Using machine learning to identify aggregation resistant biopharmaceuticals

The UK is a major stakeholder in biopharmaceutical development and production, a sector that had sales of $228 billion in 2016. Aggregation is a major hurdle to their manufacture resulting in the failure of promising candidate biologics even at very late stages in the development pipeline. The ability to identify sequences likely to aggregate during production, transport or storage is of crucial importance to the biologics industry. This is currently beyond our capability both for mAbs and for the arsenal of advanced therapies (antibody-drug conjugates etc) that have the potential of revolutionising medicine in the future.

Together with Astra Zeneca, we have developed an in vivo selection method in E.coli able to quantify the aggregation propensity of bio-therapeutics that include mAbs by linking aggregation to antibiotic resistance. We have shown the assay can be used to screen for aggregation-resistant proteins of therapeutic importance with different protein scaffolds (a previous BBSRC CASE student with Avacta/AZ)) and, most recently, have used it combined with directed evolution to generate new proteins with enhanced bioprocessing capability.

Excitingly, in addition to isolating inherently developable therapeutics, this combined approach allows isolation of thousands protein sequences with known aggregation properties, opening the door to using machine learning (ML) to identify the key drivers of aggregation from such highly complex datasets.