Gadoxetate has had increasing applications in the liver, from lesion1 and fibrosis characterization2-3 to investigation of transporter function4-5. Recently, Ulloa et al described a compartmental modeling approach to measure rate constants of gadoxetate influx and efflux as a potential biomarker of hepatobiliary transporter function5, as OATP and NTCP transporters mediate gadoxetate uptake, and biliary efflux is mediated by Mrp2 and Mrp3 transporters4-5. The purpose of this study was to 1) reproduce the described acquisition and post-processing and 2) apply the MRI assay to variations of Oatp1a/1b knock-out (KO) rats to potentially differentiate between degrees of transporter function through KO models.

Littermate wild-type (WT), heterozygous (HET), and homozygous (HOM) Oatp1a/1b knockout (KO) rats (11-13 weeks old, n = 6/group) were used in this study. Rats were imaged at 7T using a quadrature body coil. We theorized that we would observe a clear gene dosage effect; the HOM mutants for Oatp should have a profound reduction in liver enhancement and influx rate compared to HET and WT.

A bottle of water was placed on the rat as a reference signal. FLASH images were acquired using the following parameters: FOV = 6 x 6 cm, slice thicknesss = 2 mm, 17 axial slices covering the liver and spleen, TE/TR = 3.2/200 ms, 3 averages, scan time of 57 sec prior to delays from respiratory triggering, flip = 30˚, and an imaging matrix of 96 x 96. 10 baselines were acquired, 0.1 mL/kg gadoxetate (181.43 mg/mL, Bayer Healthcare) was administered IV at the 11th measurement, and 59 additional measurements after contrast were acquired for a total of 70 measurements. Prior to contrast administration, T1 maps with 6 TRs were acquired using the same slice prescription and resolution as the dynamic series.

Next, we applied the 3-parameter modeling as previously described5 to solve for the influx (k1) and efflux (Vmax and KM) rate constants, and compared the results to the vehicle group of Ulloa et al for validation. This modeling also calculated the concentration of gadoxetate in the spleen (CSpleen), hepatocytes (CHep) and liver extracellular space (CES) over time, and the areas under each of these the curves were calculated (AUC). The means and standard deviations for each rate constant were calculated for each of the groups, and t-tests were performed to determine significant differences between groups.

Table 1 compares the previously published rate constants in vehicle animals to the WT rats used on this study. k1 appears equivalent for both studies, although slight differences exist between Vmax and KM. For the purpose of this work, equivalence of k1 is most important since the influx transporters would be affected by Oatp1a/1b KO.

As expected, differences in signal enhancement (Figure 1) and rate constants (Table 2) were seen between the three groups of rats. Little enhancement is seen for the HOM group, and the WT and HET rats have relatively similar behavior, which is supported by the plot of CHep over time (Figure 2). Significant decreases were seen in k1 for the HOM group compared to the WT (p = 0.004) and HET (p = 0.0005) groups, although no differences in k1 were seen comparing the WT and HET groups (p = 0.19). No differences were seen between any group for k2, although this was expected since all efflux transporters were present.

Finally, due the KO of the influx transporters, higher concentrations of gadoxetate were found in the spleen and liver extracellular space of the HOM rats compared to the WT and HET rats (Figure 2). Similar to Figure 1, the AUC of CHep also demonstrates slightly less uptake into the hepatocytes of HET rats when compared to WT rats.

This work successfully reproduced the assay described by Ulloa et al. Differences in influx using the HOM rats were seen, and as expected, decreases in k1 were seen while k2 was unaffected between all groups. While a decrease in k1 was seen for the HET group, it was not statistically different than the WT group; the efficiency of the HET and HOM KO, and hence the density of transporters for each model, is not known. Improvements to the accuracy of the efflux modeling and validation in efflux KO rats prior to screening drug candidates for drug-induced liver injury and drug-drug interactions are underway. Additionally, these methods may allow correlation of more traditional pharmacology readouts (AUC) with the described investigative biomarkers to better understand transporter physiology.