scholarly journals Quantitative Transcriptional Biomarkers of Xenobiotic Receptor Activation in Rat Liver for the Early Assessment of Drug Safety Liabilities

2020 ◽  
Vol 175 (1) ◽  
pp. 98-112 ◽  
Author(s):  
Alexei A Podtelezhnikov ◽  
James J Monroe ◽  
Amy G Aslamkhan ◽  
Kara Pearson ◽  
Chunhua Qin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Liver Injury ◽  
Drug Development ◽  
Rat Liver ◽  
Stress Responses ◽  
Receptor Activation ◽  
Linear Modeling ◽  
Early Assessment ◽  
Drug Induced ◽  
Additional Qualification

Abstract The robust transcriptional plasticity of liver mediated through xenobiotic receptors underlies its ability to respond rapidly and effectively to diverse chemical stressors. Thus, drug-induced gene expression changes in liver serve not only as biomarkers of liver injury, but also as mechanistic sentinels of adaptation in metabolism, detoxification, and tissue protection from chemicals. Modern RNA sequencing methods offer an unmatched opportunity to quantitatively monitor these processes in parallel and to contextualize the spectrum of dose-dependent stress, adaptation, protection, and injury responses induced in liver by drug treatments. Using this approach, we profiled the transcriptional changes in rat liver following daily oral administration of 120 different compounds, many of which are known to be associated with clinical risk for drug-induced liver injury by diverse mechanisms. Clustering, correlation, and linear modeling analyses were used to identify and optimize coexpressed gene signatures modulated by drug treatment. Here, we specifically focused on prioritizing 9 key signatures for their pragmatic utility for routine monitoring in initial rat tolerability studies just prior to entering drug development. These signatures are associated with 5 canonical xenobiotic nuclear receptors (AHR, CAR, PXR, PPARα, ER), 3 mediators of reactive metabolite-mediated stress responses (NRF2, NRF1, P53), and 1 liver response following activation of the innate immune response. Comparing paradigm chemical inducers of each receptor to the other compounds surveyed enabled us to identify sets of optimized gene expression panels and associated scoring algorithms proposed as quantitative mechanistic biomarkers with high sensitivity, specificity, and quantitative accuracy. These findings were further qualified using public datasets, Open TG-GATEs and DrugMatrix, and internal development compounds. With broader collaboration and additional qualification, the quantitative toxicogenomic framework described here could inform candidate selection prior to committing to drug development, as well as complement and provide a deeper understanding of the conventional toxicology study endpoints used later in drug development.

scholarly journals Application of a Rat Liver Drug Bioactivation Transcriptional Response Assay Early in Drug Development That Informs Chemically Reactive Metabolite Formation and Potential for Drug-induced Liver Injury

2020 ◽  
Vol 177 (1) ◽  
pp. 281-299 ◽  
Author(s):  
James J Monroe ◽  
Keith Q Tanis ◽  
Alexei A Podtelezhnikov ◽  
Truyen Nguyen ◽  
Sam V Machotka ◽  
...  
Keyword(s):  
Liver Injury ◽  
Rat Liver ◽  
Stress Responses ◽  
Transcriptional Activation ◽  
Cellular Response ◽  
Drug Candidate ◽  
Related Factor ◽  
Nuclear Factor ◽  
Drug Induced ◽  
Drug Induced Liver Injury

Abstract Drug-induced liver injury is a major reason for drug candidate attrition from development, denied commercialization, market withdrawal, and restricted prescribing of pharmaceuticals. The metabolic bioactivation of drugs to chemically reactive metabolites (CRMs) contribute to liver-associated adverse drug reactions in humans that often goes undetected in conventional animal toxicology studies. A challenge for pharmaceutical drug discovery has been reliably selecting drug candidates with a low liability of forming CRM and reduced drug-induced liver injury potential, at projected therapeutic doses, without falsely restricting the development of safe drugs. We have developed an in vivo rat liver transcriptional signature biomarker reflecting the cellular response to drug bioactivation. Measurement of transcriptional activation of integrated nuclear factor erythroid 2-related factor 2 (NRF2)/Kelch-like ECH-associated protein 1 (KEAP1) electrophilic stress, and nuclear factor erythroid 2-related factor 1 (NRF1) proteasomal endoplasmic reticulum (ER) stress responses, is described for discerning estimated clinical doses of drugs with potential for bioactivation-mediated hepatotoxicity. The approach was established using well benchmarked CRM forming test agents from our company. This was subsequently tested using curated lists of commercial drugs and internal compounds, anchored in the clinical experience with human hepatotoxicity, while agnostic to mechanism. Based on results with 116 compounds in short-term rat studies, with consideration of the maximum recommended daily clinical dose, this CRM mechanism-based approach yielded 32% sensitivity and 92% specificity for discriminating safe from hepatotoxic drugs. The approach adds new information for guiding early candidate selection and informs structure activity relationships (SAR) thus enabling lead optimization and mechanistic problem solving. Additional refinement of the model is ongoing. Case examples are provided describing the strengths and limitations of the approach.

