Design of a universal strategy to generate proteaseactivatable nanobodies targeting tumor cells


García Sagarra, Maria


Glioblastoma multiforme is the most aggressive and deadly brain cancer. Despite many efforts, no effective treatment is available. Therapies targeted with antibodies binding receptors on tumor cells offer new opportunities to improve cancer treatment. However, most receptors overexpressed in tumor cells are also present in healthy tissues. Therefore, the selectivity of targeted therapies could be greatly enhanced if targeting molecules were only activated at the diseased site. To activate these molecules specific internal cues such as proteases overexpressed in the tumor must be exploited. Although several strategies to render antibodies activatable have been devised, none of them is applicable across several antibody formats and specificities.
In this study, we aim to design a new strategy to render antibodies activatable with proteases. The strategy we envision has a universal aspiration and could be applied to virtually any antibody format and specificity. In order to provide the first proof of concept, we have focused on single-domain antibodies (nanobodies) because of their increasing interest as therapeutics and their simple structure. The strategy here proposed is based on the extension of the N-terminus with a protease-cleavable linker and the conjugation of the new N-terminus to the opposite site of the antigen-binding region on the nanobody. In this way, the binding will be reversibly blocked by steric hindrance and the antibody will only be able to bind its target in the tumor microenvironment. To this end, here we have first developed a method to locate the best landing site to which the extended N-terminus will be conjugated in order to close the lock. After that, several masking linkers cleavable by tumor-specific proteases have been generated and modelled on the EGFR-specific 7D12 nanobody. Finally, the best candidates have been selected for expression and simulated via molecular dynamics to evaluate masking efficiency in silico.



Biarnés Fontal, Xevi
Oller-Salvia, Benjamí 


IQS SE - Undergraduate Program in Biotechnology