A computational toxicogenomics approach identifies a list of highly hepatotoxic compounds from a large microarray database

Rueda-Zárate, Héctor A.; Imaz-Rosshandler, Iván; Cárdenas-Ovando, Roberto A.; Castillo-Fernandez, Juan; Noguez, Julieta; Rangel‐Escareño, Claudia

doi:10.1371/journal.pone.0176284

Cited by 23 publications

(20 citation statements)

References 22 publications

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“…Interestingly, Xu et al [11] proposed a deep learning (DL) model that achieved 86.9% classification accuracy in external validation after training on a set of 475 samples. Fewer studies have focused on the of use gene expression signatures for ADR or DILI prediction [12][13][14]. Kohonen and colleagues recently proposed a large-scale data-driven modeling approach to build a predictive toxicogenomics space (PTGS) combining the US Broad Institute Connectivity Map (CMap [15]) and the US National Cancer Institute 60 tumour cell line screen (NCI-60 [16]).…”

Section: Introductionmentioning

confidence: 99%

Predictability of drug-induced liver injury by machine learning

et al. 2020

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Background: Drug-induced liver injury (DILI) is a major concern in drug development, as hepatotoxicity may not be apparent at early stages but can lead to life threatening consequences. The ability to predict DILI from in vitro data would be a crucial advantage. In 2018, the Critical Assessment Massive Data Analysis group proposed the CMap Drug Safety challenge focusing on DILI prediction. Methods and results: The challenge data included Affymetrix GeneChip expression profiles for the two cancer cell lines MCF7 and PC3 treated with 276 drug compounds and empty vehicles. Binary DILI labeling and a recommended train/test split for the development of predictive classification approaches were also provided. We devised three deep learning architectures for DILI prediction on the challenge data and compared them to random forest and multi-layer perceptron classifiers. On a subset of the data and for some of the models we additionally tested several strategies for balancing the two DILI classes and to identify alternative informative train/test splits. All the models were trained with the MAQC data analysis protocol (DAP), i.e., 10x5 cross-validation over the training set. In all the experiments, the classification performance in both cross-validation and external validation gave Matthews correlation coefficient (MCC) values below 0.2. We observed minimal differences between the two cell lines. Notably, deep learning approaches did not give an advantage on the classification performance. Discussion: We extensively tested multiple machine learning approaches for the DILI classification task obtaining poor to mediocre performance. The results suggest that the CMap expression data on the two cell lines MCF7 and PC3 are not sufficient for accurate DILI label prediction. Reviewers: This article was reviewed by Maciej Kandula and Paweł P. Labaj.

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Section: Introductionmentioning

confidence: 99%

Predictability of drug-induced liver injury by machine learning

et al. 2020

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“…TGx has also been used in semi-quantitative risk assessment such as defining points of departure and benchmark dosing ( Yang et al, 2007 ; AbdulHameed et al, 2016 ; Chauhan et al, 2016 ; Dean et al, 2017 ; Farmahin et al, 2017 ; Kawamoto et al, 2017 ). Most widely used application of TGx approaches is to understand the molecular mechanisms of different toxicological endpoints ( Ellinger-Ziegelbauer et al, 2008 ; Blomme et al, 2009 ; Rodrigues et al, 2016 ; Hendrickx et al, 2017 ; Rueda-Zárate et al, 2017 ). More recently, in addition to gene expression profiling, the study of microRNAs ( Wang et al, 2009 ; Yang et al, 2012 ; Ward et al, 2014 ; Liu et al, 2016 ) and long non-coding RNAs (lncRNAs) ( Aigner et al, 2016 ; Dempsey and Cui, 2017 ) are emerging as new technologies to be integrated into this field powered by next-generation sequencing technologies ( Yu et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Transcriptional Responses Reveal Similarities Between Preclinical Rat Liver Testing Systems

