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Drug-Induced Liver Injury Assessment by Metabolomics: Comparison
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Please note this is a comparison between Version 1 by Marta Moreno-Torres and Version 2 by Jason Zhu.

Drug-induced liver injury (DILI) is one of the most frequent adverse clinical reactions and a relevant cause of morbidity and mortality. Hepatotoxicity is among the major reasons for drug withdrawal during post-market and late development stages, representing a major concern to the pharmaceutical industry. The current biochemical parameters for the detection of DILI are based on enzymes (alanine aminotransferase (ALT), aspartate aminotransferase (AST), gammaglutamyl transpeptidase (GGT), alkaline phosphatase (ALP)) and bilirubin serum levels that are not specific of DILI and therefore there is an increasing interest on novel, specific, DILI biomarkers discovery. Metabolomics has emerged as a tool with a great potential for biomarker discovery, especially in disease diagnosis, and assessment of drug toxicity or efficacy. This review summarizes the multistep approaches in DILI biomarker research and discovery based on metabolomics and the principal outcomes from the research performed in this field.

  • Drug-induced liver injury
  • Diagnosis
  • hepatotoxicity
  • biomarkers
  • metabolomics
  • LC-MS

1. Drug-induced Liver Injury (DILI) and Its Clinical and Pharmaceutical Relevance

DILI is defined as a liver injury caused by pharmaceuticals, herbs, or other xenobiotics, resulting in abnormalities in liver tests or liver dysfunction [1]. DILI due to prescription or over-the-counter drugs is the most common manifestation of drug hepatotoxicity and a growing public health issue [2]. It represents the major reason for liver transplantation and is the leading cause of acute liver failure (ALF) in Europe and the United States. DILI accounts for 7–15% of the ALF cases (acetaminophen overdose excluded). Acetaminophen itself represents over 50% of ALF in adults in the USA, being the drug most frequently involved [3][4][5][6]. Unexpected adverse hepatic reactions to clinical drug treatments have an estimated incidence varying from 2.4 to 19 per 100,000 patients per year [7][8][9]. Its incidence and morbidity are rising in parallel to the introduction of new drugs, the extension of life expectancy, poly-medication in elderly people, and the widespread use of over-the-counter “alternative” medicines including herbal products.
On the other hand, DILI has also a significant relevance in the pharmaceutical industry business. Despite the rigorous steps in the early phases of drug development including the preclinical phase and the clinical trials [10], it is impossible to have complete information and 100% certainty about the safety of a new drug at the time of its approval. Thus, DILI is one of the most frequent and unexpected reasons for drug development discontinuation, drug non-approval, post-marketing regulatory additional actions, and withdrawal from the market [2]. Hence, the assessment and early recognition of hepatotoxicity of new drugs are of great interest to the pharmaceutical industry.

2. Types of DILI and Clinical Features of DILI

There are drugs (intrinsic hepatotoxins) that cause hepatocellular damage by defined mechanisms in a predictable dose-dependent manner and in all individuals exposed. This makes the identification of such compounds easier at the early stages of drug development. Others (idiosyncratic hepatotoxins) cause damage for reasons that are closer to the individual phenotypes rather than to the drug itself, and hence, the event is rare, unpredictable, and not always clearly related to dose, route, or treatment duration. Idiosyncratic hepatotoxicity can be classified into metabolic and immunological categories. In the first case, DILI susceptibility depends on a particular patient’s environmental setting (concomitant disease), individual’s genetic susceptibility, host-related (age, gender, and ethnicity), and drug-related factors (dose, metabolism, and lipophilicity) [11][12]. In the second case, the major player is the activation of the immune system recognizing drug-derived antigens on hepatocytes and triggering a hypersensitivity Type I or IV reaction that ultimately can evolve into an autoimmune disease [12]. Typically, idiosyncratic toxicity occurs with drugs and at doses otherwise well tolerated by the majority of patients [13].
Three patterns of damage are the hallmark for the classification of DILI, namely, hepatocellular, cholestatic, and mixed type, as adopted by the Council for International Organisations of Medical Sciences [14][15]. Hepatocellular DILI is characterised by a liver cell necrosis pattern, release of transaminases ALT and AST and lactate dehydrogenase (LDH), and inflammation, but only mild bile stasis. This hepatocellular pattern is present in acute hepatic necrosis, acute hepatitis, chronic hepatitis, and non-alcoholic steatohepatitis (NASH) [16]. On the other hand, the cholestatic DILI phenotype is characterised by bile production impairment and reflux with bile stasis, which affects ductal cells and the biliary tree with an increase in ALP and GGT in sera. Because of bile flux stasis, other conjugates normally excreted to bile are also increased in serum, (i.e., conjugated bilirubin). The mixed-type DILI shares features of both disease phenotypes and is typical of many drugs and the most frequent pattern of DILI, otherwise rarely occurring in other forms of acute liver disease.

