title: QSAR models for prediction of chromatographic behavior of homologous Fab variants.
abstract: While quantitative structure activity relationship (QSAR) models have been employed successfully for the prediction of small model protein chromatographic behavior, there have been few reports to date on the use of this methodology for larger, more complex proteins. Recently our group generated focused libraries of antibody Fab fragment variants with different combinations of surface hydrophobicities and electrostatic potentials, and demonstrated that the unique selectivities of multimodal resins can be exploited to separate these Fab variants. In this work, results from linear salt gradient experiments with these Fabs were employed to develop QSAR models for six chromatographic systems, including multimodal (Capto MMC, Nuvia cPrime, and two novel ligand prototypes), hydrophobic interaction chromatography (HIC; Capto Phenyl), and cation exchange (CEX; CM Sepharose FF) resins. The models utilized newly developed "local descriptors" to quantify changes around point mutations in the Fab libraries as well as novel cluster descriptors recently introduced by our group. Subsequent rounds of feature selection and linearized machine learning algorithms were used to generate robust, well-validated models with high training set correlations (R2> 0.70) that were well suited for predicting elution salt concentrations in the various systems. The developed models then were used to predict the retention of a deamidated Fab and isotype variants, with varying success. The results represent the first successful utilization of QSAR for the prediction of chromatographic behavior of complex proteins such as Fab fragments in multimodal chromatographic systems. The framework presented here can be employed to facilitate process development for the purification of biological products from product-related impurities by in silico screening of resin alternatives. Biotechnol. Bioeng. 2017;114: 1231-1240. © 2016 Wiley Periodicals, Inc.
authors.affiliation: Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Center for Biotechnology and Interdisciplinary Studies, 110 8th Street, Troy, New York 12180.
authors.forename: Julie R
authors.initials: JR
authors.lastname: Robinson
authors.affiliation: Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Center for Biotechnology and Interdisciplinary Studies, 110 8th Street, Troy, New York 12180.|Downstream Technology, Novo Nordisk A/S, DK-2760 Maaloev, Denmark.
authors.forename: Hanne S
authors.initials: HS
authors.lastname: Karkov
authors.affiliation: Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Center for Biotechnology and Interdisciplinary Studies, 110 8th Street, Troy, New York 12180.
authors.forename: James A
authors.initials: JA
authors.lastname: Woo
authors.affiliation: Expression Technologies, Novo Nordisk A/S, DK-2760 Maaloev, Denmark.
authors.forename: Berit O
authors.initials: BO
authors.lastname: Krogh
authors.affiliation: Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Center for Biotechnology and Interdisciplinary Studies, 110 8th Street, Troy, New York 12180.
authors.forename: Steven M
authors.initials: SM
authors.lastname: Cramer
mesh_terms: D000911:Antibodies, Monoclonal / Q000032:analysis* / Q000737:chemistry*; D003198:Computer Simulation; D007140:Immunoglobulin Fab Fragments / Q000032:analysis* / Q000737:chemistry*; D008956:Models, Chemical; D021281:Quantitative Structure-Activity Relationship; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D000911:Antibodies, Monoclonal; D007140:Immunoglobulin Fab Fragments
keywords: Fab variants; QSAR; chromatography; predictive model; protein surface properties