Epidermal growth factor receptor (EGFR) is a highly prevalent driver of non-small cell lung cancer. Triple mutant EGFR is resistant to all 3rd generation EGFR inhibitors and there is urgent clinical need for compounds that are effective against this oncogenic target. The compound EMI1 was discovered through MaMTH-DS screening, a cell-based assay able to interrogate receptor tyrosine kinase activation and effector recruitment. EMI1 blocks activation of EGFR triple mutant through a novel mechanism, leaving the kinase activity of the protein unaffected. To expand the number of chemical tools available to explore this unique therapeutic avenue, we endeavoured to find new hits with the same mechanism of action. Using the deep learning engine MatchMaker, a polypharmcological profile of EMI1 was generated that consists of a list of proteins ranked ordered by their likelihood of binding. Using Cyclica’s Ligand Design methodology, we explored Enamine’s REAL space of 1010 molecules and selected those molecules most closely matching the predicted polypharmacology of EMI1 for testing. This led to the discovery of CYC7, which, like EMI1, is able to block EGFR triple mutant activation in cell-based assays.

Encoding activity as polypharmacology: Bypassing target deconvolution

ACS Spring Meeting 2021

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.