QSAR models for prediction of chromatographic behavior of homologous Fab variants.

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.

Defining the property space for chromatographic ligands from a homologous series of mixed-mode ligands.

A homologous ligand library based on the commercially-available Nuvia cPrime ligand was generated to systematically explore various features of a multimodal cation-exchange ligand and to identify structural variants that had significantly altered chromatographic selectivity. Substitution of the polar amide bond with more hydrophobic chemistries was found to enhance retention while remaining hydrophobically-selective for aromatic residues. In contrast, increasing the solvent exposure of the aromatic ring was observed to strengthen the ligand affinity for both types of hydrophobic residues. An optimal linker length between the charged and hydrophobic moieties was also observed to enhance retention, balancing the steric accessibility of the hydrophobic moiety with its ability to interact independently of the charged group. The weak pKa of the carboxylate charge group was found to have a notable impact on protein retention on Nuvia cPrime at lower pH, increasing hydrophobic interactions with the protein. Substituting the charged group with a sulfonic acid allowed this strong MM ligand to retain its electrostatic-dominant character in this lower pH range. pH gradient experiments were also carried out to further elucidate this pH dependent behavior. A single QSAR model was generated using this accumulated experimental data to predict protein retention across a range of multimodal and ion exchange systems. This model could correctly predict the retention of proteins on resins that were not included in the original model and could prove quite powerful as an in silico approach toward designing more effective and differentiated multimodal ligands.