop across scales. With this approach, few small-scale experiments can be used for calibration, and pressure-flow data for large-scale packs can then be predicted with minimum at-scale data. The model was first tested using Phenyl Sepharose 6 FF by calibrating at small scale and then validating the model predictions at larger scale. Strong agreement was observed between predicted and experimental results. The model was then calibrated for seven resins and used to determine a probabilistic operating space calculated based on the likelihood of exceeding pressure limits at scale. Safe operating ranges, accounting for the inherent variability in column packing and resin manufacturing, were set for bed height, fluid velocity, and fluid viscosity. The power of this approach is that it enables screening of resins for potential pressure-flow issues at scale before beginning process development and can be used to set flow rate ranges as solution viscosity changes throughout a chromatographic sequence.