Development of a structure-based computational simulation to optimize the blocking efficacy of pro-antibodies

Area of Science:

• Biotechnology
• Immunology
• Computational Biology

Background:

• Monoclonal antibodies (Abs) exhibit on-target toxicity due to indiscriminate binding to antigens (Ags) in both diseased and normal tissues.
• Pro-Abs, engineered with an 'Ab lock' mechanism, aim to improve Ab selectivity by requiring activation in disease-specific environments.
• Previous pro-Ab designs showed variable blocking efficiency, necessitating optimization for diverse antibody structures.

Purpose of the Study:

• To develop and validate a structure-based computational simulation (MSCS) method for optimizing the 'Ab lock' linker in pro-Abs.
• To enhance the blocking ability and reduce side effects of antibody-based therapies.
• To ensure efficient pro-Ab formation across different antibody drugs.

Main Methods:

• Utilized structure-based computational simulation (MSCS) to design and optimize protease-cleavable linkers connecting an 'Ab lock' to antibody drugs.
• Applied MSCS to model antibodies targeting PD-1, IL-1β, CTLA-4, and TNFα, generating various linker variants (L1-L7).
• Validated MSCS predictions by generating recombinant pro-Abs and assessing their binding kinetics and blocking efficacy.

Main Results:

• MSCS predicted linker compositions that achieved Ab lock cover rates ranging from 28.33% to 42.33%.
• Pro-Abs demonstrated significantly enhanced blocking abilities: αPD-1 (200-250-fold), αIL-1β (152-186-fold), αCTLA-4 (68-150-fold), and αTNFα (20-123-fold).
• A positive correlation was observed between Ab lock cover rate and pro-Ab blocking ability, confirming MSCS efficacy.

Conclusions:

• MSCS effectively predicts optimal linkers for pro-Ab development, improving Ab selectivity and reducing off-target toxicity.
• This computational approach facilitates the creation of next-generation pro-Antibody drugs.
• Optimized pro-Abs hold significant potential to improve antibody-based therapies and patient quality of life.