Workflow for Defining Reference Chemicals for Assessing Performance of In Vitro Assays

Judson, Richard S.; Thomas, Russell S.; Baker, Nancy C.; Simha, Anita; Howey, Xia Meng; Marable, Carmen; Kleinstreuer, Nicole; Houck, Keith A.

doi:10.14573/altex.1809281

Cited by 11 publications

(19 citation statements)

References 39 publications

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“…To predict MIEs from gene expression, we trained binary classifiers using chemical-target labels from RefChemDB [ 16 ] and gene expression profiles from the LINCS L1000 gene expression compendium [ 17 ]. All data analysis was done in the R statistical framework using version 3.6.0.…”

Section: Methodsmentioning

confidence: 99%

“…To train binary classifiers to predict MIEs from gene expression, we leveraged annotations from RefChemDB [ 16 ], a database of interactions between chemicals and proteins distilled via automated curation from 15 different resources, including ChEMBL [ 18 ], the Comparative Toxicogenomics Database [ 19 ], and Drugbank [ 20 ]. We downloaded supplemental materials published in [ 16 ] containing 339,008 chemical protein annotations. MIE annotations were derived by making minor modifications to RefChemDB entries.…”

Section: Methodsmentioning

confidence: 99%

“…Briefly, our approach was to train a binary classifier for each MIE by labeling LINCS L1000 gene expression profiles with MIE labels based on annotations in RefChemDB. We focused on RefChemDB annotations with a support level of at least 5, as previous analyses demonstrated relatively high agreement between RefChemDB annotations and ToxCast assays at or above this support level [ 16 ]. First, we identified suitable MIEs for modeling using two criteria: 1) MIEs must be linked to at least 5 chemicals in RefChemDB by at least 5 literature sources each (support level > = 5) and; 2) the total number of LINCS profiles associated with the set of MIE-linked chemicals in the target cell line must be ≥50.…”

Section: Methodsmentioning

confidence: 99%

“…1 ). We developed a methodology for predicting MIEs in specific cell lines by combining data from RefChemDB [ 16 ], a database of chemical-target interactions compiled from multiple sources, and LINCS [ 17 ], a compendium of in vitro gene expression profiles associated with chemical and genetic perturbagens of specific cell lines. Reasoning that some MIEs may not be well predicted in certain cell types, we utilized a binary classification approach (as opposed to a multiclassification approach), where separate binary classifiers were trained for each MIE, thus allowing classifiers showing poor performance to be easily excluded from the analysis framework.…”

Section: Introductionmentioning

confidence: 99%

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Predicting molecular initiating events using chemical target annotations and gene expression

et al. 2022

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Background The advent of high-throughput transcriptomic screening technologies has resulted in a wealth of publicly available gene expression data associated with chemical treatments. From a regulatory perspective, data sets that cover a large chemical space and contain reference chemicals offer utility for the prediction of molecular initiating events associated with chemical exposure. Here, we integrate data from a large compendium of transcriptomic responses to chemical exposure with a comprehensive database of chemical-protein associations to train binary classifiers that predict mechanism(s) of action from transcriptomic responses. First, we linked reference chemicals present in the LINCS L1000 gene expression data collection to chemical identifiers in RefChemDB, a database of chemical-protein interactions. Next, we trained binary classifiers on MCF7 human breast cancer cell line derived gene expression profiles and chemical-protein labels using six classification algorithms to identify optimal analysis parameters. To validate classifier accuracy, we used holdout data sets, training-excluded reference chemicals, and empirical significance testing of null models derived from permuted chemical-protein associations. To identify classifiers that have variable predicting performance across training data derived from different cellular contexts, we trained a separate set of binary classifiers on the PC3 human prostate cancer cell line. Results We trained classifiers using expression data associated with chemical treatments linked to 51 molecular initiating events. This analysis identified and validated 9 high-performing classifiers with empirical p-values lower than 0.05 and internal accuracies ranging from 0.73 to 0.94 and holdout accuracies of 0.68 to 0.92. High-ranking predictions for training-excluded reference chemicals demonstrating that predictive accuracy extends beyond the set of chemicals used in classifier training. To explore differences in classifier performance as a function of training data cellular context, MCF7-trained classifier accuracies were compared to classifiers trained on the PC3 gene expression data for the same molecular initiating events. Conclusions This methodology can offer insight in prioritizing candidate perturbagens of interest for targeted screens. This approach can also help guide the selection of relevant cellular contexts for screening classes of candidate perturbagens using cell line specific model performance.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting molecular initiating events using chemical target annotations and gene expression

