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Computational design and prediction tools can help de-risk development

Identifying a disease specific target for an antibody, and then producing a monoclonal that can be shown to have an effect in the laboratory is an exciting phase of biologic discovery. However, if such discovery is to make it to clinical benefit there are many phases of testing and trials to pass through as well as the manufacturing challenges of scaling up the production of the biologic to amounts useful to commercialisation.

Some of the most important characteristics of a biologic candidate are it’s potential to cause an immune response in the patient (it’s immunogenicity) and the way it behaves in large scale culture and purification and formulation (it’s developability).

Much of this can be considered in silico, particularly if you take a computational approach to designing the humanised form of a biologic candidate. Applied Exomics has expertise in protein modelling, antibody structure and design - and has developed the free BiologicAE platform that can be used to characterise your candidates. (Try it by clicking here - or the logo below)

Evaluating developability

Evaluating (MHC II) immunogenicity