title: Optimization-based framework for resin selection strategies in biopharmaceutical purification process development.
abstract: This work addresses rapid resin selection for integrated chromatographic separations when conducted as part of a high-throughput screening exercise during the early stages of purification process development. An optimization-based decision support framework is proposed to process the data generated from microscale experiments to identify the best resins to maximize key performance metrics for a biopharmaceutical manufacturing process, such as yield and purity. A multiobjective mixed integer nonlinear programming model is developed and solved using the ε-constraint method. Dinkelbach's algorithm is used to solve the resulting mixed integer linear fractional programming model. The proposed framework is successfully applied to an industrial case study of a process to purify recombinant Fc Fusion protein from low molecular weight and high molecular weight product related impurities, involving two chromatographic steps with eight and three candidate resins for each step, respectively. The computational results show the advantage of the proposed framework in terms of computational efficiency and flexibility. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1116-1126, 2017.
authors.affiliation: Centre for Process Systems Engineering, Dept. of Chemical Engineering, University College London, Torrington Place, London, WC1E 7JE, UK.|EPSRC Centre for Innovative Manufacturing in Emergent Macromolecular Therapies, University College London, Gordon Street, London, WC1H 0AH, UK.|School of Management, Swansea University, Bay Campus, Fabian Way, Swansea, SA1 8EN, UK.
authors.forename: Songsong
authors.initials: S
authors.lastname: Liu
authors.affiliation: Centre for Process Systems Engineering, Dept. of Chemical Engineering, University College London, Torrington Place, London, WC1E 7JE, UK.
authors.forename: Spyridon
authors.initials: S
authors.lastname: Gerontas
authors.affiliation: MedImmune Limited, Milstein Building, Granta Park, Cambridge, CB1 6GH, UK.
authors.forename: David
authors.initials: D
authors.lastname: Gruber
authors.affiliation: MedImmune Limited, Milstein Building, Granta Park, Cambridge, CB1 6GH, UK.
authors.forename: Richard
authors.initials: R
authors.lastname: Turner
authors.affiliation: Centre for Process Systems Engineering, Dept. of Chemical Engineering, University College London, Torrington Place, London, WC1E 7JE, UK.
authors.forename: Nigel J
authors.initials: NJ
authors.lastname: Titchener-Hooker
authors.affiliation: Centre for Process Systems Engineering, Dept. of Chemical Engineering, University College London, Torrington Place, London, WC1E 7JE, UK.
authors.forename: Lazaros G
authors.identifier: http://orcid.org/0000-0003-4652-6086
authors.initials: LG
authors.lastname: Papageorgiou
mesh_terms: D002845:Chromatography / Q000379:methods*; D057166:High-Throughput Screening Assays; D006801:Humans; D007141:Immunoglobulin Fc Fragments / Q000737:chemistry / Q000302:isolation & purification*; D011993:Recombinant Fusion Proteins / Q000737:chemistry / Q000302:isolation & purification*; D012117:Resins, Synthetic / Q000737:chemistry*
chemical_list: D007141:Immunoglobulin Fc Fragments; D011993:Recombinant Fusion Proteins; D012117:Resins, Synthetic
keywords: Dinkelbach's algorithm; multiobjective optimization; purification process development; resin selection; ε-constraint method