MACHINE LEARNING TO ENGINEER ANTIBODY FRAMEWORKS FOR DEVELOPABILITY

摘要

Monoclonal antibodies (mAbs) have revolutionized medicine in the last 20 years and today represents ～$70B/yr in total pharmaceutical sales, most notably in the areas of oncology and autoimmune disorders. The function and developability of mAbs depend on the expression, folding and integrity of their structure. Protein pharmaceuticals must be tolerant to factors such as heat, interfacial stress, aggregation, pH and more in order to reach the market. We here apply systematic variance, inductive machine learning, synthetic genes and our high-throughput transient mammalian protein expression platform to engineer a humanized IgG1 scaffold for high developability independent of the hypervariable region present in the mAb.