When engineering antibodies into effective biotherapeutics, ideally, factors such as affinity, specificity, chemical stability and solubility should all be optimised. In practice, we know that it’s often not feasible to co-optimise all of these, and so compromises are made, but identifying these developability issues early on in the antibody drug discovery process could save costs and reduce attrition rates. For example, we could avoid choosing a candidate that expresses poorly, which would make it expensive to manufacture as a drug, or one with a high risk of aggregation that would drive unwanted immunogenicity.

On this theme, I was interested to read recently a paper by the Computational Chemistry & Biologics group at Merck (Evers et al., 2022 https://www.biorxiv.org/content/10.1101/2022.11.19.517175v1). They have developed a pipeline called SUMO (In Silico Sequence Assessment Using Multiple Optimization Parameters), that brings together publicly-available software for in silico developability assessment and creates an overall developability profile as a starting point for antibody or VHH optimisation.

For each sequence assessed, they report factors such as sequence liabilities (residues liable to chemical modifications that can alter properties such as binding affinity or aggregation propensity), surface hydrophobicity, sequence identity compared to most similar human germline and predicted immunogenicity (based on MHC-II binding). Also provided are an annotated sequence viewer and 3D visualisation of calculated properties. Profiles are annotated with a red-yellow-green colour-coding system to indicate which sequences have favourable properties.

Overall, this approach is a useful way to discriminate between candidates and steer away from those with major developability issues prior to the optimisation stage. Given that the thresholds for their colour-coding system are based on data from marketed therapeutic antibodies, and that the software used has primarily been designed for use on antibody datasets, I would be interested to see whether the particular descriptors chosen for SUMO translate well to VHHs, or whether there are other properties that are stronger indicators of nanobody developability.