Screening for Susceptibility-Related Biomarkers of Diclofenac-Induced Liver Injury in Rats Using Metabolomics

Abstract

Early identification of individuals susceptible to idiosyncratic drug-induced liver injury (IDILI) is a challenging unmet demand. Diclofenac, one of the most widely available over-the-counter drugs for pain management worldwide, may induce liver dysfunction, acute liver failure, and death. Herein, we report that diclofenac-related hepatobiliary adverse reactions occurred more frequently in cases with immune activation. Furthermore, experiments with rats demonstrated divergent hepatotoxicity responses in individuals exposed to diclofenac, and modest inflammation potentiated diclofenac-induced liver injury. Susceptible rats had unique plasma metabolomic characteristics, and as such, the metabolomic approach could be used to distinguish susceptible individuals. The 23 identified susceptibility-related metabolites were enriched by several metabolic pathways related to acute-phase reactions of immunocytes and inflammatory responses, including sphingolipid, tyrosine, phenylalanine, tryptophan, and lipid metabolism pathways. This finding implies a mechanistic role of metabolic and immune disturbances affects susceptibility to diclofenac-IDILI. Further nine metabolite biomarkers with potent diagnostic capabilities were identified using receiver operating characteristic curves. These findings elucidated the potential utility of metabolomic biomarkers to identify individuals susceptible to drug hepatotoxicity and the underlying mechanism of metabolic and immune disturbances occurring in IDILI.

Keywords: biomarker; diclofenac; idiosyncratic drug-induced liver injury; metabolomics; susceptible individual.

FIGURE 1

Characteristics of Dicl-related hepatobiliary ADR. (A) Time of hepatobiliary ADR, accumulated dose, and improvement time of Dicl-related hepatobiliary ADR. (B) Sex, age, and underlying complications of Dicl-related hepatobiliary ADR.

FIGURE 2

Phenotypes of Dicl-IDILI in normal rats or model rats with modest inflammation. (A) Dose-responsive liver injury phenotype of Dicl exposure in normal rats. Dicl doses ranged from 0 to 100 mg/kg (i.p.) (n = 6, ^## p < 0.01 vs. control). (B) The liver injury phenotype of Dicl (20 mg/kg, i.p.) exposure in rats with modest inflammation. Con (n = 10), the control group; Mod (n = 10), the non-toxic dose of the LPS-induced modest inflammation model group; Dicl (n = 10), normal rats treated with a low dose of Dicl (20 mg/kg, i.p); Mod/Dicl (n = 20), the modest inflammation model of rats treated with a low dose Dicl (20 mg/kg, i.p). The results are expressed as mean ± SD, and significant differences are indicated (^∗∗ p < 0.01, vs. Mod). (C) Histological alterations in rat livers among the different groups (H&E stained, ×200 and ×400 magnification).

FIGURE 3

Metabolomic profile analysis of Dicl-IDILI in rats. (A) The PCA score plots for different experimental groups of either negative (left) or positive (right) ESI modes, respectively. The rats were randomized into four groups as follows: the Con group (n = 8), Dicl group (n = 8), Mod group (n = 8), and Mod/Dicl group (n = 15). (B) OPLS-DA score plots of the paired groups (Con vs. Mod, Con vs. Dicl, and Mod vs. Mod/Dicl). (C) The number of shared and unique metabolites visualized in the Venn diagram for Con vs. Mod, Con vs. Dicl, and Mod vs. Mod/Dicl. (D) The clustered heat map of the 40 metabolites with significantly different expressions among the control, Mod, Dicl, and Mod/Dicl groups. The colors in the heat map indicate increased (red) or decreased (blue) relative metabolite contents.

FIGURE 4

Profile of susceptibility-related metabolomic biomarkers of Dicl-IDILI. (A) The relative abundance radar plot of 23 metabolites. The blue line represents the control group (Con), and the orange line represents the susceptibility model group (Mod). (B) The bubble diagram of the disturbed metabolic pathways. (C) The network map of metabolic pathways and metabolites. Metabolites in red or blue indicate up- or downregulated expressions, respectively, in the Mod group compared to the control group.

FIGURE 5

Susceptibility-related metabolite biomarkers of Dicl-IDILI. (A) Cluster analysis of the area under the curve (AUC) and p-values of the receiver operating characteristic (ROC) curve of each of the 23 metabolites in discriminating the susceptibility model (Mod) group from the control (Con) group. The color indicates the AUC value. (B) ROC curves of nine metabolites in discriminating the Mod group from the Con group. (C) Identification of the susceptible-related metabolic fingerprint (eigenmetabolite). (D) Differential expressions of nine metabolites in the Mod group and the Con group (^∗ p < 0.05,^∗∗ p < 0.01 vs. Con).