Contextualising the developability risk of antibodies with lambda light chains using enhanced therapeutic antibody profiling
Antibodies with lambda light chains ({lambda}-antibodies) are generally considered to be less developable than those with kappa light chains ({kappa}-antibodies), leading to substantial systematic biases in drug discovery pipelines. This has contributed to kappa dominance amongst clinical-stage therapeutics. However, the identification of increasing numbers of epitopes preferentially engaged by{lambda} -antibodies shows there is a functional cost to neglecting them as potential lead candidates during discovery campaigns. Here, we update our Therapeutic Antibody Profiler (TAP) tool to use the latest data and machine learning-based structure prediction methods, and apply this new protocol to evaluate developability risk profiles for{kappa} -antibodies and{lambda} -antibodies based on their surface physicochemical properties. We find that{lambda} -antibodies are on average at a higher risk of poor developability -- as an indication, over 40% of single-cell sequenced human{lambda} -antibodies are flagged by TAP for risk-prone patches of surface hydrophobicity (PSH), compared to around 11% of human{kappa} -antibodies. Nonetheless, a substantial proportion of natural{lambda} -antibodies are assigned more moderate risk profiles by TAP and should therefore represent more tractable candidates for therapeutic development. We also analyse the populations of high and low risk antibodies, highlighting opportunities for strategic design that TAP suggests would enrich for more developable{lambda} -based candidates. Overall, we provide context to the differing developability of{kappa} - and{lambda} -antibodies, enabling a rational approach to incorporate more diversity into the initial pool of immunotherapeutic candidates.