Biotherapeutics, including antibodies, enzymes, signaling peptides, and toxins, are revolutionizing disease therapy, but these next-generation drugs carry an inherent risk of eliciting detrimental immune responses. Stealth’s proprietary technology seamlessly integrates advanced computational models, protein design algorithms, protein engineering methodologies, and immunoassays to develop optimized therapeutic candidates that are less immunogenic but functionally equivalent to, or better than, nature’s own biomolecules.
Stealth Biologics® employs a cutting-edge protein design platform that leverages advanced computational methods to develop stable, active, and immunotolerant therapeutic candidates. The primary driver of a typical anti-biotherapeutic immune response is molecular recognition of a protein’s constituent T cell epitopes. We render a therapeutic protein more "stealthy" by mutating it so as to eliminate such immunogenic peptides, while ensuring that the therapeutic function is not compromised.
Three key aspects, elaborated below, distinguish the Stealth approach. We simultaneously optimize immunogenicity and function. We assess impacts of combinations of mutations on a protein as a whole. We identify globally optimal and near-optimal designs. Our platform thus enables us to engineer portfolios of highly active biotherapeutic candidates with minimized effector T cell recognition profiles.
In order to assess the effects of possible mutations on function and immunogenicity, we leverage computational models that integrate evolutionary information and/or structure-based energy evaluation with sequence-based T cell epitope prediction. We design either a set of functionally deimmunized variants, ranging over aggressiveness and mutational loads, or a deimmunized library, enriched in a diverse set of functionally deimmunized clones. Our novel combinatorial optimization algorithms generate designs that make the best trade offs between the assessments of immunogenicity and function. In a closed-loop approach, experimental results from initial assays are fed back into the design process in order to improve and focus further refinements.