Mapping the Pareto optimal design space for a functionally deimmunized biotherapeutic candidate

Area of Science:

• Biochemistry
• Immunology
• Protein Engineering

Background:

• Biotherapeutic immunogenicity hinders clinical application and development.
• Improved deimmunization technologies are crucial for advancing biotherapeutics.
• Existing methods often involve tradeoffs between reduced immunogenicity and protein function.

Purpose of the Study:

• To design, construct, and experimentally evaluate the Pareto frontier of a therapeutic enzyme for deimmunization.
• To assess the functional penalty associated with progressively deimmunized biotherapeutic candidates.
• To validate computational predictions of protein function and immunogenicity.

Main Methods:

• Utilized algorithms for simultaneous optimization of reduced T cell epitope content and protein function.
• Performed in silico analysis to explore the dual-objective design space.
• Designed, constructed, and experimentally evaluated variants on the Pareto frontier.
• Measured protein performance and correlated it with computational predictions.

Main Results:

• In silico analysis revealed a spectrum of deimmunized designs with inherent tradeoffs between immunogenicity and function.
• Experimental evaluation confirmed that protein performance mapped a functional sequence space aligning with computational predictions.
• The study systematically assessed the functional consequences of pursuing enhanced deimmunization.

Conclusions:

• The Pareto frontier represents optimal designs balancing immunogenicity and function.
• Computational algorithms can effectively guide the design of deimmunized biotherapeutics.
• These algorithms offer a powerful tool to accelerate and de-risk biotherapeutic development by managing immunogenicity-functionality tradeoffs.