1. Introduction
Drug-induced liver injury (DILI) is an unexpected toxic effect of bioactive compounds, not linked to their intrinsic pharmacological properties of the drug. This event can occur during the initial clinical phases of drug development as well as during the widespread clinical use of a drug, including self-consumption of over-the-counter drugs containing bioactive compounds. Hepatotoxicity is among the major reasons for drug development discontinuation and withdrawal from the market, representing a major safety and economic concern for the pharmaceutical industry and health systems. Although DILI is in most cases self-remitting when the administration of the responsible drug is stopped [
1,
2,
3], in rare cases, it may lead to a potentially serious adverse reaction that can result in a spectrum of liver problems, ranging from mild abnormalities in liver function tests to severe hepatitis, liver failure [
4], or even death [
5], making it particularly challenging to understand and manage [
6]. DILI stands as the predominant trigger for acute liver failure (ALF) in Western regions, boasting a case fatality rate ranging from 10% to 50% [
6]. Annually in the United States, there are around 2000 instances of acute liver failure, with drugs contributing to over half of these cases (39% attributed to acetaminophen and 13% to idiosyncratic reactions from other medications). Among patients hospitalized with jaundice, drugs account for 2–5% of cases, and approximately 10% of all acute hepatitis cases are linked to drug use [
7].
In addition, the incidence of DILI events is rising in parallel to the introduction of new drugs, increasing life expectancy and poly-medication in elderly people, and the widespread use of self-prescribed complementary dietetic or herbal products.
Within hepatotoxins causing DILI, the most easily identifiable ones are those having dose-dependent effects in all individuals exposed. On the contrary, idiosyncratic DILI (iDILI) stands out as an often-unpredictable phenomenon occurring in certain individuals. iDILI refers to liver injury caused by a drug or medication in a manner that is not clearly dose-dependent, sometimes with superimposed immunological hypersensitivity features that occur only in certain patients and also strongly linked to a patient’s characteristics (genetic, metabolic, etc.) [
8,
9]. However, the mechanisms underlying iDILI remain poorly understood.
RUCAM, a widely recognized diagnostic scale, effectively evaluates cases of DILI by utilizing well-documented clinical characteristics [
10,
11]. DILI clinical diagnosis involves anamnesis and differential diagnosis, as patients often exhibit nonspecific symptoms like fatigue, jaundice, abdominal pain, and nausea, and increased liver tests which can resemble other liver disorders or common illnesses [
12]. The clinical DILI outcome typically ranges from hepatocellular damage to cholestasis phenotype, which is biochemically identified using the “R score”. This score is calculated as a ratio: (patient’s ALT (alanine aminotransferase, ALT)/upper limit of ALT normality)/(patient’s ALP (alkaline phosphatase, ALP)/upper limit of ALP normality). It aids in classifying the DILI episode as cholestatic (R score < 2), mixed (2 < R score < 5), or hepatocellular (R score > 5) [
10,
11]. Hepatocellular injury primarily affects hepatocytes, which are the main functional cells of the liver, while cholestatic injury may also involve affectation of cholangiocytes and the bile ducts, disrupting the bile flow. However, DILI can be a complex event, and its classification based exclusively on these two liver enzymes may not capture the interindividual variability and the full array of DILI subtypes existing between the pure cholestatic and the pure hepatocellular injury patterns [
9,
12]. Thus, significant progress is required to enhance the precision of DILI classification phenotypes and effectively translate biochemical data into decision making within clinical practice.
Metabolomics is a valuable approach for characterizing the metabolic pathways and networks of biological systems [
13]. Our approach involves scrutinizing pertinent metabolic alterations occurring in the liver during a DILI event, which manifest in the patient’s sera as well.
