Lonza New Platform lowers Risks of Antibody Aggregation

Lonza launches a Server-based platform designed to help improve the quality and safety of therapeutic antibodies.

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Lonza recently announced the introduction of the Sentinel Apart platform, a server-based antibody aggregation prediction and re-engineering tool. By using this high-throughput screening platform, customers can assess multiple candidates, which aids lead ranking and selection at the late discovery stage.

Therapeutic antibody aggregation issues can result in a higher risk of immunogenicity and delayed timelines, which can increase development costs and attrition rates. In addition, there is an increasing expectation from regulators for levels of aggregation to be identified, reported and addressed as part of the development process.

Calculation Tool for More Safety

Lonza developed the Sentinel Apart platform based on their extensive knowledge and expertise in antibody production and protein engineering. The tool was built and validated on experimental aggregation data for more than 500 antibodies. Using the primary sequence as input, it provides qualitative prediction (high or low) of aggregation risk for antibodies.

The protein re-engineering functionality of the platform identifies residues that contribute to aggregation and provides a guided approach for suitable amino acid substitutions.

The tool calculates the change in aggregation risk for either one or two position substitutions, taking into consideration potential impact on post-translational modifications in the re-engineered antibody sequence. This guided approach reduces the need for extensive bioinformatics or protein modeling experience.

Karen Fallen, Vice President, Business Unit Head, Clinical Development and Licensing for Lonza, claimed stated that the new platform supported the company’s customers’ existing discovery and development activities by addressing a common issue found with therapeutic antibodies; with Lonza’s easy-to-use, high-throughput tool helping customers address the challenges in early development by assessing, designing and optimizing therapeutic antibodies for clinical success.

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