Building a developmental toxicity ontology

Baker, Nancy C.; Boobis, Alan R.; Burgoon, Lyle D.; Carney, Edward W.; Currie, Richard A.; Fritsche, Ellen; Knudsen, Thomas B.; Laffont, Madeleine; Piersma, Aldert H.; Poole, Alan; Schneider, Steffen; Daston, George P.

doi:10.1002/bdr2.1189

Cited by 25 publications

(6 citation statements)

References 66 publications

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“…As to practical application of the ontology, coverage of the rate-limiting key events in quantitative alternative assays, combined and extrapolated to the intact human using quantitative in vitro to in vivo extrapolation (QIVIVE) modelling should in principle allow animal-free risk assessment. A current project at RIVM, aiming at a developmental ontology, makes use of chemistry, toxicological as well as fundamental developmental biology data to mechanistically map neural tube closure (Hessel et al, 2018), (Baker et al, 2018). This map will be used to design a computational model for neural tube closure, which will also allow the assessment of adverse effect by chemicals causing critical gene expression changes.…”

Section: Setting the Scene: Evolution Versus Revolution In Innovatingmentioning

confidence: 99%

Workshop on the validation and regulatory acceptance of innovative 3R approaches in regulatory toxicology – Evolution versus revolution

Burgdorf

Piersma

Landsiedel

et al. 2019

Toxicology in Vitro

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Section: Setting the Scene: Evolution Versus Revolution In Innovatingmentioning

confidence: 99%

Workshop on the validation and regulatory acceptance of innovative 3R approaches in regulatory toxicology – Evolution versus revolution

Burgdorf

Piersma

Landsiedel

et al. 2019

Toxicology in Vitro

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“…Detailed information on our current understanding of biology and model systems is accessible through a wide variety of bioinformatics data sources. Consequently, it has been proposed that using appropriate bioinformatic analyses may aid the development of DART IATAs ( Baker et al, 2018 ). Here we report how such an integrated bioinformatic analysis of gene-phenotype, MIE, human protein interactome and cell line mRNA expression data can provide solutions to aid DART IATA building.…”

Section: Introductionmentioning

confidence: 99%

“…This comprehensive set of proteins that participate in MIEs or are the transcriptomic/proteomic KE biomarkers of DART effects we define for this paper as the hypothetical “human DARTable genome” (HDG) by analogy to the “Druggable Genome,” which is a comprehensive subset of genes that meet some specific criteria for potential to be drug targets ( Finan et al, 2017 ). Baker et al (2018) proposed a developmental toxicity ontology that codified the relationships between disparate data types that might be helpful in solving this problem. Indeed, it has been shown that some gene knockouts (KOs) phenocopy adverse events caused by chemicals ( Deaton et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Exploration of the DARTable Genome- a Resource Enabling Data-Driven NAMs for Developmental and Reproductive Toxicity Prediction

2022

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The identification of developmental and reproductive toxicity (DART) is a critical component of toxicological evaluations of chemical safety. Adverse Outcome Pathways (AOPs) provide a framework to describe biological processes leading to a toxic effect and can provide insights in understanding the mechanisms underlying toxicological endpoints and aid the development of new approach methods (NAMs). Integrated approaches to testing and assessment (IATA) can be developed based on AOP knowledge and can serve as pragmatic approaches to chemical hazard characterization using NAMs. However, DART effects remain difficult to predict given the diversity of biological mechanisms operating during ontogenesis and consequently, the considerable number of potential molecular initiating events (MIEs) that might trigger a DART Adverse Outcome (DART AO). Consequently, two challenges that need to be overcome to create an AOP-based DART IATA are having sufficient knowledge of relevant biology and using this knowledge to determine the appropriate selection of cell systems that provide sufficient coverage of that biology. The wealth of modern biological and bioinformatics data can be used to provide this knowledge. Here we demonstrate the utility of bioinformatics analyses to address these questions. We integrated known DART MIEs with gene-developmental phenotype information to curate the hypothetical human DARTable genome (HDG, ∼5 k genes) which represents the comprehensive set of biomarkers for DART. Using network analysis of the human interactome, we show that HDG genes have distinct connectivity compared to other genes. HDG genes have higher node degree with lower neighborhood connectivity, betweenness centralities and average shortest path length. Therefore, HDG is highly connected to itself and to the wider network and not only to their local community. Also, by comparison with the Druggable Genome we show how the HDG can be prioritized to identify potential MIEs based on potential to interact with small molecules. We demonstrate how the HDG in combination with gene expression data can be used to select a panel of relevant cell lines (RD-1, OVCAR-3) for inclusion in an IATA and conclude that bioinformatic analyses can provide the necessary insights and serve as a resource for the development of a screening panel for a DART IATA.

