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Updated: Jun 3, 2026

Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro
Published on: January 31, 2022
Assessment of nonsteroidal anti-inflammatory drug-induced hepatotoxicity
José Ag Agúndez1, María Isabel Lucena, Carmen Martínez
1Department of Pharmacology, Medical School University of Extremadura, Avda. de Elvas s/n, E-06071, Badajoz, Spain. jagundez@unex.es
Introduction:
Liver toxicity related to NSAIDs is of outstanding importance because of the wide use of these drugs. NSAIDs are responsible for roughly 10% of the total of cases of drug-induced hepatotoxicity. The assessment of NSAID-induced hepatotoxicity, presently based on clinical and analytical biomarkers, is critical for early diagnosis and immediate withdrawal of the causing drug.
Areas Covered:
The review presents an overview of current knowledge of the assessments of NSAID-induced hepatotoxicity with emphasis on the causative drugs, the NSAID-specific mechanisms involved, and a summary of genetic and non-genetic risk factors. Additionally, the authors discuss genetic factors which show NSAID-specific risk, namely CYP2C, UGT2B7, GSTM1 and GSTT1, as well as HLA alleles. The paper includes a list of the NSAID 'usual suspects' that cause hepatotoxicity based on the integrated information of drug-induced hepatotoxicity databases.
Expert Opinion:
The ultimate goal of this research is pre-prescription testing. Unfortunately, genetic testing, alone, is not sufficient to predict NSAID-induced hepatotoxicity. The development of genetic biomarkers capable of identifying at-risk individuals will not be complete until we develop the ability to fully characterize patients' phenomes and the phenome-genome interaction in patients with NSAID-induced hepatotoxicity. Additionally, a characterization of the metabolic profile of the causative drug in patients with NSAID-induced hepatotoxicity would add crucial information which is presently disregarded in most studies. The full development of robust biomarkers will require the combination of several disciplines including causal statistics, phenomics, genomics, transcriptomics and metabonomics.
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