RT Journal Article
SR Electronic
T1 Predicting Drug Interactions with Human Equilibrative Nucleoside Transporters 1 and 2 Using Functional Knockout Cell Lines and Bayesian Modeling
JF Molecular Pharmacology
JO Mol Pharmacol
FD American Society for Pharmacology and Experimental Therapeutics
SP MOLPHARM-AR-2020-000169
DO 10.1124/molpharm.120.000169
A1 Siennah R. Miller
A1 Xiaohong Zhang
A1 Raymond K. Hau
A1 Joseph L. Jilek
A1 Erin Q. Jennings
A1 James J. Galligan
A1 Daniel H. Foil
A1 Kimberley M. Zorn
A1 Sean Ekins
A1 Stephen H. Wright
A1 Nathan J. Cherrington
YR 2020
UL http://molpharm.aspetjournals.org/content/early/2020/12/01/molpharm.120.000169.abstract
AB Equilibrative nucleoside transporters (ENT) 1 and 2 facilitate nucleoside transport across the blood-testis barrier (BTB). Improving drug entry into the testes with drugs that use endogenous transport pathways may lead to more effective treatments for diseases within the reproductive tract. In this study, CRISPR/Cas9 was used to generate HeLa cell lines in which ENT expression was limited to ENT1 or ENT2. We characterized uridine transport in these cell lines and generated Bayesian models to predict interactions with the ENTs. Quantification of [3H]uridine uptake in the presence of the ENT specific inhibitor S-(4-nitrobenzyl)-6-thioinosine (NBMPR) demonstrated functional loss of each transporter. Nine nucleoside reverse transcriptase inhibitors and thirty-seven nucleoside/heterocycle analogs were evaluated to identify ENT interactions. Twenty-one compounds inhibited uridine uptake and abacavir, nevirapine, ticagrelor, and uridine triacetate had different IC50 values for ENT1 and ENT2. Total accumulation of four identified inhibitors was measured with and without NBMPR to determine if there was ENT-mediated transport. Clofarabine and cladribine were ENT1 and ENT2 substrates, while nevirapine and lexibulin were ENT1 and ENT2 non-transported inhibitors. Bayesian models generated using Assay Central® machine learning software yielded reasonably high internal validation performance (ROC > 0.7). ENT1 IC50-based models were generated from ChEMBL; subvalidations using this training dataset correctly predicted 58% of inhibitors when analyzing activity by percent uptake and 63% when using estimated-IC50 values. Determining drug interactions with these transporters can be useful in identifying and predicting compounds that are ENT1 and ENT2 substrates, and can thereby circumvent the BTB through this transepithelial transport pathway. Significance Statement This study is the first to predict drug interactions with ENT1 and ENT2 using Bayesian modeling. Novel CRISPR/Cas9 functional knockouts of ENT1 and ENT2 in HeLa S3 cells were generated and characterized. Determining drug interactions with these transporters can be useful in identifying and predicting compounds that are ENT1 and ENT2 substrates, and can circumvent the blood-testis barrier through this transepithelial transport pathway in Sertoli cells.