3. The Diagnosis of DILI: Conventional Clinical and Biochemical Biomarkers

Currently, there are no specific biomarkers for the early and conclusive diagnosis and monitoring of DILI. Thus, the pathological examination of a liver biopsy remains the gold standard providing the most conclusive insight into the pathophysiological nature of a drug-induced liver damage event. Yet, it is not free from risks and side effects for the patient and therefore is not always applicable. In practical terms, DILI is diagnosed by excluding other putative liver diseases, and by the application of causality scales that assign a probability degree to an event as being caused by a drug [17]. The temporal relationship between drug intake and the clinical symptoms is determinant in the score provided by causality scales. However, reactions may occur from weeks to months after treatment initiation or even appear once the administration of the drug has been discontinued [18][19]. The situation becomes even more complex in multi- or poly-medicated patients and/or with concomitant comorbidities. In many cases, re-challenging with the drug, whenever acceptable, renders them unique and definitive proof to identify the drug responsible for DILI [20].
To assist in the classification of the different DILI phenotypes, serum enzymes AST, ALT, ALP, GGT and total bilirubin (TB) can be used. ALT levels are more liver-specific than AST, but are not aetiology specific [21][22]. ALP is also not liver-specific and can be considerably elevated in other liver non-related diseases [23]. Regarding bilirubin levels, they only increase when there has been extensive liver damage. Moreover, jaundice runs parallel to cholestasis in most but not in all DILI cases.
In this regard, Hy’s law is an empirical estimation of the severity and risk of suffering an ALF as a consequence of a DILI episode. It states that drug-induced jaundice together with hepatocellular injury, without a significant obstructive component, is an empirical indicator of the probability of suffering ALF. It is based on the evidence that jaundice as expressed by TB in blood, decreased hepatic function, expressed by the International Normalised Ratio of prothrombin time test (INR), and a high ratio of AST or ALT, can predict the risk of developing ALF. ALF is very likely to occur when hepatic injury (ALT/AST > 3 × upper limit of normal (ULN)), jaundice (TB > 2 × ULN), or liver dysfunction (INR > 1.5 × ULN) and in the absence of obstructive cholestasis (ALP < 2 × ULN) and excluded from other disease-induced liver damage.
Based on the ratio of ALT and ALP enzymes and their value over the upper normal levels, clinicians easily classify the various phenotypes of DILI using an R-value defined as [ALT value/ALT upper normal limit (UNL)]/[ALP value/ALP UNL]). DILI is assigned as hepatocellular when R ≥ 5, cholestatic when R ≤ 2, and mixed type if 2 < R < 5 [24]. The R index may display the same value, independently of the absolute magnitudes of ALT and ALP as far as their relative ratio remains constant and does not assist in better estimating the nature of the “mixed-type” DILI phenotype. In general, in the course of DILI, levels of serum liver enzymes do not correlate with the degree of hepatocyte metabolic dysfunction, histological patterns of damage, or severity , rather they increase only upon substantial and massive hepatocyte damage [25]. In addition, mitochondrial respiratory chain inhibition, or mitochondrial dysfunction that often occurs at the early stages of drug-induced injury is not accompanied by elevated ALT or ALP values [26], preventing an early diagnosis of DILI. These drawbacks limit the confident use of the R-score for a precise phenotype diagnosis and progression of DILI.
Several genetic and circulating biomarkers for the estimation of the risk of suffering a DILI event have been proposed by the large-scale initiative of the Safer and Faster Evidence-based Translation (SAFE-T) Consortium in Europe. These include glutamate dehydrogenase (GLDH), glutathione S-transferase, high-mobility group box-1 (HMGB-1), miRNA-122, full length, and caspase-cleaved keratin-18 (K-18) [27][28][29], different HLA alleles [30][31], and sorbitol dehydrogenase (SDH). However, altered levels of these biomarkers are not specific to DILI or liver disease [32].

4. Metabolomics in DILI Research and Diagnosis

4.1. Metabolomics vs. Transcriptomics and Proteomics in DILI Biomarker Discovery

The use of omics techniques for the identification of the biochemical changes caused by hepatotoxic drugs, and hence, uncovering new DILI biomarkers is an active field of research. In the past decade, transcriptomics and proteomics have been widely used for the study of hepatotoxicity [33][34]. These techniques help to visualise effects of hepatotoxins on gene activation and protein expression, but cannot reveal subtle changes in the cell’s metabolism, the end step of any disturbing action elicited by a toxicant. The liver plays a key role in the homeostasis of the whole organism, with many active metabolic processes susceptible to being altered by the noxious effects of a drug. Thus, it is reasonable to assume that the effects of a drug ultimately causing DILI are reflected in changes in the metabolome [26][35]. While genomics and proteomics describe what might be potentially happening, given that metabolites are the end products of the metabolic reaction pathways, metabolite alterations, and the metabolome provide the best comprehensive insight into what is actually happening as a consequence of a noxious stimulus [36]. They better represent the actual metabolic status of a cell or tissue being a relevant source of information to identify the molecular initiating events in the context of the Adverse Outcome Pathway (AOP) approach [37]. Metabolites are commonly present in many cell types, tissues, and organisms and are part of many conserved metabolic pathways; thus, the analysis and translation of results to humans are affordable [38].

