Improvement of an overloaded, multi-component, solvent gradient bioseparation through multiobjective optimization

Solvent gradient chromatography is quite often used in analytical studies for decreasing the analysis time of samples having components with widely different retention behaviour. Several studies, both theoretical and experimental, have been reported on the optimization of gradient profiles in improving analytical separation performance, suggesting various linear and non-linear gradients. In preparative chromatography, on the other hand, though solvent gradient is being increasingly used (especially in bioseparation) to improve the product yield and productivity, there is a dearth of literature and clearer understanding of the effect(s) of modifier gradients on the separation performance. For this, the gradients used in applications are of relatively simple profiles like step or linear gradients, obtained through hand optimization based on experience and intuition. Significant improvements, however, can be expected using the state-of-the art modelling of chromatographic processes and optimization routines running on widely available hi-speed desktop computers. In this work we are reporting such an optimization procedure to improve the purification of an industrial multi-component mixture, containing 65.8% of Calcitonin as the main product, in an overloaded reversed-phase column. The work comprises both theoretical simulations and their experimental validation using multilinear gradients as optimization variable. The study produced interesting insights for modifier gradient design, like using peak deformation of the target peptide to increase yield and productivity, and improved our understanding of the effect of modifier gradients in non-linear separations.