Contextualising the developability risk of antibodies with lambda light chains using enhanced therapeutic antibody...

Area of Science:

• Biologics
• Immunotherapeutics
• Antibody Engineering

Background:

• Antibodies with lambda light chains (λ-antibodies) are often overlooked in drug discovery compared to kappa light chain antibodies (κ-antibodies).
• This bias stems from a perceived, but not formally established, notion of lower developability for λ-antibodies.
• Neglecting λ-antibodies may lead to missing therapeutic opportunities, as they engage unique epitopes.

Purpose of the Study:

• To re-evaluate the developability of λ-antibodies versus κ-antibodies using updated data and machine learning.
• To identify λ-antibody candidates with favorable developability profiles for therapeutic development.
• To provide insights for strategic antibody design to improve λ-antibody developability.

Main Methods:

• Updated the Therapeutic Antibody Profiler (TAP) tool with recent data and machine learning-based structure prediction.
• Assessed developability risk profiles of κ- and λ-antibodies based on surface physicochemical properties.
• Conducted comparative analyses of low- and high-risk antibody populations.

Main Results:

• On average, human λ-antibodies present a higher developability risk than κ-antibodies.
• A substantial subset of λ-antibodies demonstrated low-risk profiles according to TAP.
• Identified specific design strategies that TAP suggests could enhance λ-antibody developability.

Conclusions:

• The developability of λ- and κ-antibodies differs, but not universally, with many λ-antibodies being tractable therapeutic candidates.
• TAP provides a data-driven approach to identify and design developable λ-antibodies.
• Encouraging the inclusion of λ-antibodies can diversify the pool of immunotherapeutic candidates.