Preceding metabolomics studies have supported its utility in evaluating hepatotoxicity [
14,
15]. A previous study on DILI identified metabolomic alterations in the patient’s plasma associated with the type and severity of the DILI event, enabling an accurate classification and the follow up of the patient’s clinical progression. The study identified conjugated bile acids and glycerophospholipids as the most significant classes of metabolites discriminating the different DILI phenotypes [
16]. Multivariate models enabled the discrimination among the various DILI phenotypes and the recovered status according to the metabolome analysis. The analysis included the development of three Partial Least Squares Discriminant Analysis (PLS–DA) models, discriminating hepatocellular vs. cholestatic and recovered patients, cholestatic vs. hepatocellular and recovered patients, and recovered vs. hepatocellular and cholestatic DILI patients. Model development included a first step of model optimization using double-cross validation, before its application to new samples. To allow a visual identification of the severity and extent of the different DILI phenotypes, the PLS predicted values using the three models were graphically represented within ternary plots. Thus, the position in the ternary diagram summarizes the patient’s metabolomic status at a specific time point, which can be interpreted as a summary of the contribution of each DILI phenotype to the patient’s current status, offering insight into differences among mixed-type DILI cases (see
Figure 1).
This study explored the utility of assessing changes in the plasma metabolomic profiles of DILI patients to characterize this condition better and numerically, and compare results obtained with the standard clinical characterization. For drugs inducing iDILI, examining the impact of DILI in the metabolome may help to evidence the individual’s response to drugs (variability in response), where factors such as genetics, environment, and lifestyle are likely involved in developing individual metabolic DILI patterns, thus providing better understanding of the different individuals responses to bioactive compounds. Additionally, this study assessed the different metabolomic profiles among DILI patients exposed to the same drug, evidencing the occurrence of variability in the observed DILI phenotypes. Furthermore, the results showed that metabolomic analysis enabled the monitoring of transitions between sub-phenotypes during disease progression, and identified a spectrum of residual DILI metabolic features which can be overlooked using standard clinical diagnosis during patient follow-up. Altogether, these findings underscore the value of metabolomics for better characterization and monitoring of DILI events over time.
3. Discussion
The patients’ serum ALT and ALP values are used for the calculation of the R score, a widely used parameter for the classification of DILI phenotypes as hepatocellular, cholestatic, or mixed type. However, this classification, which is essentially based on the relative ratio of hepatocyte’s transaminase ALT over hepatocyte-cholangiocyte’s phosphatase alkaline ALP, lacks precision in appreciating subtle differences within intermediate mixed types of DILI and the magnitude, extent, and the DILI progression. Some limitations arise particularly in cases involving specific toxicity mechanisms [
27]. For instance, hepatocellular DILI resulting from early-stage inhibition of the mitochondrial respiratory chain may not be reflected in elevated ALT or ALP values [
28]. Moreover, ALT and AST are not specific to the liver but also to muscle and cardiac damage [
29,
30]. Moreover, ALT, ALP, and AST are not specific to the etiology of liver damage, and baseline alterations may be present in individuals with prior liver diseases [
31]. Mixed-type DILI introduces uncertainty, as liver enzyme levels may not reliably correlate with histological patterns [
32]. Elevated ALT levels during treatment with potentially hepatotoxic drugs may normalize despite ongoing cellular dysfunction [
31]. Finally, the extended half-life of transaminases poses challenges for dynamic monitoring, and the nature of toxic liver injury can evolve over the course of the illness, with specific drugs not consistently associated with distinct damage patterns [
33]. Given the liver’s crucial role in regulating the body’s metabolic processes that can be disrupted by the interfering effects of a drug, it was reasonable to assume that the effects of a hepatotoxic drug causing DILI would be reflected in changes within the cell’s metabolome and exo-metabolome. Therefore, studying the impact on the metabolome provides a more comprehensive insight into DILI outcomes, revealing the intricate individual responses to drugs, and uncovering DILI variability which could be missed by more conventional scores. For that purpose, a cohort study involving 79 DILI patients, assessed by the updated RUCAM, treated with 31 different drugs was recruited for the analysis of the variation of their plasma metabolome during the DILI event after diagnosis [
16]. Several metabolites including free and conjugated bile acids and glycerophospholipids were determinant for accurate classification of the DILI sub-phenotypes provided by an ensemble of PLS–DA models. In the present work, we explored the feasibility of metabolomics analysis to evidence phenotype variability among patients experiencing a DILI event caused by the same drug. This approach allowed the identification of a potential sub-classification of recovered patients into two groups, those who have achieved complete recovery and those who continue to display concerns associated with persistent DILI features; and it permitted us to analyze the different progression patterns of each patient after DILI diagnosis.
