Identification of Novel High-Affinity Substrates of OCT1 Using Machine Learning-Guided Virtual Screening and Experimental Validation

OCT1 is the most
highly expressed cation transporter in the liver
and affects pharmacokinetics and pharmacodynamics. Newly marketed
drugs have previously been screened as potential OCT1 substrates and
verified by virtual docking. Here, we used machine learning with transport
experiment data to predict OCT1 substrates based on classic molecular
descriptors, pharmacophore features, and extended-connectivity fingerprints
and confirmed them by in vitro uptake experiments.
We virtually screened a database of more than 1000 substances. Nineteen
predicted substances were chosen for in vitro testing.
Sixteen of the 19 newly tested substances (85%) were confirmed as,
mostly strong, substrates, including edrophonium, fenpiverinium, ritodrine,
and ractopamine. Even without a crystal structure of OCT1, machine
learning algorithms predict substrates accurately and may contribute
not only to a more focused screening in drug development but also
to a better molecular understanding of OCT1 in general.