Acetylcholinesterase (AChE) is an
important drug target in neurological
disorders like Alzheimer’s disease, Lewy body dementia, and
Parkinson’s disease dementia as well as for other conditions
like myasthenia gravis and anticholinergic poisoning. In this study,
we have used a combination of high-throughput screening, machine learning,
and docking to identify new inhibitors of this enzyme. Bayesian machine
learning models were generated with literature data from ChEMBL for
eel and human AChE inhibitors as well as butyrylcholinesterase inhibitors
(BuChE) and compared with other machine learning methods. High-throughput
screens for the eel AChE inhibitor model identified several molecules
including tilorone, an antiviral drug that is well-established outside
of the United States, as a newly identified nanomolar AChE inhibitor.
We have described how tilorone inhibits both eel and human AChE with
IC50’s of 14.4 nM and 64.4 nM, respectively, but
does not inhibit the closely related BuChE IC50 > 50
μM.
We have docked tilorone into the human AChE crystal structure and
shown that this selectivity is likely due to the reliance on a specific
interaction with a hydrophobic residue in the peripheral anionic site
of AChE that is absent in BuChE. We also conducted a pharmacological
safety profile (SafetyScreen44) and kinase selectivity screen (SelectScreen)
that showed tilorone (1 μM) only inhibited AChE out of 44 toxicology
target proteins evaluated and did not appreciably inhibit any of the
485 kinases tested. This study suggests there may be a potential role
for repurposing tilorone or its derivatives in conditions that benefit
from AChE inhibition.