Model-based analysis and quantitative prediction of membrane chromatography: Extreme scale-up from 0.08 ml to 1200 ml

• Published in: Journal of Chromatography A Vol. 1332; pp. 8 - 13
• Main Authors: GHOSH, Pranay, VAHEDIPOUR, Kaveh, LEUTHOLD, Martin, VON LIERES, Eric
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier 07.03.2014
• Subjects: Binding; Biological and medical sciences; Calibration; Chromatography; Chromatography - instrumentation; Chromatography - methods; Computational fluid dynamics; Decoupling; Dynamical systems; General pharmacology; Hydrodynamics; Mathematical models; Medical sciences; Membranes; Membranes, Artificial; Models, Chemical; Pharmaceutical technology. Pharmaceutical industry; Pharmacology. Drug treatments; Drug; Membrane chromatography; Purification; Computational fluid dynamics; Preparative chromatography; Scale effect; Prediction; Computational fluid dynamics (CFD); Theoretical study; Biopharmaceuticals; Modeling; Scale-up; Extrapolation; Membrane chromatography (MC); Pharmaceutical industry; Quantitative analysis; Breakthrough curve
• ISSN: 0021-9673; 1873-3778
• DOI: 10.1016/j.chroma.2014.01.047

A model-based approach is presented for quantitatively decoupling the impacts of non-ideal flow and non-ideal binding in membrane chromatography (MC) capsules at different scales. The internal geometry of Sartobind capsules with 0.08 ml and 1200 ml membrane volume is reconstructed from MRI measurements and manufacturer data. Based on this information, computational fluid dynamics (CFD) simulations are used for computing internal flow patterns of both capsules. Measured breakthrough curves (BTC) under non-binding conditions are used for calibrating PFR and CSTR models of the holdup volumes in the Äkta systems. A suitable binding model is determined and the binding parameters are estimated from binding BTC data of the 0.08 ml capsule. Due to the decoupling of non-idealities, the binding parameters can be directly transferred between the CFD models of both capsules. This advantage is used for quantitatively predicting BTC data of the 1200 ml capsule under binding conditions. The model-based prediction excellently matches with independently measured BTC data, facilitating an extreme scale-up factor of 15,000. The presented approach has previously been shown to be universally applicable to capsules from other vendors with different flow configurations and membrane types.