scholarly journals Lipidomics Analysis Indicates Disturbed Hepatocellular Lipid Metabolism in Reynoutria multiflora-Induced Idiosyncratic Liver Injury

2020 ◽  
Vol 11 ◽  
Author(s):  
Xiaofang Wu ◽  
Yating Zhang ◽  
Jiaqi Qiu ◽  
Ya Xu ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Lipid Metabolism ◽  
Liver Injury ◽  
Rat Liver ◽  
High Performance ◽  
Phospholipid Metabolism ◽  
Polygonum Multiflorum ◽  
Drug Induced ◽  
Metabolic Characteristics ◽  
Lipid Biomarker ◽  
Q Exactive

The root of Reynoutria multiflora (Thunb.) Moldenke (syn.: Polygonum multiflorum Thunb., HSW) is a distinguished herb that has been popularly used in traditional Chinese medicine (TCM). Evidence of its potential side effect on liver injury has accumulated and received much attention. The objective of this study was to profile the metabolic characteristics of lipids in injured liver of rats induced by HSW and to find out potential lipid biomarkers of toxic consequence. A lipopolysaccharide (LPS)-induced rat model of idiosyncratic drug-induced liver injury (IDILI) was constructed and evident liver injury caused by HSW was confirmed based on the combination of biochemical, morphological, and functional tests. A lipidomics method was developed for the first time to investigate the alteration of lipid metabolism in HSW-induced IDILI rat liver by using ultra-high-performance liquid chromatography/Q-exactive Orbitrap mass spectrometry coupled with multivariate analysis. A total of 202 characterized lipids, including phosphatidylcholine (PC), lysophosphatidylcholine (LPC), phosphatidylethanolamine (PE), lysophosphatidylethanolamine (LPE), sphingomyelin (SM), phosphatidylinositol (PI), lysophosphatidylinositol (LPI), phosphatidylserine (PS), phosphoglycerols (PG), and ceramide (Cer), were compared among groups of LPS and LPS + HSW. A total of 14 out 26 LPC, 22 out of 47 PC, 19 out of 29 LPE, 16 out of 36 PE, and 10 out of 15 PI species were increased in HSW-treated rat liver, which indicated that HSW may cause liver damage via interfering the phospholipid metabolism. The present work may assist lipid biomarker development of HSW-induced DILI and it also provide new insights into the relationships between phospholipid perturbation and herbal-induced idiosyncratic DILI.

scholarly journals Evaluation of the relevance of DILI predictive hypotheses in early drug development: review of in vitro methodologies vs. BDDCS classification

2018 ◽  
Vol 7 (3) ◽  
pp. 358-370 ◽  
Author(s):  
Rosa Chan ◽  
Leslie Z. Benet
Keyword(s):  
Liver Injury ◽  
Drug Development ◽  
Safety Concern ◽  
Drug Induced ◽  
Development Review ◽  
Drug Induced Liver Injury ◽  
Underlying Mechanisms ◽  
Early Drug

Drug-induced liver injury (DILI) is a major safety concern; it occurs frequently; it is idiosyncratic; it cannot be adequately predicted; and a multitude of underlying mechanisms has been postulated.

scholarly journals Diverse approaches to predicting drug-induced liver injury using gene-expression profiles

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
G. Rex Sumsion ◽  
Michael S. Bradshaw ◽  
Jeremy T. Beales ◽  
Emi Ford ◽  
Griffin R. G. Caryotakis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Liver Injury ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Drug Induced ◽  
Drug Induced Liver Injury

scholarly journals Aryl Hydrocarbon Receptor Activation by TCDD Modulates Expression of Extracellular Matrix Remodeling Genes during Experimental Liver Fibrosis

