With an FDA approval rate for new drugs hovering at around 40 molecular entities per year, including those that are improved versions of existing drugs and those that exist but are repurposed for different applications, the lack of breakthrough drugs is a major cause for concern. Despite the lowering attrition rates of biotherapeutics in the preclinical and clinical stages, only one in five drug candidates entering clinical trials make it through Phases I, II, and III to obtain regulatory approval. Exacerbating the issue is the increase in overall expenditure that goes into the research and development (R&D) pipeline for new therapeutics.
In the case of antibody-based biologics, ongoing advancements in knowledge and cutting-edge technologies are currently attempting to address these challenges. One approach is to streamline the early discovery and characterization stages to only select lead candidates with optimal properties for later-stage development and testing. This is critical to realizing the full potential of next generation recombinant antibody derivatives such as camelid-derived nanobodies – a small and very stable biologics family with excellent tissue penetration compared to full-size antibodies.
Here, we demonstrate the application of SPR (surface plasmon resonance) kinetic analysis and HDX-MS (hydrogen-deuterium exchange mass spectrometry) epitope mapping in the characterization of recombinant alpaca nanobodies developed using our REpAb® Antibody Discovery Platform. Our upstream antibody discovery and antibody selection work resulted in four lead monoclonal antibody (mAb) candidates with unique specificities, selectivities, and affinities. We continue here with the kinetic and structural characterization of nanobodies that have been synthesized based on the protein sequences of the top mAb leads. Using SPR epitope binning and HDX-MS epitope mapping, epitopic diversity was observed with the four nanobody candidates.
The synergistic approach of machine learning-driven proteomics, SPR, and HDX-MS make it possible to accelerate the camelid nanobody discovery and development workflow. Working with physiologically relevant, high-affinity binders – identified directly from serum through de novo protein sequencing – can reduce the need for extensive stability and pharmacokinetic optimization downstream. Combined with rapid lead optimization through epitope mapping and binning, the need to produce the entire population of candidate antibodies can be minimized.