Selectivity Approaches in Therapeutic Antibody Design

Area of Science:

• Biotechnology
• Immunology
• Drug Discovery

Background:

• Antibody-based therapies are crucial for treating diseases due to their specificity and longevity.
• Artificial intelligence and generative modeling are advancing the design of novel peptide binders.
• Current approaches focus on enhancing antibody selectivity and engineering conditional activity.

Purpose of the Study:

• To highlight strategies for improving antibody selectivity during design and preclinical stages.
• To emphasize the engineering of conditionally active antibodies and probodies for improved safety and efficacy.
• To explore the diversity of clinically relevant antibodies and antibody-drug conjugates.

Main Methods:

• Review of structural modifications linked to functional signaling and therapeutic response.
• Analysis of conditionally active antibodies and probodies exploiting tumor microenvironment features.
• Examination of structural and mechanistic diversity of antibodies (e.g., anti-CD20, anti-PD1) and antibody-drug conjugates.

Main Results:

• Structural innovations enhance tumor selectivity and therapeutic precision.
• Engineering conditionally active antibodies improves safety and efficacy by targeting the tumor microenvironment.
• Diverse antibody formats, including antibody-drug conjugates with various payloads and linkers, offer advanced therapeutic options.

Conclusions:

• Structural innovation is key to advancing next-generation immunotherapeutics.
• Enhanced antibody selectivity and conditional activation improve treatment outcomes.
• The field is progressing towards more precise and effective protein-based therapies.