Metabolomics also represents an interesting approach regarding toxicity response prediction, based on the identification of preclinical indicators of susceptibility to hepatotoxins that can be anticipated by assessing the basal pattern of endogenous metabolites prior to drug administration [39][40]. Indeed, several studies have evidenced that humans display characteristic and individual metabolic phenotypes or “metabotypes” [41][42][42] which are intended to be used to anticipate the distinct responses to drugs in drug metabolism, and therefore susceptibility to DILI, advocating for individualized drug therapies.

4.2. Metabolites Identified as Putative Biomarkers of DILI in Humans

Considerable advances have recently been made in metabolomics in the discovery of useful biomarkers. Here, it has bween presented review the major outcomes of metabolomics studies performed for DILI evaluation. A literature search was conducted using the terms “metabolomics”, “DILI” and “humans” in the PUBMED, Web of Science, PubTator, and Embase databases. In vitro and animal studies were excluded from the revision. Despite some initial studies being performed with NMR, MS was the prominent methodology used in the vast majority of research since it provides different ionisation techniques, increasing the number of metabolites that can potentially be detected with enormous sensitivity and selectivity. Being an emerging area of research, the number of scientific reports is reduced. Table S1 (Supplementary Material) summarises metabolic alterations associated with DILI and provides a list of metabolites consistently reported between 2009 and 2021 in 25 clinical and experimental studies, considered as emerging circulating DILI biomarkers. Attention was paid principally to their links to the DILI and not to the drug causing the event. Among them, few studies addressed the identification of specific markers for DILI prediction or individual susceptibility determination [39][43][44][45][46] while the vast majority were focused on DILI diagnosis and follow-up [47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66].

Detailed information with the results, outcomes and conclusions of the review can be found at: doi: 10.3390/metabo12060564.

5. Conclusions and Future Perspectives

Great advances have been made in the applicability of MS-metabolomics for the diagnosis of DILI by examining urine, plasma, and serum human samples of patients. These biological samples carry over parts of the hepatocyte exometabolome that allows the identification of DILI-specific biomarkers. These research papers support the great potential of metabolomics as a major source of novel biomarkers for DILI diagnosis and susceptibility, type and severity of the response, and effects caused by a given drug therapy. Altogether, it can contribute to a better understanding of the DILI phenomena and increase safety scrutinization of drugs under development. Several metabolites appeared as statistically significant discriminants in those studies, demonstrating their potential as putative DILI biomarkers; however, up to today, there is not a unanimous agreement on a specific metabolic fingerprint that can be recommended for the routine diagnosis and prognosis of DILI in the clinics. Nevertheless, very promising steps forward have been undertaken and point at future clinical routine use. Inconsistencies among studies can be explained by several factors. First, the different methods applied for metabolomic analysis may influence the type and number of analytes detected and identified. Second, the majority of the studies clustered DILI and non-DILI patients without considering the DILI phenotypes and the causing agent that can belong to a wide range of pharmacological categories and, hence, influence distinct metabolic pathways. Third, the prevailing limitation for clinical verification of a novel biomarker is the requirement to test them in a meaningfully large and well-defined patient population, and here, both the number of individuals included in each cohort study and the number of studies performed so far are still insufficient to provide biomarker candidates with enough statistical discriminative power. Even more, not all studies claiming new biomarkers have proved the sensitivity and specificity of their candidate metabolites as diagnostic biomarkers of DILI. Fourth, the scope of the research differs among the studies. While some of them are focused on the identification of discriminating metabolites between DILI and non-DILI patients, others included distinct grades of DILI severity and even other types of liver diseases which difficults the identification of commonly agreed biomarkers. Therefore, there is still a need for standardised and multicentre studies to address these challenges in the field of DILI metabolomics. All this has prevented up to now, the widespread use of metabolomics-derived biomarkers in routine clinical practice. No less relevant is the fact that data analysis still requires technical expertise and highly experienced personnel. This will probably be made simpler when user-friendly analytical software for data processing will be developed and standardised. Notwithstanding the current limitations, some studies have made very significant advances in the way DILI can be approached from the metabolome analysis, not only to discriminate the phenotype, severity, and evolution of the disease but also to bring new insights to numerically describe the behaviour of a given drug in different patients. An important outcome is that focus should be put on the comparisons of the metabolic pathways altered in DILI rather than on a combination of individual metabolites. The influences of the methodology in biassing data having been retrieved from patients in different moments and with different analysers that tend to be minimised, reaching a comparable diagnosis for a given patient’s sample. The increasing applicability of metabolomics in biomedical research has led to the development of computational and visual tools such as the mummichog algorithm and pathway analysis, as well as tools for the comparison and meta-analysis of metabolomics results, which permit extracting meaningful and more reproducible information in this type of research than with the analysis of individual metabolite levels per se. Given the great possibilities that offer to combine physiological and metabolic pathway information, together with further technological innovation and larger cohorts of patients, the application of metabolomics will provide new findings of early specific markers of diagnostic, treatment, and prognosis of DILI.

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