Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter

Inadequate
bioavailability is one of the most critical reasons
for the failure of oral drug development. However, the way that substructures
affect bioavailability remains largely unknown. Serotonin transporter
(SERT) inhibitors are first-line drugs for major depression disorder,
and improving their bioavailability may be able to decrease side-effects
by reducing daily dose. Thus, it is an excellent model to probe the
relationship between substructures and bioavailability. Here, we proposed
the concept of “nonbioavailable substructures”, referring
to substructures that are unfavorable to bioavailability. A machine
learning model was developed to identify nonbioavailable substructures
based on their molecular properties and shows the accuracy of 83.5%.
A more potent SERT inhibitor DH4 was discovered with
a bioavailability of 83.28% in rats by replacing the nonbioavailable
substructure of approved drug vilazodone. DH4 exhibits
promising anti-depression efficacy in animal experiments. The concept
of nonbioavailable substructures may open up a new venue for the improvement
of drug bioavailability.