Among the 31 different drugs evaluated, we identified two different drug categories. The first category, including epistane, oxaliplatin, and azathioprine, consistently led to the same phenotype in all patients who were exposed. The second category comprised drugs like acetaminophen, amoxicillin-clavulanic, and methotrexate, which exhibited variable and patient-related metabolome features. Despite being exposed to the same drug, patients displayed different DILI sub-phenotypes, sometimes changing along the DILI event, within this category of drugs.
The cholestatic effect of epistane has been attributed to an induced bile acid synthesis that favored bile acid accumulation in hepatocytes at least in part by the androgen receptor activator. Regarding epistane, it was speculated that the significant phenotypic diversity observed in human bile acid synthesis enzymes and transporters offers a potential explanation of this idiosyncratic occurrence [
17].
Regarding oxaliplatin, it is a drug potentially causing adverse episodes of hepatotoxicity with cholestasis being a documented event. This has been associated with mild-to-moderate histological changes in the liver by sinusoidal dilation, congestion, and sinusoidal obstruction syndrome (SOS). This obstruction can result in congestion and damage to liver cells, and it can compromise the normal flow of bile, causing cholestasis [
34,
35]. According to the metabolomic analysis, cholestasis was the predominant and consistent phenotype detected in all studied cases from our cohort.
Azathioprine hepatic injury typically resolves promptly upon discontinuation of the medication. It is characterized for its tendency to temporarily elevate liver enzyme levels and potentially trigger cholestasis with subsequent development of hepatic ductopenia [
21,
36]. Indeed, the findings in our study were consistent in azathioprine inducing cholestatic phenotype in all patients from our cohort.
Although acetaminophen has generally been used as an example of intrinsic DILI, where liver toxicity (necrosis) is induced in a predictable and dose-related necrosis, it is also displaying variable DILI responses. While acetaminophen toxicity by overdose is primarily associated with hepatocellular injury, variable responses of DILI were observed, including a cholestatic sub-phenotype [
37,
38]. This variability in responses is well evidenced in the metabolomics analysis of individuals of our cohort that displayed a range between hepatocellular and cholestatic phenotypes.
Regarding amoxicillin-clavulanic (AC), class I and II HLA (human leukocyte antigens) genotypes have been shown to affect susceptibility to amoxicillin-clavulanic-DILI, indicating the importance of the adaptive immune response in pathogenesis; however, they still have limited utility as predictive or diagnostic biomarkers because of the low positive predictive values [
39]. In the liver damage induced by AC, the liver enzyme elevation pattern is commonly cholestatic, characterized by notable increases in alkaline phosphatase and gamma-glutamyl transpeptidase. However, in some cases, aminotransferase levels are significantly elevated, resulting in a mixed or hepatocellular pattern as well. These diverse phenotypes are also evidenced by the metabolomics analysis in our cohort study where we found patients showing pure cholestatic, hepatocellular or mixed phenotypes. The variability found among these DILI patients may also arise from the combined administration of two potential causal drugs (amoxicillin and clavulanic acid) with different toxicity mechanisms.
The immunosuppressive methotrexate drug is recognized for causing increases in serum aminotransferase levels, but prolonged therapy has also been associated with the development of fatty liver disease, cholestasis, fibrosis, and even cirrhosis [
40,
41]. Accordingly, the metabolomics data analysis from our DILI cohort evidenced dual hepatocellular and cholestatic phenotypes in patients.
Taken together, all of these results demonstrated the ability of metabolomics analysis to evidence the occurrence of variable DILI responses for certain drugs, while in others, the phenotype observed in patients is more homogeneous. The accurate and numerical classification of DILI episodes according to metabolomic data, revealed additional information to the categorization of DILI based on ALT and ALP levels and the R score classification, thus demonstrating that the metabolome analysis of patients can provide a faithful description of the metabolic status and alterations occurring in DILI patients.
Despite the potential relevance and clinical translatability of this study, we are aware of several limitations that, in our opinion, do not invalidate this proof-of-concept exercise. The work relies on a cohort of 79 well documented DILI patients, which comprises 31 different drugs. For some drugs, we had a relatively large number of cases to compare and to assess variability, while for other drugs, the number of DILI cases was quite limited. This might have biased the drug classification regarding the occurrence or not of variability in DILI responses of patients. Further endeavors are still needed to strengthen the clinical significance of the presented model. This would involve the utilization of complementary methods such as lipidomics and quantitative targeted metabolomics approaches, as well as ensuring the model validation is updated in additional DILI cohorts in forthcoming clinical studies.