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“…At least 12 assays in the ToxCast/Tox21 portfolio map to molecular targets in the retinoid signaling pathway. A preliminary analysis revealed low-/submicromolar bioactivity on one or more target assays for over 100 structurally diverse ToxCast/Tox21 chemicals (e.g., conazoles, organochlorine pesticides, organotins, retinoids, and pharma compounds) suggesting that they can be used to generate models of the retinoid system and provide predictive toxicological information relevant to developmental disruption ( Baker et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

“…Here, we hypothesize that a predictive model of chemical compounds associated with embryonic skeletal developmental toxicity and ATRA signaling pathway disruption will reliably provide scientifically based principles for regulatory decisions regarding chemical use related to pregnant vertebrates. This study uses NAMs data to: 1) compile data available in vitro ToxCast and Tox21 High-Throughput Screening assays on ATRA signaling and metabolism for n = 374 chemicals; 2) cull information from ToxRefDB and select animal studies on prenatal developmental toxicity for n = 370 ToxCast chemicals with skeletal defects identified; 3) identify the relationships of these chemicals and their association with skeletal defects using literature mining ( Baker et al, 2018 ); 4) systematically organize the in vitro and in vivo findings to provide insight into potential molecular initiating events (MIEs) on the ATRA pathway that may lead to testable AOPs. While providing foundational weight-of-evidence, a lack of three-dimensional, dynamic biological systems limits the applicability of AOPs to systemic problems, ushering in the need for more encompassing NAMs.…”

Section: Introductionmentioning

confidence: 99%

Computational model for fetal skeletal defects potentially linked to disruption of retinoic acid signaling

et al. 2022

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All-trans retinoic acid (ATRA) gradients determine skeletal patterning morphogenesis and can be disrupted by diverse genetic or environmental factors during pregnancy, leading to fetal skeleton defects. Adverse Outcome Pathway (AOP) frameworks for ATRA metabolism, signaling, and homeostasis allow for the development of new approach methods (NAMs) for predictive toxicology with less reliance on animal testing. Here, a data-driven model was constructed to identify chemicals associated with both ATRA pathway bioactivity and prenatal skeletal defects. The phenotype data was culled from ToxRefDB prenatal developmental toxicity studies and produced a list of 363 ToxRefDB chemicals with altered skeletal observations. Defects were classified regionally as cranial, post-cranial axial, appendicular, and other (unspecified) features based on ToxRefDB descriptors. To build a multivariate statistical model, high-throughput screening bioactivity data from >8,070 chemicals in ToxCast/Tox21 across 10 in vitro assays relevant to the retinoid signaling system were evaluated and compared to literature-based candidate reference chemicals in the dataset. There were 48 chemicals identified for effects on both in vivo skeletal defects and in vitro ATRA pathway targets for computational modeling. The list included 28 chemicals with prior evidence of skeletal defects linked to retinoid toxicity and 20 chemicals without prior evidence. The combination of thoracic cage defects and DR5 (direct repeats of 5 nucleotides for RAR/RXR transactivation) disruption was the most frequently occurring phenotypic and target disturbance, respectively. This data model provides valuable AOP elucidation and validates current mechanistic understanding. These findings also shed light on potential avenues for new mechanistic discoveries related to ATRA pathway disruption and associated skeletal dysmorphogenesis due to environmental exposures.

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Building a developmental toxicity ontology

Abstract: This report outlines an approach to construct a developmental toxicity ontology. Such an ontology will facilitate computer-based prediction of substances likely to induce human developmental toxicity.

Cited by 25 publications

References 66 publications

Workshop on the validation and regulatory acceptance of innovative 3R approaches in regulatory toxicology – Evolution versus revolution

Workshop on the validation and regulatory acceptance of innovative 3R approaches in regulatory toxicology – Evolution versus revolution

Exploration of the DARTable Genome- a Resource Enabling Data-Driven NAMs for Developmental and Reproductive Toxicity Prediction

Computational model for fetal skeletal defects potentially linked to disruption of retinoic acid signaling

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