Data-Driven Construction of Antitumor Agents with Controlled Polypharmacology

Controlling
which particular members of a large protein family
are targeted by a drug is key to achieving a desired therapeutic response.
In this study, we report a rational data-driven strategy for achieving
restricted polypharmacology in the design of antitumor agents selectively
targeting the TYRO3, AXL, and MERTK (TAM) family tyrosine kinases.
Our computational approach, based on the concept of fragments in structural
environments (FRASE), distills relevant chemical information from
structural and chemogenomic databases to assemble a three-dimensional
inhibitor structure directly in the protein pocket. Target engagement
by the inhibitors designed led to disruption of oncogenic phenotypes
as demonstrated in enzymatic assays and in a panel of cancer cell
lines, including acute lymphoblastic and myeloid leukemia (ALL/AML)
and nonsmall cell lung cancer (NSCLC). Structural rationale underlying
the approach was corroborated by X-ray crystallography. The lead compound
demonstrated potent target inhibition in a pharmacodynamic study in
leukemic mice.