Literature DB >> 29908304

Pathway-based predictive approaches for non-animal assessment of acute inhalation toxicity.

Amy J Clippinger1, David Allen2, Holger Behrsing3, Kelly A BéruBé4, Michael B Bolger5, Warren Casey6, Michael DeLorme7, Marianna Gaça8, Sean C Gehen9, Kyle Glover10, Patrick Hayden11, Paul Hinderliter12, Jon A Hotchkiss13, Anita Iskandar14, Brian Keyser15, Karsta Luettich14, Lan Ma-Hock16, Anna G Maione11, Patrudu Makena15, Jodie Melbourne17, Lawrence Milchak7, Sheung P Ng18, Alicia Paini19, Kathryn Page20, Grace Patlewicz21, Pilar Prieto19, Hans Raabe3, Emily N Reinke22, Clive Roper23, Jane Rose24, Monita Sharma17, Wayne Spoo15, Peter S Thorne25, Daniel M Wilson13, Annie M Jarabek26.   

Abstract

New approaches are needed to assess the effects of inhaled substances on human health. These approaches will be based on mechanisms of toxicity, an understanding of dosimetry, and the use of in silico modeling and in vitro test methods. In order to accelerate wider implementation of such approaches, development of adverse outcome pathways (AOPs) can help identify and address gaps in our understanding of relevant parameters for model input and mechanisms, and optimize non-animal approaches that can be used to investigate key events of toxicity. This paper describes the AOPs and the toolbox of in vitro and in silico models that can be used to assess the key events leading to toxicity following inhalation exposure. Because the optimal testing strategy will vary depending on the substance of interest, here we present a decision tree approach to identify an appropriate non-animal integrated testing strategy that incorporates consideration of a substance's physicochemical properties, relevant mechanisms of toxicity, and available in silico models and in vitro test methods. This decision tree can facilitate standardization of the testing approaches. Case study examples are presented to provide a basis for proof-of-concept testing to illustrate the utility of non-animal approaches to inform hazard identification and risk assessment of humans exposed to inhaled substances.
Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Entities:  

Keywords:  Acute inhalation toxicity; Adverse outcome pathway; Aggregate exposure pathway; Dosimetry; Ex vivo; In silico; In vitro; Integrated approach to testing and assessment (IATA); Quantitative structure-activity relationships (QSAR); Risk assessment

Mesh:

Year:  2018        PMID: 29908304      PMCID: PMC6760245          DOI: 10.1016/j.tiv.2018.06.009

Source DB:  PubMed          Journal:  Toxicol In Vitro        ISSN: 0887-2333            Impact factor:   3.500


  12 in total

1.  Ozone Responsive Gene Expression as a Model for Describing Repeat Exposure Response Trajectories and Interindividual Toxicodynamic Variability In Vitro.

Authors:  Emma C Bowers; Elizabeth M Martin; Annie M Jarabek; David S Morgan; Hannah J Smith; Lisa A Dailey; Emily R Aungst; David Diaz-Sanchez; Shaun D McCullough
Journal:  Toxicol Sci       Date:  2021-12-28       Impact factor: 4.849

2.  In vitro airway models from mice, rhesus macaques, and humans maintain species differences in xenobiotic metabolism and cellular responses to naphthalene.

Authors:  Jacklyn Kelty; Nataliia Kovalchuk; Eric Uwimana; Lei Yin; Xinxin Ding; Laura Van Winkle
Journal:  Am J Physiol Lung Cell Mol Physiol       Date:  2022-07-19       Impact factor: 6.011

3.  3D Printer Particle Emissions: Translation to Internal Dose in Adults and Children.

Authors:  Peter Byrley; William K Boyes; Kim Rogers; Annie M Jarabek
Journal:  J Aerosol Sci       Date:  2021-05-01       Impact factor: 4.586

4.  In silico approaches in organ toxicity hazard assessment: Current status and future needs for predicting heart, kidney and lung toxicities.

Authors:  Arianna Bassan; Vinicius M Alves; Alexander Amberg; Lennart T Anger; Lisa Beilke; Andreas Bender; Autumn Bernal; Mark T D Cronin; Jui-Hua Hsieh; Candice Johnson; Raymond Kemper; Moiz Mumtaz; Louise Neilson; Manuela Pavan; Amy Pointon; Julia Pletz; Patricia Ruiz; Daniel P Russo; Yogesh Sabnis; Reena Sandhu; Markus Schaefer; Lidiya Stavitskaya; David T Szabo; Jean-Pierre Valentin; David Woolley; Craig Zwickl; Glenn J Myatt
Journal:  Comput Toxicol       Date:  2021-09-13

5.  Combined In Vitro and In Vivo Approaches to Propose a Putative Adverse Outcome Pathway for Acute Lung Inflammation Induced by Nanoparticles: A Study on Carbon Dots.

Authors:  Maud Weiss; Jiahui Fan; Mickaël Claudel; Luc Lebeau; Françoise Pons; Carole Ronzani
Journal:  Nanomaterials (Basel)       Date:  2021-01-13       Impact factor: 5.076

6.  Integration of transcriptome analysis with pathophysiological endpoints to evaluate cigarette smoke toxicity in an in vitro human airway tissue model.

Authors:  Rui Xiong; Yue Wu; Qiangen Wu; Levan Muskhelishvili; Kelly Davis; Priya Tripathi; Ying Chen; Tao Chen; Matthew Bryant; Hans Rosenfeldt; Sheila M Healy; Xuefei Cao
Journal:  Arch Toxicol       Date:  2021-03-03       Impact factor: 5.153

7.  An Adverse Outcome Pathway for Decreased Lung Function Focusing on Mechanisms of Impaired Mucociliary Clearance Following Inhalation Exposure.

Authors:  Karsta Luettich; Monita Sharma; Hasmik Yepiskoposyan; Damien Breheny; Frazer J Lowe
Journal:  Front Toxicol       Date:  2021-12-14

8.  Molecular Image-Based Prediction Models of Nuclear Receptor Agonists and Antagonists Using the DeepSnap-Deep Learning Approach with the Tox21 10K Library.

Authors:  Yasunari Matsuzaka; Yoshihiro Uesawa
Journal:  Molecules       Date:  2020-06-15       Impact factor: 4.411

Review 9.  In silico prediction of toxicity and its applications for chemicals at work.

Authors:  Kyung-Taek Rim
Journal:  Toxicol Environ Health Sci       Date:  2020-05-14

Review 10.  New Approach Methods to Evaluate Health Risks of Air Pollutants: Critical Design Considerations for In Vitro Exposure Testing.

Authors:  Jose Zavala; Anastasia N Freedman; John T Szilagyi; Ilona Jaspers; John F Wambaugh; Mark Higuchi; Julia E Rager
Journal:  Int J Environ Res Public Health       Date:  2020-03-23       Impact factor: 3.390

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.