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Updated: Apr 15, 2026

Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro
Published on: January 31, 2022
Pharmacogenetic testing in idiosyncratic drug-induced liver injury: current role in clinical practice
1National Institute for Health Research (NIHR) Nottingham Digestive Diseases Biomedical Research Unit, Nottingham University Hospital NHS Trust and University of Nottingham, Nottingham, UK.
Abstract:
In contrast to the studies that have explored association of genetic variants with other complex traits, those investigating hepatotoxicity have identified risk alleles with substantially higher risk ratios for the susceptibility to drug-induced liver injury (DILI). In addition, a relatively small number of human leukocyte antigen (HLA) alleles have overlapping associations with a variety of adverse reactions including DILI, cutaneous hypersensitivity and drug-induced pancreatitis. However, if used as a test prior to prescription to prevent potential adverse reaction, genotyping would have a very high negative predictive value, yet a low positive predictive value based on the low incidence of DILI. One potential consideration is to treat all relevant HLA genotypes as one panel covering different forms of adverse drug reactions, thereby improving the positive predictive value of the panel and widen its application. The majority of HLA alleles associated with DILI have a very high negative predictive value; therefore, they can be used to rule out hepatotoxicity caused by particular drugs. A high negative predictive value of a genetic test can be used to identify the correct agent underlying DILI when the patient had been exposed to two concomitant medications with a potential to cause DILI. Inclusion of genetic tests in the causality assessment of an event, where DILI is suspected, may improve consistency and precision of causality assessment tools. A recent clinical trial used N-acetyltransferase 2 genotyping to determine the appropriate dose of isoniazid in an anti-tuberculosis therapeutic regimen and demonstrated that pharmacogenetic-based clinical algorithms have the potential to improve efficacy of a drug and to reduce DILI.
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