FutureTox III: Bridges for Translation

Juberg, Daland R.; Knudsen, Thomas B.; Sander, Miriam; Beck, Nancy B.; Faustman, Elaine M.; Mendrick, Donna L.; Fowle, John R.; Hartung, Thomas; Tice, Raymond R.; Lemazurier, Emmanuel; Becker, Richard A.; Fitzpatrick, Suzanne; Daston, George P.; Harrill, Alison H.; Hines, Ronald N.; Keller, Douglas A.; Lipscomb, John C.; Watson, David; Bahadori, Tina; Crofton, Kevin M.

doi:10.1093/toxsci/kfw194

Cited by 19 publications

(11 citation statements)

References 37 publications

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“…How will the field deal with this? One approach could be to combine tests in biological networks guided by molecular mechanisms/AOPs (Jaworska et al 2015;Juberg et al 2016), or to use biological information across assays in form of a biological read across (Hartung 2016;Patlewicz and Fitzpatrick 2016;Strickland et al 2016a, b). Another old, but continuously present and topical issue (Judson et al 2016;Leist et al 2010) is the problem of cytotoxicity.…”

Section: Discussionmentioning

confidence: 99%

Combination of multiple neural crest migration assays to identify environmental toxicants from a proof-of-concept chemical library

et al. 2017

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Many in vitro tests have been developed to screen for potential neurotoxicity. However, only few cell function-based tests have been used for comparative screening, and thus experience is scarce on how to confirm and evaluate screening hits. We addressed these questions for the neural crest cell migration test (cMINC). After an initial screen, a hit follow-up strategy was devised. A library of 75 compounds plus internal controls (NTP80-list), assembled by the National Toxicology Program of the USA (NTP) was used. It contained some known classes of (developmental) neurotoxic compounds. The primary screen yielded 23 confirmed hits, which comprised ten flame retardants, seven pesticides and six drug-like compounds. Comparison of concentration-response curves for migration and viability showed that all hits were specific. The extent to which migration was inhibited was 25-90%, and two organochlorine pesticides (DDT, heptachlor) were most efficient. In the second part of this study, (1) the cMINC assay was repeated under conditions that prevent proliferation; (2) a transwell migration assay was used as a different type of migration assay; (3) cells were traced to assess cell speed. Some toxicants had largely varying effects between assays, but each hit was confirmed in at least one additional test. This comparative study allows an estimate on how confidently the primary hits from a cell function-based screen can be considered as toxicants disturbing a key neurodevelopmental process. Testing of the NTP80-list in more assays will be highly interesting to assemble a test battery and to build prediction models for developmental toxicity.

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Section: Discussionmentioning

confidence: 99%

Combination of multiple neural crest migration assays to identify environmental toxicants from a proof-of-concept chemical library

et al. 2017

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“…Moreover, a large number of chemical substances need to be studied to identify the adverse effects on development, metabolic homeostasis, reproduction, cytotoxicity, etc. (Zhu et al, 2014; Bell et al, 2017; Insel et al, 2017; Juberg et al, 2017; Clark and Steger-Hartmann, 2018; Mortensen et al, 2018). Thus, high-throughput (HTP) assays and economical methods are required (Tollefsen et al, 2014; Chen et al, 2015; Wang et al, 2015; Richard et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Optimization of a Deep-Learning Method Based on the Classification of Images Generated by Parameterized Deep Snap a Novel Molecular-Image-Input Technique for Quantitative Structure–Activity Relationship (QSAR) Analysis

Matsuzaka

Uesawa

2019

Front. Bioeng. Biotechnol.

