Structure-based engineering of pH-dependent antibody binding for selective targeting of solid-tumor microenvironment

Area of Science:

• Biotechnology
• Immunology
• Computational Biology

Background:

• Monoclonal antibodies are key cancer treatments but can affect healthy tissues.
• Solid tumors often have an acidic microenvironment distinct from normal tissues.
• Targeting tumor acidity could improve antibody safety and efficacy.

Purpose of the Study:

• To computationally engineer anti-Her2 antibodies for selective binding in acidic tumor microenvironments.
• To optimize antibody pH selectivity using a structure-based approach and affinity maturation.
• To develop safer and more effective antibody-based cancer therapies.

Main Methods:

• Structure-based computational design of anti-Her2 antibodies.
• Affinity maturation using dual-pH histidine-scanning mutagenesis for pH selectivity.
• In vitro testing of antibody variants for Her2 binding and tumor spheroid growth inhibition.

Main Results:

• Identified antibody fragments (Fabs) with desired pH-dependent binding behavior.
• Achieved up to 25-fold improvement in binding selectivity toward acidic pH.
• Designed full-size antibodies inhibited tumor spheroid growth at acidic pH comparable to trastuzumab, with reduced effects at physiological pH.

Conclusions:

• Demonstrated feasibility of computational antibody optimization for targeting acidic tumor environments.
• Developed Her2 antibodies with enhanced selectivity for acidic tumor conditions.
• This strategy offers a pathway to improve the safety and specificity of antibody cancer therapies.