Protein adsorption on ion exchange resins and monoclonal antibody charge variant modulation

A novel multicomponent adsorption equilibrium model for proteins on ion-exchange resins is developed on a statistical thermodynamic basis including surface coverage effects and protein-resin and protein-protein interactions. The resulting model exhibits a general competitive Langmuirian behavior and was applied to the study and optimization of the separation of monoclonal antibody charge variants on two strong cation exchangers. The model accounts explicitly for the effect of both pH and salt concentration, and its parameters can be determined in diluted conditions, that is, through physically sound assumptions, all model parameters can be obtained using solely experiments in diluted conditions, and be used to make predictions in overloaded conditions.The parameterization of the model and optimization of the separation is based on a two-step approach. First, gradient experiments in diluted conditions are undertaken in order to determine the model parameters. Based on these experiments and on information about the proteins of interest and the stationary phase used, all the model parameters can be estimated. Second, using the parameterized model, an initial Pareto optimization is undertaken where overloaded operating conditions are investigated. Experiments from this Pareto set are then used to refine the estimation of the model parameters. A second Pareto optimization can then be undertaken, this time with the refined parameters. This can be repeated until a satisfactory set of model parameters is found.This iterative approach is shown to be extremely efficient and to provide large amounts of knowledge based on only a few experiments. It is shown that due to the strong physical foundation of the model and the very low number of adjustable parameters, the number of iterations is expected to be at most two or three. Furthermore, the model based tool is improved as more experimental knowledge is provided, allowing for better estimations of the chromatographic processes considered at each iteration. This makes it a very suitable tool for the design and the development of preparative and industrial purification processes, including the determination of both the optimal operating conditions, as well as the allowable process operating space.