et al. 2018

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Toxicogenomics (TGx) is an important tool to gain an enhanced understanding of toxicity at the molecular level. Previously, we developed a pair ranking (PRank) method to assess in vitro to in vivo extrapolation (IVIVE) using toxicogenomic datasets from the Open Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System (TG-GATEs) database. With this method, we investiagted three important questions that were not addressed in our previous study: (1) is a 1-day in vivo short-term assay able to replace the 28-day standard and expensive toxicological assay? (2) are some biological processes more conservative across different preclinical testing systems than others? and (3) do these preclinical testing systems have the similar resolution in differentiating drugs by their therapeutic uses? For question 1, a high similarity was noted (PRank score = 0.90), indicating the potential utility of shorter term in vivo studies to predict outcome in longer term and more expensive in vivo model systems. There was a moderate similarity between rat primary hepatocytes and in vivo repeat-dose studies (PRank score = 0.71) but a low similarity (PRank score = 0.56) between rat primary hepatocytes and in vivo single dose studies. To address question 2, we limited the analysis to gene sets relevant to specific toxicogenomic pathways and we found that pathways such as lipid metabolism were consistently over-represented in all three assay systems. For question 3, all three preclinical assay systems could distinguish compounds from different therapeutic categories. This suggests that any noted differences in assay systems was biological process-dependent and furthermore that all three systems have utility in assessing drug responses within a certain drug class. In conclusion, this comparison of three commonly used rat TGx systems provides useful information in utility and application of TGx assays.

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“…We applied this approach to a toxicogenomics dataset corresponding to 33 compounds (mainly toxic drugs) profiled in rat kidney (RK), rat liver (RL), primary rat and human hepatocytes (RH and HH, respectively), obtained from Open TG-GATEs. This resource was especially fitting our purpose, due to consistent experimental design across the four target systems; TG-GATEs has been recently exploited to find hepatotoxic compounds [54] and to compare rat liver testing systems [55]. We evaluated multiple ways to aggregate these data for the description of drug-induced toxicity in human kidney and liver.…”

Section: Discussionmentioning

confidence: 99%

Literature optimized integration of gene expression for organ-specific evaluation of toxicogenomics datasets

et al. 2019

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The study of drug toxicity in human organs is complicated by their complex inter-relations and by the obvious difficulty to testing drug effects on biologically relevant material. Animal models and human cell cultures offer alternatives for systematic and large-scale profiling of drug effects on gene expression level, as typically found in the so-called toxicogenomics datasets. However, the complexity of these data, which includes variable drug doses, time points, and experimental setups, makes it difficult to choose and integrate the data, and to evaluate the appropriateness of one or another model system to study drug toxicity (of particular drugs) of particular human organs. Here, we define a protocol to integrate drug-wise rankings of gene expression changes in toxicogenomics data, which we apply to the TG-GATEs dataset, to prioritize genes for association to drug toxicity in liver or kidney. Contrast of the results with sets of known human genes associated to drug toxicity in the literature allows to compare different rank aggregation approaches for the task at hand. Collectively, ranks from multiple models point to genes not previously associated to toxicity, notably, the PCNA clamp associated factor (PCLAF), and genes regulated by the master regulator of the antioxidant response NFE2L2, such as NQO1 and SRXN1. In addition, comparing gene ranks from different models allowed us to evaluate striking differences in terms of toxicity-associated genes between human and rat hepatocytes or between rat liver and rat hepatocytes. We interpret these results to point to the different molecular functions associated to organ toxicity that are best described by each model. We conclude that the expected production of toxicogenomics panels with larger numbers of drugs and models, in combination with the ongoing increase of the experimental literature in organ toxicity, will lead to increasingly better associations of genes for organism toxicity.

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A computational toxicogenomics approach identifies a list of highly hepatotoxic compounds from a large microarray database

Cited by 23 publications

References 22 publications

Predictability of drug-induced liver injury by machine learning

Predictability of drug-induced liver injury by machine learning

Transcriptional Responses Reveal Similarities Between Preclinical Rat Liver Testing Systems

Literature optimized integration of gene expression for organ-specific evaluation of toxicogenomics datasets

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