To our knowledge, this study reports, for the first time, the use of metabolomics to reveal clear differences in the presentation and evolution of DILI in patients for whom the same drug was identified as the causative agent responsible for the DILI event. In addition, the results obtained indicate the presence of subtle metabolic alterations still linked to DILI in patients clinically classified as recovered. This information might be relevant for better patient follow up at late stages of the disease, as well as for anticipating a potential perpetuation of damage through other mechanisms (i.e., drug-induced autoimmune hepatitis). Another significant outcome of this research is that the predictive model developed allowed us to monitor variations in the class of sub-phenotype during disease progression. Thus, monitoring the metabolome might be an additional informative and helpful procedure to assess the DILI events in clinical practice.
4. Materials and Methods
4.1. Compliance with Ethical Standards
The present study was approved by the Ethics Committee for Biomedical Research of the Instituto de Investigación Sanitaria, Hospital Universitario y Politécnico La Fe (Valencia, Spain) (approval Nr. 2012/0452) and was conducted in accordance with the relevant guidelines, good clinical practices, and legal and ethical regulations. All patients gave written informed consent prior to participating in the clinical study.
4.2. Clinical Study: Patients’ Recruitment
In a study conducted between 2013 and 2018, 79 participating patients after providing written informed consent were referred to the Clinical Hepatotoxicity Unit for further DILI evaluation. The diagnosis of DILI followed international causality criteria as the well-established diagnostic scale RUCAM, considering factors such as clinical history, standard analytical results, chronological relationship, exclusion of alternative causes, use of hepatotoxic drugs, and high scores on causality scales (RUCAM > 6) [
9,
10]. Only episodes classified as highly probable (score 6 or higher) were included in the study.
Experienced clinicians established the diagnosis and classified hepatic damage as hepatocellular, cholestatic, or mixed-type DILI, according to R score. Patients were classified as cholestatic DILI if their ALP levels were ≥147 unit/L and had an R-score < 2; hepatocellular DILI if ALT levels were ≥56 unit/L and had an R-score ≥ 5, mixed DILI if the R-score fell between 2 and 5, being ALT ≥ 56 unit/L and ALP ≥ 147 unit/L, and finally recovered if ALT < 56 unit/L and ALP < 147 unit/L, and no clinical or analytical signs of disease were present. Blood samples were collected during scheduled clinical monitoring visits, and the number of samples varied depending on the follow-up and the duration of the recovery of each patient. A total of 278 plasma samples were collected and subjected to metabolomic analysis. These included 34 samples from patients with pure hepatocellular DILI, 79 samples from cholestatic DILI patients, 54 samples from mixed DILI patients, and 111 samples from patients who had clinically recovered. Patient data, such as gender, age, standard liver function indicators (ALT, gamma-glutamyl transferase (GGT), ALP, total bilirubin, and albumin) and other variables reflecting liver function, were also recorded alongside the collection of samples.
4.3. Standards and Reagents
Liquid chromatography–mass spectrometry (LC–MS) grade acetonitrile (CH3CN) and methanol (CH3OH) were obtained from Scharlau (Barcelona, Spain), and formic acid (HCOOH, ≥95%) from Sigma-Aldrich Química SL (Madrid, Spain). Ultra-pure water was generated employing a Milli-Q Integral Water Purification System from Merck Millipore (Darmstadt, Germany). Internal standards phenylalanine-D5, tryptophan-D5, and caffeine-D9 were purchased from C/D/N Isotopes Inc. (Quebec, QC, Canada).
4.4. Sample Preparation
A flowchart of the methodology applied is shown in
Figure 6. A 100 µL sample of the plasma fraction was thawed at room temperature. Subsequently, 300 µL of cold (4 °C) CH
3OH was added for protein precipitation. The sample was homogenized using a vortex shaker for 10 s and centrifuged at 15,000×
g for 10 min at 4 °C. Next, 300 µL of the supernatant was collected, and the solvent was evaporated under vacuum at 25 °C. The resulting residue was reconstituted in 150 µL of a 1 µM internal standard solution containing phenylalanine-D
5, tryptophan-D
5, and caffeine-D
9 in H
20:CH
3CN (98:2, 0.1%
v/
v HCOOH).
