From Cancer to Pain Target by Automated Selectivity Inversion of a Clinical Candidate

Elimination of inadvertent binding
is crucial for inhibitor design
targeting conserved protein classes like kinases. Compounds in clinical
trials provide a rich source for initiating drug design efforts by
exploiting such secondary binding events. Considering both aspects,
we shifted the selectivity of tozasertib, originally developed against
AurA as cancer target, toward the pain target TrkA. First, selectivity-determining
features in binding pockets were identified by fusing interaction
grids of several key and off-target conformations. A focused library
was subsequently created and prioritized using a multiobjective selection
scheme that filters for selective and highly active compounds based
on orthogonal methods grounded in computational chemistry and machine
learning. Eighteen high-ranking compounds were synthesized and experimentally
tested. The top-ranked compound has 10000-fold improved selectivity
versus AurA, nanomolar cellular activity, and is highly selective
in a kinase panel. This was achieved in a single round of automated
in silico optimization, highlighting the power of recent advances
in computer-aided drug design to automate design and selection processes.