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Cheri L. Lamb ◽  
Giovan N. Cholico ◽  
Daniel E. Perkins ◽  
Michael T. Fewkes ◽  
Julia Thom Oxford ◽  
...  
Keyword(s):  
Gene Expression ◽  
Extracellular Matrix ◽  
Liver Injury ◽  
Aryl Hydrocarbon Receptor ◽  
Receptor Activation ◽  
Matrix Remodeling ◽  
Mrna Levels ◽  
Gelatinase Activity ◽  
Ecm Remodeling ◽  
Aryl Hydrocarbon

The aryl hydrocarbon receptor (AhR) is a soluble, ligand-activated transcription factor that mediates the toxicity of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Increasing evidence implicates the AhR in regulating extracellular matrix (ECM) homeostasis. We recently reported that TCDD increased necroinflammation and myofibroblast activation during liver injury elicited by carbon tetrachloride (CCl4). However, TCDD did not increase collagen deposition or exacerbate fibrosis in CCl4-treated mice, which raises the possibility that TCDD may enhance ECM turnover. The goal of this study was to determine how TCDD impacts ECM remodeling gene expression in the liver. Male C57BL/6 mice were treated for 8 weeks with 0.5 mL/kg CCl4, and TCDD (20 μg/kg) was administered during the last two weeks. Results indicate that TCDD increased mRNA levels of procollagen types I, III, IV, and VI and the collagen processing molecules HSP47 and lysyl oxidase. TCDD also increased gelatinase activity and mRNA levels of matrix metalloproteinase- (MMP-) 3, MMP-8, MMP-9, and MMP-13. Furthermore, TCDD modulated expression of genes in the plasminogen activator/plasmin system, which regulates MMP activation, and it also increased TIMP1 gene expression. These findings support the notion that AhR activation by TCDD dysregulates ECM remodeling gene expression and may facilitate ECM metabolism despite increased liver injury.

scholarly journals Utilization of Causal Reasoning of Hepatic Gene Expression in Rats to Identify Molecular Pathways of Idiosyncratic Drug-Induced Liver Injury

2013 ◽  
Vol 137 (1) ◽  
pp. 234-248 ◽  
Author(s):  
Daphna Laifenfeld ◽  
Luping Qiu ◽  
Rachel Swiss ◽  
Jennifer Park ◽  
Michael Macoritto ◽  
...  
Keyword(s):  
Gene Expression ◽  
Liver Injury ◽  
Causal Reasoning ◽  
Molecular Pathways ◽  
Hepatic Gene Expression ◽  
Drug Induced ◽  
Drug Induced Liver Injury

scholarly journals Prediction of Alternative Drug-Induced Liver Injury Classifications Using Molecular Descriptors, Gene Expression Perturbation, and Toxicology Reports

2021 ◽  
Vol 12 ◽  
Author(s):  
Wojciech Lesiński ◽  
Krzysztof Mnich ◽  
Witold R. Rudnicki
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Liver Injury ◽  
Cell Lines ◽  
Predictive Models ◽  
Molecular Descriptors ◽  
Chemical Properties ◽  
Human Cell Lines ◽  
Drug Induced ◽  
Drug Induced Liver Injury

Motivation: Drug-induced liver injury (DILI) is one of the primary problems in drug development. Early prediction of DILI, based on the chemical properties of substances and experiments performed on cell lines, would bring a significant reduction in the cost of clinical trials and faster development of drugs. The current study aims to build predictive models of risk of DILI for chemical compounds using multiple sources of information.Methods: Using several supervised machine learning algorithms, we built predictive models for several alternative splits of compounds between DILI and non-DILI classes. To this end, we used chemical properties of the given compounds, their effects on gene expression levels in six human cell lines treated with them, as well as their toxicological profiles. First, we identified the most informative variables in all data sets. Then, these variables were used to build machine learning models. Finally, composite models were built with the Super Learner approach. All modeling was performed using multiple repeats of cross-validation for unbiased and precise estimates of performance.Results: With one exception, gene expression profiles of human cell lines were non-informative and resulted in random models. Toxicological reports were not useful for prediction of DILI. The best results were obtained for models discerning between harmless compounds and those for which any level of DILI was observed (AUC = 0.75). These models were built with Random Forest algorithm that used molecular descriptors.

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