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Numerous chemical compounds are distributed around the world and may affect the homeostasis of the endocrine system by disrupting the normal functions of hormone receptors. Although the risks associated with these compounds have been evaluated by acute toxicity testing in mammalian models, the chronic toxicity of many chemicals remains due to high cost of the compounds and the testing, etc. However, computational approaches may be promising alternatives and reduce these evaluations. Recently, deep learning (DL) has been shown to be promising prediction models with high accuracy for recognition of images, speech, signals, and videos since it greatly benefits from large datasets. Recently, a novel DL-based technique called DeepSnap was developed to conduct QSAR analysis using three-dimensional images of chemical structures. It can be used to predict the potential toxicity of many different chemicals to various receptors without extraction of descriptors. DeepSnap has been shown to have a very high capacity in tests using Tox21 quantitative qHTP datasets. Numerous parameters must be adjusted to use the DeepSnap method but they have not been optimized. In this study, the effects of these parameters on the performance of the DL prediction model were evaluated in terms of the loss in validation as an indicator for evaluating the performance of the DL using the toxicity information in the Tox21 qHTP database. The relations of the parameters of DeepSnap such as (1) number of molecules per SDF split into (2) zoom factor percentage, (3) atom size for van der waals percentage, (4) bond radius, (5) minimum bond distance, and (6) bond tolerance, with the validation loss following quadratic function curves, which suggests that optimal thresholds exist to attain the best performance with these prediction models. Using the parameter values set with the best performance, the prediction model of chemical compounds for CAR agonist was built using 64 images, at 105° angle, with AUC of 0.791. Thus, based on these parameters, the proposed DeepSnap-DL approach will be highly reliable and beneficial to establish models to assess the risk associated with various chemicals.

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“…3 Target-level molecular/cellular data from high-throughput screening (HTS) assays, virtual tissue computer simulation models constructed from extant knowledge of embryology, and MPS technologies form a powerful triumvirate for characterizing potential developmental hazards in drug development and chemical safety. [4][5][6][7][8] This overview of opportunities and challenges in programming MPS models for children's health protection research is focused on novel testing strategies for developmental and reproductive toxicity. It is intended on one hand to the MPS community to highlight challenges and opportunities related to the developmental basis of health and disease, and on the other hand to the developmental biology community to note that validation criteria are being discussed in the MPS community that put these technologies on the horizon.…”

Section: Introductionmentioning

confidence: 99%

Programming microphysiological systems for children’s health protection

Knudsen

Klieforth²,

Slikker

2017

Exp Biol Med (Maywood)

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This 'commentary' summarizes research needs and opportunities for engineered MPS models for developmental and reproductive toxicity testing. Emerging concepts can be taken forward to a virtual tissue modeling framework for assessing chemical (and non-chemical) stressors on human development. These models will advance children's health research, both basic and translational and new ways to evaluate complex embryological and reproductive impacts of drug and chemical exposures to inform safety assessments. AbstractMicrophysiological systems (MPS) and computer simulation models that recapitulate the underlying biology and toxicology of critical developmental transitions are emerging tools for developmental effects assessment of drugs/chemicals. Opportunities and challenges exist for their application to alternative, more public health relevant and efficient chemical toxicity testing methods. This is especially pertinent to children's health research and the evaluation of complex embryological and reproductive impacts of drug/chemical exposure. Scaling these technologies to higher throughput is a key challenge and drives the need for in silico models for quantitative prediction of developmental toxicity to inform safety assessments. One example is cellular agent-based models, constructed from extant embryology, that produce data useful to simulate critical developmental transitions and thereby predict phenotypic consequences of disruption in silico. Biologically inspired MPS models built from human induced pluripotent stem (iPS)-derived cells and synthetic matrices that recapitulate organ-specific physiologies and native tissue architectures are providing exciting new research opportunities to advance the assessment of developmental toxicity and offer the possibility of deriving a full 'human on a chip' system, or a 'Homunculus.'

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FutureTox III: Bridges for Translation

Cited by 19 publications

References 37 publications

Combination of multiple neural crest migration assays to identify environmental toxicants from a proof-of-concept chemical library

Combination of multiple neural crest migration assays to identify environmental toxicants from a proof-of-concept chemical library

Optimization of a Deep-Learning Method Based on the Classification of Images Generated by Parameterized Deep Snap a Novel Molecular-Image-Input Technique for Quantitative Structure–Activity Relationship (QSAR) Analysis

Programming microphysiological systems for children’s health protection

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