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Genetic Variations in TXNRD1 as Potential Predictors of Drug-Induced Liver Injury.

ABSTRACT: PURPOSE:Drug-induced liver injury (DILI) is the most common adverse drug reaction; however, it is not easily predicted. We hypothesize that DILI has a common genetic basis. Based on the findings of previous animal studies on toxic hepatitis, we selected the thioredoxin reductase 1 gene (TXNRD1) as a candidate marker of DILI for this genetic association study. METHODS:Records from 118 patients with DILI were extracted from the database of the Adverse Drug Reaction Research Group in South Korea. Causative drugs included antituberculosis drugs (n=68, 57.6%), antibiotics (n=22, 18.6%), antiepileptic drugs (n=7, 5.9%), non-steroidal anti-inflammatory drugs (n=5, 4.2%), and others (n=16, 13.7%). Seven single nucleotide polymorphisms (SNPs) in TXNRD1 (rs10735393, rs4964287, rs4595619, rs10861201, rs11111997, rs4246270, and rs4246271) were scored in 118 DILI patients and in 120 drug-matched controls without liver injury. RESULTS:No differences were found between the frequencies of any of the 7 SNPs in the cases and controls; however, a significant association was found between a TTA haplotype composed of rs10735393, rs4964287, and rs4595619 and DILI using an allele model (odds ratio, 1.79; 95% confidence interval, 1.18-2.73; P=0.008; Bonferroni corrected P=0.024). CONCLUSIONS:These results suggest that genetic variations in TXNRD1 favor the development of DILI, although a larger confirmative study is needed.

SUBMITTER: Kwon JW

PROVIDER: S-EPMC3328729 | biostudies-literature | 2012 May

REPOSITORIES: biostudies-literature

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Genetic Variations in TXNRD1 as Potential Predictors of Drug-Induced Liver Injury.

Kwon Jae-Woo JW Shin Eun-Soon ES Lee Jong-Eun JE Kim Sang-Heon SH Kim Sang-Hoon SH Jee Young-Koo YK Kim Yoon-Keun YK Park Hae-Sim HS Min Kyung-Up KU Park Heung-Woo HW

Allergy, asthma & immunology research 20111110 3

<h4>Purpose</h4>Drug-induced liver injury (DILI) is the most common adverse drug reaction; however, it is not easily predicted. We hypothesize that DILI has a common genetic basis. Based on the findings of previous animal studies on toxic hepatitis, we selected the thioredoxin reductase 1 gene (TXNRD1) as a candidate marker of DILI for this genetic association study.<h4>Methods</h4>Records from 118 patients with DILI were extracted from the database of the Adverse Drug Reaction Research Group in ...[more]

PMID: 22548205

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Project description:A major challenge in drug development is safety and toxicity concerns due to drug side effects. One such side effect, drug-induced liver injury (DILI), is considered a primary factor in regulatory clearance. The Critical Assessment of Massive Data Analysis (CAMDA) 2020 CMap Drug Safety Challenge goal was to develop prediction models based on gene perturbation of six preselected cell-lines (CMap L1000), extended structural information (MOLD2), toxicity data (TOX21), and FDA reporting of adverse events (FAERS). Four types of DILI classes were targeted, including two clinically relevant scores and two control classifications, designed by the CAMDA organizers. The L1000 gene expression data had variable drug coverage across cell lines with only 247 out of 617 drugs in the study measured in all six cell types. We addressed this coverage issue by using Kru-Bor ranked merging to generate a singular drug expression signature across all six cell lines. These merged signatures were then narrowed down to the top and bottom 100, 250, 500, or 1,000 genes most perturbed by drug treatment. These signatures were subject to feature selection using Fisher's exact test to identify genes predictive of DILI status. Models based solely on expression signatures had varying results for clinical DILI subtypes with an accuracy ranging from 0.49 to 0.67 and Matthews Correlation Coefficient (MCC) values ranging from -0.03 to 0.1. Models built using FAERS, MOLD2, and TOX21 also had similar results in predicting clinical DILI scores with accuracy ranging from 0.56 to 0.67 with MCC scores ranging from 0.12 to 0.36. To incorporate these various data types with expression-based models, we utilized soft, hard, and weighted ensemble voting methods using the top three performing models for each DILI classification. These voting models achieved a balanced accuracy up to 0.54 and 0.60 for the clinically relevant DILI subtypes. Overall, from our experiment, traditional machine learning approaches may not be optimal as a classification method for the current data.

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Genetic Variations in TXNRD1 as Potential Predictors of Drug-Induced Liver Injury.

Publications

Genetic Variations in TXNRD1 as Potential Predictors of Drug-Induced Liver Injury.

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