Our proprietary technology platform, which we refer to as our Mutation-Allostery-Pharmacology, or MAP platform, is built on three central pillars:
Through comprehensive analysis of population-level genetic sequencing data, we identify oncogenic mutations among hundreds of unique alterations within a single gene. Our MAP platform algorithm uses genetic and proteomic features to rank mutations for potential oncogenicity. We use our algorithm as a machine learning tool to classify mutations as either pathogenic or benign and predict the probability, or MAP score, that a mutation is pathogenic.
We confirm the oncogenicity of the identified mutations through cell and tumor models and reveal how these mutations drive conformational changes in proteins. Many oncogenic mutations confer conformational changes affecting both local and distal (allosteric) sites relative to ATP-binding region, and often multiple mutations give rise to shared conformational states. This allows us to group subsets of mutations into families based upon similar protein structures and shared selectivity profiles.
Using these shared characteristics, we seek to develop product candidates designed to inhibit only the intended mutation family.