et al. 2022

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“…We used literature mining to identify which of the 93 target genes have known relationships to members of the set of nuclear and other receptors. Literature mining was performed on our database of MeSH annotations extracted from articles in PubMed 92 . The database was queried for the MeSH terms identifying the genes and receptors of interest (run June 2017).…”

Section: Methodsmentioning

confidence: 99%

High-throughput toxicogenomic screening of chemicals in the environment using metabolically competent hepatic cell cultures

et al. 2021

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The ToxCast in vitro screening program has provided concentration-response bioactivity data across more than a thousand assay endpoints for thousands of chemicals found in our environment and commerce. However, most ToxCast screening assays have evaluated individual biological targets in cancer cell lines lacking integrated physiological functionality (such as receptor signaling, metabolism). We evaluated differentiated HepaRGTM cells, a human liver-derived cell model understood to effectively model physiologically relevant hepatic signaling. Expression of 93 gene transcripts was measured by quantitative polymerase chain reaction using Fluidigm 96.96 dynamic arrays in response to 1060 chemicals tested in eight-point concentration-response. A Bayesian framework quantitatively modeled chemical-induced changes in gene expression via six transcription factors including: aryl hydrocarbon receptor, constitutive androstane receptor, pregnane X receptor, farnesoid X receptor, androgen receptor, and peroxisome proliferator-activated receptor alpha. For these chemicals the network model translates transcriptomic data into Bayesian inferences about molecular targets known to activate toxicological adverse outcome pathways. These data also provide new insights into the molecular signaling network of HepaRGTM cell cultures.

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A framework for establishing scientific confidence in new approach methodologies

Zalm¹,

et al. 2022

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Robust and efficient processes are needed to establish scientific confidence in new approach methodologies (NAMs) if they are to be considered for regulatory applications. NAMs need to be fit for purpose, reliable and, for the assessment of human health effects, provide information relevant to human biology. They must also be independently reviewed and transparently communicated. Ideally, NAM developers should communicate with stakeholders such as regulators and industry to identify the question(s), and specified purpose that the NAM is intended to address, and the context in which it will be used. Assessment of the biological relevance of the NAM should focus on its alignment with human biology, mechanistic understanding, and ability to provide information that leads to health protective decisions, rather than solely comparing NAM-based chemical testing results with those from traditional animal test methods. However, when NAM results are compared to historical animal test results, the variability observed within animal test method results should be used to inform performance benchmarks. Building on previous efforts, this paper proposes a framework comprising five essential elements to establish scientific confidence in NAMs for regulatory use: fitness for purpose, human biological relevance, technical characterization, data integrity and transparency, and independent review. Universal uptake of this framework would facilitate the timely development and use of NAMs by the international community. While this paper focuses on NAMs for assessing human health effects of pesticides and industrial chemicals, many of the suggested elements are expected to apply to other types of chemicals and to ecotoxicological effect assessments.

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Workflow for Defining Reference Chemicals for Assessing Performance of In Vitro Assays

Cited by 11 publications

References 39 publications

Predicting molecular initiating events using chemical target annotations and gene expression

Predicting molecular initiating events using chemical target annotations and gene expression

High-throughput toxicogenomic screening of chemicals in the environment using metabolically competent hepatic cell cultures

A framework for establishing scientific confidence in new approach methodologies

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