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Antibodies with lambda light chains (lambda-antibodies) are generally considered to be less developable than those with kappa light chains (kappa-antibodies). Though this hypothesis has not been formally established, it has led to substantial systematic biases in drug discovery pipelines and thus 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 to consider them as potential lead candidates. Here, we update our Therapeutic Antibody Profiler (TAP) tool to use the latest data and machine learning-based structure prediction, and apply it to evaluate developability risk profiles for kappa-antibodies and lambda-antibodies based on their surface physicochemical properties. We find that while human lambda-antibodies on average have a higher risk of developability issues than kappa-antibodies, a sizeable proportion are assigned lower-risk profiles by TAP and should represent more tractable candidates for therapeutic development. Through a comparative analysis of the low- and high-risk populations, we highlight opportunities for strategic design that TAP suggests would enrich for more developable lambda-antibodies. 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.


Journal article


Communications Biology


Nature Research (part of Springer Nature)

Publication Date