Antibody sequence-based prediction of pH gradient elution in multimodal chromatography

Multimodal chromatography has emerged as a promising technique for antibody purification, owing to its ca-
pacity to selectively capture and separate target molecules. However, the optimization of chromatography pa-
rameters remains a challenge due to the intricate nature of protein-ligand interactions. To tackle this issue,
efficient predictive tools are essential for the development and optimization of multimodal chromatography
processes. In this study, we introduce a methodology that predicts the elution behavior of antibodies in multi-
modal chromatography based on their amino acid sequences. We analyzed a total of 64 full-length antibodies,
including IgG1, IgG4, and IgG-like multispecific formats, which were eluted using linear pH gradients from pH
9.0 to 4.0 on the anionic mixed-mode resin Capto adhere. Homology models were constructed, and 1312
antibody-specific physicochemical descriptors were calculated for each molecule. Our analysis identified six key
structural features of the multimodal antibody interaction, which were correlated with the elution behavior,
emphasizing the antibody variable region. The results show that our methodology can predict pH gradient
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elution for a diverse range of antibodies and antibody formats, with a test set R2 of 0.898. The developed model
can inform process development by predicting initial conditions for multimodal elution, thereby reducing trial
and error during process optimization. Furthermore, the model holds the potential to enable an in silico manu-
facturability assessment by screening target antibodies that adhere to standardized purification conditions. In
conclusion, this study highlights the feasibility of using structure-based prediction to enhance antibody purifi-
cation in the biopharmaceutical industry. This approach can lead to more efficient and cost-effective process
development while increasing process understanding.