After the success of monoclonal antibodies, the product pipelines of the biopharmaceutical industry are steadily increasing in diversity. Without established platform-processes to work from, the ability to successfully manufacture a drug candidate at larger scales needs to be assessed at an early development stage, when only very limited sample quantities are available.<br/><br/>The industry has adopted a variety of high-throughput techniques to cope with the large parameter space that needs to be explored to enable the development of an efficient process, but most are very inefficient in terms of sample utilization. Model-based optimization of the chromatography steps has been proposed as a tool to reduce the number of material consuming experiments. The characterization of process related impurities, such as host cell proteins, towards their thermodynamic properties is a necessary step to allow the application of such mechanistic models to downstream process optimization. Current approaches based on parameter regression from complex chromatograms require too much manual input to be compatible with a high-throughput workflow.<br/><br/>We present a platform approach where high-throughput experiments and analytics are combined with automated fast and sensitive data-analysis algorithms to allow a statistically robust determination of single component properties directly from crude samples without relying on error-prone operator descisions.

Robust characterization of the chromatographic behaviour of complex biological feedstocks

249th National Meeting (2015)

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