(47c) Computationally Driven Therapeutic Protein Deimmunization

The unparalleled specificity and activity of therapeutic proteins has reshaped many aspects of modern clinical practice, and aggressive development of new protein drugs promises a continued revolution in disease therapy. As a result of their biological origins, therapeutic proteins present unique design challenges for the biomolecular engineer. One distinguishing risk factor is the prospect of eliciting an immune response upon administration to human patients. An anti-drug immune response can compromise therapeutic efficacy and even threaten patient safety. In the case of therapeutic antibodies, codified humanization technologies can mitigate much of the risk associated with immune reactions. These grafting-based deimmunization strategies are not, however, broadly applicable to other protein classes. In particular, they require a detailed understanding of structure-function relationships and the availability of homologous human protein scaffolds. Looking to the future, our capacity to effectively tap the full diversity of non-immunoglobulin therapeutic candidates will require more broadly applicable protein deimmunization technologies. To help meet this challenge, we have recently developed optimization algorithms that seamlessly integrate computational prediction of T cell epitopes and bioinformatics-based assessment of the structural and functional consequences of epitope-deleting mutations. The algorithms have been extensively tested and refined using retrospective studies of therapeutic candidates from the literature. More recently, we have experimentally validated the algorithms with the Enterobacter cloacae P99 β-lactamase enzyme, a component of Antibody Directed Enzyme Prodrug Therapies for cancer. This talk will highlight recent results in which we have explored the dual-objective protein design space and mapped the Pareto optimal frontier, i.e. testing undominated enzyme designs whose stability and immunogenicity scores are not simultaneously bested by any other protein.  We conclude that our deimmunization algorithms, which can be applied to virtually any protein target, guide the protein engineer towards promising immunoevasive therapeutic candidates.