Literature DB >> 22122743

Is toxicogenomics a more reliable and sensitive biomarker than conventional indicators from rats to predict drug-induced liver injury in humans?

Min Zhang1, Minjun Chen, Weida Tong.   

Abstract

Around 40% of drug-induced liver injury (DILI) cases are not detected in preclinical studies using the conventional indicators. It has been hypothesized that genomic biomarkers will be more sensitive than conventional markers in detecting human hepatotoxicity signals in preclinical studies. For example, it has been hypothesized and demonstrated in some cases that (1) genomic biomarkers from the rat liver can discriminate drug candidates that have a greater or lesser potential to cause DILI in susceptible patients despite no conventional indicators of liver toxicity being observed in preclinical studies, and (2) more sensitive biomarkers for early detection of DILI can be derived from a "subtoxic dose" at which the injury in the liver occurs at the molecular but not the phenotypic level. With a public TGx data set derived from short-term in vivo studies using rats, we divided drugs exhibiting human hepatotoxicity into three groups according to whether elevated alanine aminotransferase (ALT) or total bilirubin (TBL) were observed in the treated rats: (A) The elevation was observed in the treated rats, (B) no elevation was observed for all of the treated rats, and (C) no elevation could be observed at a lower dose and shorter duration but occur when a higher or longer treatment was applied. A control group (D) was comprised of drugs known not to cause human hepatotoxicity and for which no rats exhibited elevated ALT or TBL. We developed classifiers for groups A, B, and C against group D and found that the gene signature from scenario A could achieve 83% accuracy for human hepatotoxicity potential of drugs in a leave-one-compound-out cross-validation process, much higher than scenarios B (average 45%) and C (61%). Furthermore, the signature derived from scenario A exhibited relevance to hepatotoxicity in a pathway-based analysis and performed well on two independent public TGx data sets using different chemical treatments and profiled with different microarray platforms. Our study implied that the human hepatotoxicity potential of a drug can be reasonably assessed using TGx analysis of short-term in vivo studies only if it produces significant elevation of ALT or TBL in the treated rats. The study further revealed that the value of "sensitive" biomarkers derived from scenario C was not promising as expected for DILI assessment using the reported TGx design. The study will facilitate further research to understand the role of genomic biomarkers from rats for assessing human hepatotoxicity.

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Year:  2011        PMID: 22122743     DOI: 10.1021/tx200320e

Source DB:  PubMed          Journal:  Chem Res Toxicol        ISSN: 0893-228X            Impact factor:   3.739


  14 in total

1.  Predicting drug-induced liver injury in human with Naïve Bayes classifier approach.

Authors:  Hui Zhang; Lan Ding; Yi Zou; Shui-Qing Hu; Hai-Guo Huang; Wei-Bao Kong; Ji Zhang
Journal:  J Comput Aided Mol Des       Date:  2016-09-17       Impact factor: 3.686

2.  Integrating Drug's Mode of Action into Quantitative Structure-Activity Relationships for Improved Prediction of Drug-Induced Liver Injury.

Authors:  Leihong Wu; Zhichao Liu; Scott Auerbach; Ruili Huang; Minjun Chen; Kristin McEuen; Joshua Xu; Hong Fang; Weida Tong
Journal:  J Chem Inf Model       Date:  2017-04-10       Impact factor: 4.956

Review 3.  Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays.

Authors:  Yen Sia Low; Alexander Yeugenyevich Sedykh; Ivan Rusyn; Alexander Tropsha
Journal:  Curr Top Med Chem       Date:  2014       Impact factor: 3.295

Review 4.  Development of blood biomarkers for drug-induced liver injury: an evaluation of their potential for risk assessment and diagnostics.

Authors:  David E Amacher; Shelli J Schomaker; Jiri Aubrecht
Journal:  Mol Diagn Ther       Date:  2013-12       Impact factor: 4.074

Review 5.  Advances in Engineered Liver Models for Investigating Drug-Induced Liver Injury.

Authors:  Christine Lin; Salman R Khetani
Journal:  Biomed Res Int       Date:  2016-09-20       Impact factor: 3.411

Review 6.  Enoxaparin-Induced Liver Injury: Case Report and Review of the Literature and FDA Adverse Event Reporting System (FAERS).

Authors:  Katherine J Hahn; Shannon J Morales; James H Lewis
Journal:  Drug Saf Case Rep       Date:  2015-12

Review 7.  Idiosyncratic Drug-Induced Liver Injury (IDILI): Potential Mechanisms and Predictive Assays.

Authors:  Alexander D Roth; Moo-Yeal Lee
Journal:  Biomed Res Int       Date:  2017-01-04       Impact factor: 3.411

8.  Integration of metabolomics and transcriptomics in nanotoxicity studies.

Authors:  Tae Hwan Shin; Da Yeon Lee; Hyeon-Seong Lee; Hyung Jin Park; Moon Suk Jin; Man-Jeong Paik; Balachandran Manavalan; Jung-Soon Mo; Gwang Lee
Journal:  BMB Rep       Date:  2018-01       Impact factor: 4.778

9.  Development of Decision Forest Models for Prediction of Drug-Induced Liver Injury in Humans Using A Large Set of FDA-approved Drugs.

Authors:  Huixiao Hong; Shraddha Thakkar; Minjun Chen; Weida Tong
Journal:  Sci Rep       Date:  2017-12-11       Impact factor: 4.379

Review 10.  Key Challenges and Opportunities Associated with the Use of In Vitro Models to Detect Human DILI: Integrated Risk Assessment and Mitigation Plans.

Authors:  Franck A Atienzar; Eric A Blomme; Minjun Chen; Philip Hewitt; J Gerry Kenna; Gilles Labbe; Frederic Moulin; Francois Pognan; Adrian B Roth; Laura Suter-Dick; Okechukwu Ukairo; Richard J Weaver; Yvonne Will; Donna M Dambach
Journal:  Biomed Res Int       Date:  2016-09-05       Impact factor: 3.411

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