Computational discovery

Networks are the mechanistic bridge between genotype and phenotype and we strive to address the lack of biological understanding to unlock key unmet medical needs. Modelling and analysis of biological networks provides a novel approach to drug discovery that explicitly considers the true complexity of biology. By representing, as closely as possible, the biological systems we are seeking to disrupt, we increase the likelihood of identifying and developing effective therapies.

To complement our GalNAc-siRNA capabilities and feed our in-house pipeline, we have created a tailored hepatocyte-focused specialisation within our core computational platform, including the development of a comprehensive hepatocyte knowledge graph.

We are compiling experimental data at genome-wide scale using bespoke human hepatocyte assays and combining it with our existing state-of-the-art network analytics and artificial intelligence/machine learning (“AI/ML”) approaches to create a seamless connection between the computer and the laboratory.

RNA interference

Our therapeutic modality of choice is RNAi, on which we are basing our in-house pipeline. GalNAc (N-Acetylgalactosamine)-conjugated siRNA is a powerful and well validated commercial-stage technology, which enables specific delivery to hepatocytes in the liver, patient-friendly subcutaneous administration and a long duration of therapeutic action.

Our proprietary GalNAc-sRNA platform technology enables us to rapidly design potential drug candidates to prosecute novel targets identified using our computational discovery engine. The use of RNAi as a therapeutic modality allows us to minimise speed asymmetries with the acceleration of early discovery stages allowed by our computational capabilities.