Biomarkers of drug-induced liver injury: a mechanistic perspective through acetaminophen hepatotoxicity

ABSTRACT

Introduction: Liver injury induced by drugs is a serious clinical problem. Many circulating biomarkers for identifying and predicting drug-induced liver injury (DILI) have been proposed.

Areas covered: Biomarkers are mainly predicated on the mechanistic understanding of the underlying DILI, often in the context of acetaminophen overdose. New panels of biomarkers have emerged that are related to recovery/regeneration rather than injury following DILI. We explore the clinical relevance and limitations of these new biomarkers including recent controversies. Extracellular vesicles have also emerged as a promising vector of biomarkers, although the biological role for EVs may limit their clinical usefulness. New technological approaches for biomarker discovery are also explored.

Expert opinion: Recent clinical studies have validated the efficacy of some of these new biomarkers, cytokeratin-18, macrophage colony-stimulating factor receptor, and osteopontin for DILI prognosis. Low prevalence of DILI is an inherent limitation to DILI biomarker development. Furthering mechanistic understanding of DILI and leveraging technological advances (e.g. machine learning/omics) is necessary to improve upon the newest generation of biomarkers. The integration of omics approaches with machine learning has led to novel insights in cancer research and DILI research is poised to leverage these technologies for biomarker discovery and development.

KEYWORDS:

• Biomarker
• drug-induced liver injury
• machine learning
• acetaminophen
• osteopontin
• cytokeratin-18
• macrophage colony-stimulating factor receptor
• extracellular vesicles

Article highlights

• Mechanistic insights into DILI, especially acetaminophen-induced liver injury, has helped identify novel biomarkers that can be broadly classified as relating to the injury phase or to the recovery phase.

• The recent retraction in the letter of support by EMA from further development of acHMGB1/HMGB1, K18, MCSFR, and osteopontin as biomarkers was due to falsified data regarding acHMGB1. However, the other biomarkers have been validated by other groups.

• K18, MCSFR, and osteopontin have emerged as novel biomarkers for DILI prognosis. GLDH and miR-122 are both liver-specific, but the high variability in miR-122 and difficulty in measuring it in a clinical setting has somewhat dampened excitement for miR-122.

• A common acetaminophen metabolite, APAP-CYS, is effective at identifying APAP overdose patients due to a longer half-life than APAP. An immunoassay that is feasible in a clinical setting has been developed and tested.

• Extracellular vesicle (EV)-associated biomarkers may have enhanced stability in the blood compared to their ‘free’ counterparts; however, evidence suggests that EVs are released in DILI to facilitate a biological effect and the kinetics of endogenous EVs remains unexplored.

• The declining costs in gene sequencing and other omics approaches correspond with the exponential increase in machine-learning (ML) approaches that can be used in an untargeted fashion to identify novel biological signatures. ML, as a tool, is becoming much more accessible which will foster interest in leveraging ML for DILI biomarker discovery/development.

Declaration of interest

H. Jaeschke is supported by NIH R01 grant DK102142 and grants from the National Institute of General Medical Sciences (P20 GM103549 and P30 GM118247) of the National Institutes of Health. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.