Scientists from the University of Ghent say they have developed new techniques for assessing antibody stability during drug development to select formulations that minimize degradation during long-term storage. The methods aim to quickly predict which antibody formulations are likely to degrade over time due to aggregation.

“During formulation development, it’s helpful to predict which formulations are stable, without having to wait one or two years for real-time stability studies to find out,” explains Hristo Svilenov, PhD, an associate professor from the University of Ghent. “Our approach evaluates how the formulation conditions affect the aggregation of partially unfolded antibodies at temperatures relevant for long-term storage.”

The colloidal stability and aggregation tendency of antibody drug candidates can be estimated from their refolding efficiency using chemical denaturants such as urea. [Hristo Svilenov, PhD]

When native antibodies unfold, the aggregation-prone parts of the protein are exposed and can form aggregates. These aggregates can increase the risk of immune reactions in patients or, if they become large enough to form visible particles, they can lead to the drug batch being discarded, Svilenov explains, adding that the team has developed new methods for predicting the aggregation of antibodies during storage in a faster manner.

These methods can be used during drug development to find, for example, the optimum formulations for storage, including the optimal buffer or other excipients. According to Svilenov, the techniques use common chemical denaturants, such as urea and guanidinium salts. These can be added to the antibodies to induce the formation of partially unfolded proteins and accelerate the aggregation to allow comparisons to be made on short timescales.

“We aim to develop predictive techniques that are simple so you don’t need special equipment—you can perform them with devices in every lab and our goal is that they can be used by anyone,” he says.

The techniques can also be used during biomanufacturing to compare production batches and ensure that the drug product has the same degradation profile. Svilenov says the team is now following up on this research by developing quantitative prediction tools to rank formulations and drug candidates by aggregation risk. They also aim to automate these methods and apply them to other biotherapeutics beyond antibodies.

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