The System Profile of Renal Drug Transporters in Tubulointerstitial Fibrosis Model and Consequent Effect on Pharmacokinetics

Abstract

With the widespread clinical use of drug combinations, the incidence of drug-drug interactions (DDI) has significantly increased, accompanied by a variety of adverse reactions. Drug transporters play an important role in the development of DDI by affecting the elimination process of drugs in vivo, especially in the pathological state. Tubulointerstitial fibrosis (TIF) is an inevitable pathway in the progression of chronic kidney disease (CKD) to end-stage renal disease. Here, the dynamic expression changes of eleven drug transporters in TIF kidney have been systematically investigated. Among them, the mRNA expressions of Oat1, Oat2, Oct1, Oct2, Oatp4C1 and Mate1 were down-regulated, while Oat3, Mrp2, Mrp4, Mdr1-α, Bcrp were up-regulated. Pearson correlation analysis was used to analyze the correlation between transporters and Creatinine (Cr), OCT2 and MATE1 showed a strong negative correlation with Cr. In contrast, Mdr1-α exhibited a strong positive correlation with Cr. In addition, the pharmacokinetics of cimetidine, ganciclovir, and digoxin, which were the classical substrates for OCT2, MATE1 and P-glycoprotein (P-gp), respectively, have been studied. These results reveal that changes in serum creatinine can indicate changes in drug transporters in the kidney, and thus affect the pharmacokinetics of its substrates, providing useful information for clinical use.

Keywords: MATE1; OCT2; P-gp; drug transporters; pharmacokinetics.

Figure 1

The renal parameters for TIF model. (A)The right kidney of TIF group compared with that of the control group on 4th, 7th, 10th, and 14th days. C: Control; D4: Day 4 of TIF model; D7: Day 7 of TIF model; D10: Day 10 of TIF model; D14: Day 14 of TIF model. (B) In the anatomical model group on different days, the ligation kidney of rats was weighed, and the weight obtained was compared with that of the control group. (C) Renal index was calculated as the ratio of the weight of the left kidney and the ligation kidney to body weight. Data were expressed as mean ± SD. L: left kidney, R: ligated kidney. (D) Changes in Cr in serum concentration of rats at different modeling time, compared with the control group. (E) Changes in Ccr in serum concentration of rats at different modeling times, compared with the control group. **** p < 0.0001 *** p < 0.001, ** p < 0.01, * p < 0.05, ^ns p > 0.05.

Figure 2

Histopathological results. Sections of the right kidney of on rats at 4th, 7th, 10th, and 14th days were taken to H&E staining (A). Scale: 600 μm (200×). Histopathological changes in kidney sections were scored as a semi-quantitative percentage of damaged area: 0, normal; 1, cortical area <25%; 2, cortical area 25–50%; 3, the cortical area is 50–75%; 4, cortical area >75%, compared with control group. (B) Sections of the right kidney of on rats at 4th, 7th, 10th and 14th days were taken for Masson staining. Fibrosis area was quantified by Image J Pro Plus 6.0 compared with the control group. **** p < 0.0001 *** p < 0.001, ** p < 0.01.

Figure 3

The variation in mRNA expression of drug transporter in TIF kidney. Samples were detected on 4th, 7th, 10th and 14th days. C: Control; D4: Day 4 of TIF model; D7: Day 7 of TIF model; D10: Day 10 of TIF model; D14: Day 14 of TIF model. *** p < 0.001, ** p < 0.01, * p < 0.05, ^ns p > 0.05.

Figure 4

(A) Real-time q-PCR analysis showed that the mRNA content of these transporters was 2^−∆∆Ct relative to the mRNA β-actin expression, and Pearson correlation was used to analyze the dynamic changes between the main kidney transporters and Cr. A correlation analysis of the relative size of 2^−∆∆Ct between the changed transporter and β-actin and Ccr was conducted. (B) The mRNA expression of transporter was detected on the 4th, 7th, 10th, and 14th days, and then the correlation between the expression value of transporter and the Ccr rate was analyzed.

Figure 5

(A) The expression of 11 transporters in different groups was correlated with the results of Masson staining. (r > 0.6, strong correlation, 0.4 < r < 0.6, strong correlation, 0.2 < r, weak correlation) (B) Correlation between renal fibrosis and dynamic changes in Cr.

Figure 6

PK changes of renal OCT2, MATE1, P-gp substrates in the TIF model. (A)/(a) blank plasma + ebesartan (A)/(b) Blank plasma + Ganciclovir standard + internal standard (A)/(c), plasma sample (B)/(a) blank plasma + ebesartan (B)/(b) Blank plasma + Digoxin standard + internal standard (B)/(c), plasma sample (C)/(a) blank plasma + ebesartan (C)/(b) Blank plasma + Cimetidine standard + internal standard (C)/(c), plasma sample (D) the drug-concentration time curve of ganciclovir. (E) the drug concentration-time curve of Digoxin. (F) the drug-concentration time curve of cimetidine.