Prof Karl Griswold

By establishing protein design algorithms that simultaneously optimize drug candidates for both decreased immunogenic epitope content and high level stability and activity, Dartmouth's Norris Cotton Cancer Center investigator [and Dartmouth engineering professor] Karl Griswold, PhD, and his collaborator Chris Bailey-Kellogg, PhD, have established a novel testing platform. Published in PLOS Computational Biology, the paper, titled, "Mapping the Pareto Optimal Design Space for a Functionally Deimmunized Biotherapeutic Candidate," guides biotechnologists toward protein designs that function appropriately using sophisticated design algorithms.

"Protein deimmunization has proved important for a number of promising drug candidates, including potent anti-cancer immunotoxins that are now progressing through clinical trials," said Griswold. "Intuition suggests, and our design algorithms predict, that development of increasingly deimmunized biotherapeutics comes at the cost of progressive loss of molecular function."

To explore these putative tradeoffs, the Dartmouth team deimmunized the catalytic component of Antibody Directed Enzyme Prodrug Therapy (ADEPT), which is an experimental anti-cancer treatment. A systematic analysis of 18 deimmunized drug candidates revealed that experimentally measured molecular fitness mapped surprisingly closely onto the computational design space. This showed the potential to predict and design tradeoffs between a protein's immunogenic potential and molecular function.

"Biotherapeutics are revolutionizing modern medicine, but the fact that protein drugs are susceptible to immune surveillance in the human body represents a barrier to development and large scale deployment of even more of these powerful medicines," explained Griswold. "Using innovative dual objective algorithms, we can guide biotechnologists toward protein designs that benefit from a reduced risk of undesirable immunogenicity while maintaining high levels of inherent activity."