Using Antibody Sequence-Based Prediction for Biopharmaceutical Research


A recent study highlights the use of predictive tools to purify antibodies.

In a recent study published in the Journal of Chromatography A, researchers are harnessing the power of structure-based prediction to advance the purification of antibodies in the biopharmaceutical industry. This approach promises more efficient and cost-effective processes while bolstering our understanding of the complex protein-ligand interactions that drive antibody purification (1).

Multimodal chromatography has rapidly gained recognition for its potential to selectively capture and separate target molecules, making it a crucial step in antibody purification. However, optimizing chromatography parameters has long been a challenge due to the intricate nature of protein-ligand interactions. To address this challenge, scientists emphasize the need for predictive tools that can aid in the development and optimization of multimodal chromatography processes.

This newly introduced methodology focuses on predicting the elution behavior of antibodies in multimodal chromatography based on their amino acid sequences. The researchers conducted a comprehensive analysis, encompassing 64 full-length antibodies, spanning across various formats including IgG1, IgG4, and IgG-like multispecific structures. These antibodies were subjected to elution using linear pH gradients ranging from pH 9.0 to 4.0 on the anionic mixed-mode resin Capto adhere.

Multimodal chromatography is separation technique that combines two or more separation mechanisms within a single chromatographic column. By using multiple interaction modes, such as size exclusion, ion exchange, hydrophobic interaction, or affinity chromatography, it allows for enhanced selectivity and purification of complex mixtures, making it particularly valuable in the separation of biomolecules like proteins and peptides.

To construct their predictive model, the researchers built homology models and calculated a staggering 1312 antibody-specific physicochemical descriptors for each antibody molecule. Then they identified six key structural features governing multimodal antibody interactions. Notably, these features were closely correlated with elution behavior, with a strong emphasis on the antibody variable region.

The results of this study showcased the ability to predict pH gradient elution for a diverse range of antibodies and antibody formats, achieving a test set R² value of 0.898. This predictive model offers a significant advantage to the industry by guiding initial conditions for multimodal elution and minimizing the often time-consuming and resource-intensive trial and error approach during process optimization.

The predictive model also opens doors to the prospect of conducting in silico manufacturability assessments. It allows for the screening of target antibodies that adhere to standardized purification conditions, offering a new level of efficiency in the biopharmaceutical production pipeline.

This article was written with the help of artificial intelligence and has been edited to ensure accuracy and clarity. You can read more about our policy for using AI here.


Hess, R.; Faessler, J.; Yun, D.; Saleh, D.; Grosch, J.-H.; Schwab, T.; Hubbuch, J. Antibody Sequence-Based Prediction of Ph Gradient Elution in Multimodal Chromatography. Journal of Chromatography A 2023, 1711, 464437. DOI:10.1016/j.chroma.2023.464437.

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Toby Astill | Image Credit: © Thermo Fisher Scientific
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