4.5. Metabolomic Analysis
Metabolomics analysis was performed using an Agilent 1290 Infinity UPLC system (Agilent Technologies, Santa Clara, CA, USA) with a Kinetex C18 column (Phenomenex, Torrance, CA, USA). The temperature of the autosampler and column was maintained at 4 °C and 55 °C, respectively, and the injection volume was 4 µL. A gradient elution method was employed at a flow rate of 400 µL/min, starting with 98% mobile phase A (H20, 0.1% v/v HCOOH) for 0.5 min, followed by a linear gradient from 2 to 20% mobile phase A (H20, 0.1% v/v HCOOH) for 0.5 min, followed by a linear gradient from 2 to 20% mobile phase B (CH3CN, 0.1% v/v HCOOH) over 4 min and from 20 to 95% B over 4 min. After holding at 95% B for 1 min, a 0.25 min gradient was used to return to the initial conditions, which were maintained for 2.8 min. The mass spectrometry analysis was performed on an iFunnel QTOF Agilent 6550 spectrometer (Agilent Technologies, Santa Clara, CA, USA) in full scan mode, covering the m/z range of 70 to 1200. To ensure data accuracy, MS spectra recalibration was carried out by introducing reference standards into the source. For metabolite annotation, MS/MS data acquisition was performed with a collision energy of 25 V, and precursor ions were automatically selected in cycles. A specific m/z inclusion range was employed for repeated analysis of the QC samples.
4.6. Peak Table Generation and Batch Effect Correction
Each batch (ESI+/−) was processed individually using the XCMS software 2.7 in R [
42], performing peak detection, integration, deconvolution, and alignment. The centWave method with specific parameters was employed for peak detection, and overlapping peaks were distinguished based on a minimum
m/
z difference. Intensity-weighted
m/
z values were calculated for each feature, and peak integration limits were determined using filtered data. Matching peaks across samples were achieved through the nearest method considering mz-retention time balance. To address missing data, raw data files were reintegrated in the regions of missing peaks. The accuracy of peak integration and alignment was evaluated by comparing automated and manual integration results.
Within-batch effect correction was carried out using the non-parametric QC-SVRC approach with a Radial Basis Function kernel [
43]. Metabolite annotation was performed using MS/MS data and databases like the Human Metabolome Database and METLIN. The analysis included 278 samples and 828 annotated LC-MS features.
4.7. Software and Analysis
The t-test was utilized to evaluate the null hypothesis, examining whether the data from two groups (such as cholestatic DILI versus recovered patients) originated from independent random samples sharing equal means but with unknown and unequal variances. LC-MS features demonstrating t-test FDR-adjusted p values < 0.05 were deemed significantly altered and were selected accordingly.
For multivariate supervised analysis, PLS-DA was performed. Double cross-validation (2CV) with subject-wise cross-validation was used to estimate the out-of-sample prediction error of PLS-DA. Model development excluded patients classified as mixed-type and samples used for the development of the PLS models were excluded from the test sets. Cross-validation (CV) folds included all samples collected from each patient, and the selection of the model complexity (i.e., the number of latent variables, LVs) for each PLS model was based on the classification cross-validation accuracy, including 4 (cholestatic model), 5 (hepatocellular model), and 3 LVs (recovered model). For each PLS model, a VIP score threshold of 1 was used to select the most relevant features that were subsequently used to build the optimized PLS models including 291 (cholestatic model), 286 (hepatocellular model), and 298 features (recovered model). MATLAB R2021a (Mathworks Inc., Natick, MA, USA) and the PLS toolbox 9.0 (Eigenvector Research Inc., Wenatchee, WA, USA) were utilized for PLS-DA using in-house-written scripts. Raw data conversion for metabolite annotation was carried out using ProteoWizard. LipiDex software v1.1 [
44] was employed for metabolite annotation, matching measured MS/MS spectra to an in silico library called LipidBlast.