(Collaboration between Deep Origin and NYU Langone Health; Deep Origin contributors include Tigran Abramyan, Hayk Saribekyan, Lev Tsidilkovski, and Garegin Papoian)

A new npj Precision Oncology paper from Deep Origin’s computational biology team, in collaboration with NYU Langone Health, maps how lung cancers might outsmart the latest wave of targeted EGFR inhibitors.

Using deep mutational scanning—a high-throughput technique that tests tens of thousands of possible amino-acid substitutions in parallel—the team built an exhaustive resistance atlas for BLU-945, a fourth-generation tyrosine kinase inhibitor designed to overcome the stubborn T790M and C797S mutations that limit earlier drugs.

The screen recovered known osimertinib escape routes (like L718Q/V) and revealed new liabilities at residues including Q791L/K, K716T, and K728E. Computational simulations by Deep Origin scientists showed how these subtle changes reshape the drug’s binding pocket and weaken inhibitor affinity—predictions later mirrored in clinical cases, where patients acquiring L718X mutations experienced early progression on BLU-945.

Why does this matter for drug-discovery scientists?

This study demonstrates how comprehensive resistance profiling can anticipate failure modes before they appear in the clinic.

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