  The link between in vitro hERG ion channel inhibition and subsequent in vivo
QT interval prolongation, a critical risk factor for the development of
arrythmias such as Torsade de Pointes, is so well established that in vitro
hERG activity alone is often sufficient to end the development of an otherwise
promising drug candidate. It is therefore of tremendous interest to develop
advanced methods for identifying hERG-active compounds in the early stages of
drug development, as well as for proposing redesigned compounds with reduced
hERG liability and preserved on-target potency. In this work, we present
CardioGenAI, a machine learning-based framework for re-engineering both
developmental and commercially available drugs for reduced hERG activity while
preserving their pharmacological activity. The framework incorporates novel
state-of-the-art discriminative models for predicting hERG channel activity, as
well as activity against the voltage-gated NaV1.5 and CaV1.2 channels due to
their potential implications in modulating the arrhythmogenic potential induced
by hERG channel blockade. We applied the complete framework to pimozide, an
FDA-approved antipsychotic agent that demonstrates high affinity to the hERG
channel, and generated 100 refined candidates. Remarkably, among the candidates
is fluspirilene, a compound which is of the same class of drugs
(diphenylmethanes) as pimozide and therefore has similar pharmacological
activity, yet exhibits over 700-fold weaker binding to hERG. We envision that
this method can effectively be applied to developmental compounds exhibiting
hERG liabilities to provide a means of rescuing drug development programs that
have stalled due to hERG-related safety concerns. We have made all of our
software open-source to facilitate integration of the CardioGenAI framework for
molecular hypothesis generation into drug discovery workflows.
