In the first experimental use of algorithms that employ structure-based molecular modeling to optimize de-immunized drug candidates, Karl Griswold, Ph.D., and co-investigator Christopher Bailey-Kellogg, Ph.D., of Dartmouth College complement their prior sequence-based de-immunizing algorithms and expand the tool kit of protein engineering technologies to use in next-generation drug development. Their paper, "Protein Deimmunization via Structure-Based Design Enables Efficient Epitope Deletion at High Mutational Loads," was published online on February 5, 2015 in Biotechnology and Bioengineering. "This work is part of our larger collaborative initiative to develop performance-enhanced protein drugs that are invisible to the human immune system," explained Dr. Griswold. "Biotherapeutics offer potent treatment options for a wide range of diseases but, due to their biological origins, these powerful therapies can elicit detrimental immune responses in humans." Development of biotherapeutic agents is a time-consuming and costly endeavor, and there exists a substantial risk that deleterious immunogenicity issues will undermine otherwise promising drug candidates late in the development process. While methods for identifying immunogenic hotspots, or epitopes, are evolving rapidly, technologies to redesign the hotspots, while maintaining biotherapeutic activity and stability are far less developed. The current study employed P99 betalactamase, a component of Antibody-Directed Enzyme Prodrug Therapy, to show that structure-based de-immunization resulted in highly-active and stable biotherapeutic designs that were different from those generated with earlier sequence-based algorithms. In particular, the structure-based designs remodeled a putative immunogenic hotspot that was not readily addressed with other methods.
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