ENGINEERING MULTIVALENCY FOR SUPERSELECTIVE RECOGNITION OF PATHOGEN TARGETS

The recent pandemic has emphasised the need to rapidly develop materials that can detect, inhibit and destroy viruses and other pathogens. Antibodies achieve selective recognition of biological targets at ultralow concentrations via multivalent interactions, i.e. the cooperative binding of multiple sites to cognate receptors. While antibody deployment is ubiquitous in diagnostics and therapy, their animal-based manufacturing is costly, time-consuming, lacks universal applicability and raises ethical concerns following EU Directive 2010/63/EU. Recent theoretical studies have established novel material design principles for the emergence of so-called superselectivity, yet the required detailed interplay of valency and individual bond strength with repulsive steric interactions remains inaccessible with available synthetic pathways. The aim of EngToTarget is to establish a closed-loop engineering approach to superselective nanoparticles that is based on the acquisition, analysis and learning from large data sets. An experimental platform will be developed that combines robotic hardware, synthetic procedures amenable to automation, high-throughput characterisation and advanced statistical methods. Influenza, cholera and SARS-CoV-2 will be used as case studies to demonstrate the effectiveness of the method for achieving greatly enhanced selectivity and target discrimination in physiological fluids. Such nanoparticles will be deployed in novel detection principles that are enabled by superselectivity and validated for their effectiveness in diagnostic assays. The methodology is generic, affordable and open-source, allowing for rapid adaption to a broad range of emerging viruses or other biological targets (e.g. cancer biomarkers) and serving a global community of experimentalists with rational, effective and expedited approaches to nanoparticle engineering.