Experimental and computational approaches for evaluating molecule interactions with equilibrative nucleoside transporters 1 and 2

Abstract

Equilibrative nucleoside transporters (ENTs) facilitate the equilibrative movement of nucleosides and nucleobases across cell membranes in a sodium-independent manner. ENT1 (SLC29A1) and ENT2 (SLC29A2) also transport nucleoside analogs and can affect the pharmacokinetics and pharmacodynamics of drugs used in cancer, viral infections, and inflammatory disorders. ENT1 and ENT2 may be differentiated functionally by their sensitivity to inhibition by nitrobenzylthioinosine (NBMPR), and we used this difference in NBMPR sensitivity to create a HeLa-based ENT2 inhibition assay. We then screened a library of 1600 diverse compounds composed of drugs and natural products for inhibition against ENT1 and ENT2, selecting a subset of compounds for side-by-side comparison of dose-response studies. We used these screening data to build machine learning models for ENT1 and ENT2 inhibition, employing dataset balancing and conformal prediction to adjust for the asymmetrical nature of the data. A random forest model predicted a prospective test set of 44 additional molecules (from the MedChem Express Drug Repurposing Library [2700 compounds]) as potential ENT1 inhibitors with 59% accuracy. This resulted in the identification of the Food and Drug Administration-approved drugs isradipine, avanafil, and istradefylline as inhibitors of ENT1. These new experimental and computational methods and models for these clinically relevant transporters can be used to evaluate drug-transporter interactions early in drug discovery, before testing in vivo. SIGNIFICANCE STATEMENT: Recent regulatory guidance have suggest the inclusion of the equilibrative nucleoside transporters (eg, ENT1 and ENT2) as transporters with emerging clinical relevance for in vitro and in vivo assessment. We have screened over 1600 diverse molecules, allowing us to build machine learning models that in turn were further used to make predictions to validate the models. Our combined experimental and machine learning approach resulted in the identification of multiple Food and Drug Administration-approved medications as inhibitors of ENT1 or ENT2.

Keywords: Compound library; Conformal prediction; Drug screening; Equilibrative nucleoside transporters; Machine learning models; Random forest model.

Figure 1.

Characterization of hENT1 and hENT2 activity by the inhibition by 100nM and 100uM of NBMPR of [³H]uridine uptake into WT HeLa and HeLa FlpIn Cell Lines overexpressing hENT2.

Figure 2.

Time course of uridine into wild type HeLa cells and equilibrative nucleoside transporter 2 (hENT2) overexpressing HeLa cells. Time course of [³H]uridine (~10 nM) open squares represent transport measured in the presence of 5mM unlabeled uridine. Each point is the mean of four replicate measures of uptake determined in a single representative experiment.

Figure 3.

Kinetics of radiolabeled uridine uptake at pH 7.4. WT HeLa cells and HeLa cells overexpressing hENT1 were exposed for 5 min to Waymouth’s buffer (WB; pH 7.4) containing: ~10 nM [³H]uridine and increasing concentrations of unlabeled uridine. Each open circle is the mean (±SD) of total uptakes measured in three separate experiments, each determined in four replicate wells. J_max, maximal rate of transport, K_t, Michaelis constant.

Figure 4.

A) Inhibitory effect of 1600 test compounds from the Spectrum collection on the hENT1 and hENT2 -mediated transport of 10 nM labeled uridine. The 5 min accumulation of substrate was measured in the presence of a 20 μM concentration of each test agent. The scatter dot represents each individual value (expressed relative to uptake measured in the absence of inhibitor, i.e., “control”) determined in two-three separate experiments, each measured in triplicate. The dots are arranged from no inhibition (left side) to complete inhibition (right side). The gray area represents the “inactive” inhibitors (>20% control, to the left). The horizontal blue striped area indicates above 20% inhibition; The horizontal red striped area indicates above 50% inhibition.

Figure 5.

Comparison of model performance using conformal predictions on a stratified test set. a) Precision scores for conformal predictions of the ENT1 Full and ENT1 Balanced models against the ENT1 stratified test set for the given values of alpha. b) Precision scores for conformal predictions of the ENT2 Full and ENT2 Balanced models against the ENT2 stratified test set for the given values of alpha. c) Number of compounds scored active by the ENT1 Full and ENT1 Balanced models at the given values of alpha. d) Number of compounds scored active by the ENT2 Full and ENT2 Balanced models at the given values of alpha. Dotted-lines represent the selected alpha value for the given target.

Figure 6.

Inhibition by ketoconazole (A), sinensetin (B), medrysone (C), myrcetin (D), benzbromarone (E) canagliflozin(F), gefitinib (G), quercetin(H), kanamycin (I), domperidone (J), naringin (K), methapyrilene (L), flumazenil (M) & losartan (N) uptake into WT Hela cells (teal squares) or HeLa cells overexpressing hENT2 (purple circles). Five-minute uptakes of ~10 nM [³H]uridine were measured in the presence of increasing concentrations of each inhibitor. Each point is the mean (±SD) of results determined in two to three separate experiments. The IC₅₀ values are listed on Table 1.