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Koval LE, Carberry CK, Kim YH, McDermott E, Hartwell H, Jaspers I, Gilmour MI, Rager JE. Wildfire Variable Toxicity: Identifying Biomass Smoke Exposure Groupings through Transcriptomic Similarity Scoring. Environ Sci Technol 2022; 56:17131-17142. [PMID: 36399130 PMCID: PMC10777820 DOI: 10.1021/acs.est.2c06043] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The prevalence of wildfires continues to grow globally with exposures resulting in increased disease risk. Characterizing these health risks remains difficult due to the wide landscape of exposures that can result from different burn conditions and fuel types. This study tested the hypothesis that biomass smoke exposures from variable fuels and combustion conditions group together based on similar transcriptional response profiles, informing which wildfire-relevant exposures may be considered as a group for health risk evaluations. Mice (female CD-1) were exposed via oropharyngeal aspiration to equal mass biomass smoke condensates produced from flaming or smoldering burns of eucalyptus, peat, pine, pine needles, or red oak species. Lung transcriptomic signatures were used to calculate transcriptomic similarity scores across exposures, which informed exposure groupings. Exposures from flaming peat, flaming eucalyptus, and smoldering eucalyptus induced the greatest responses, with flaming peat grouping with the pro-inflammatory agent lipopolysaccharide. Smoldering red oak and smoldering peat induced the least transcriptomic response. Groupings paralleled pulmonary toxicity markers, though they were better substantiated by higher data dimensionality and resolution provided through -omic-based evaluation. Interestingly, groupings based on smoke chemistry signatures differed from transcriptomic/toxicity-based groupings. Wildfire-relevant exposure groupings yield insights into risk assessment strategies to ultimately protect public health.
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Affiliation(s)
- Lauren E Koval
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina27599, United States
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina27599, United States
| | - Celeste K Carberry
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina27599, United States
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina27599, United States
| | - Yong Ho Kim
- The Center for Environmental Medicine, Asthma and Lung Biology, School of Medicine, The University of North Carolina, Chapel Hill, North Carolina27599, United States
- Public Health and Integrated Toxicology Division, Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, Durham, North Carolina27711, United States
| | - Elena McDermott
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina27599, United States
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina27599, United States
| | - Hadley Hartwell
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina27599, United States
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina27599, United States
| | - Ilona Jaspers
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina27599, United States
- The Center for Environmental Medicine, Asthma and Lung Biology, School of Medicine, The University of North Carolina, Chapel Hill, North Carolina27599, United States
- Curriculum in Toxicology, School of Medicine, University of North Carolina, Chapel Hill, North Carolina27599, United States
- Department of Pediatrics, School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina27599, United States
| | - M Ian Gilmour
- Public Health and Integrated Toxicology Division, Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, Durham, North Carolina27711, United States
| | - Julia E Rager
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina27599, United States
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina27599, United States
- The Center for Environmental Medicine, Asthma and Lung Biology, School of Medicine, The University of North Carolina, Chapel Hill, North Carolina27599, United States
- Curriculum in Toxicology, School of Medicine, University of North Carolina, Chapel Hill, North Carolina27599, United States
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Rehman AU, Zaini DB, Lal B. Predictive ecotoxicological modeling of ionic liquids using QSAR techniques: A mini review. Process Safety Progress 2022. [DOI: 10.1002/prs.12349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Adeel ur Rehman
- Department of Chemical Engineering Universiti Teknologi PETRONAS Bandar Seri Iskandar Perak Malaysia
| | - Dzulkarnain B. Zaini
- Department of Chemical Engineering Universiti Teknologi PETRONAS Bandar Seri Iskandar Perak Malaysia
| | - Bhajan Lal
- Department of Chemical Engineering Universiti Teknologi PETRONAS Bandar Seri Iskandar Perak Malaysia
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3
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Coquerel QRR, Démares F, Geldenhuys WJ, Le Ray AM, Bréard D, Richomme P, Legros C, Norris E, Bloomquist JR. Toxicity and mode of action of the aporphine plant alkaloid liriodenine on the insect GABA receptor. Toxicon 2021; 201:141-147. [PMID: 34474068 DOI: 10.1016/j.toxicon.2021.08.019] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 08/18/2021] [Accepted: 08/23/2021] [Indexed: 11/28/2022]
Abstract
Liriodenine is a biologically active plant alkaloid with multiple effects on mammals, fungi, and bacteria, but has never been evaluated for insecticidal activity. Accordingly, liriodenine was applied topically in ethanolic solutions to adult female Anopheles gambiae, and found to be mildly toxic. Its lethality was synergized in mixtures with dimethyl sulfoxide and piperonyl butoxide. Recordings from the ventral nerve cord of larval Drosophila melanogaster showed that liriodenine was neuroexcitatory and reversed the inhibitory effect of 1 mM GABA at effective concentrations of 20-30 μM. GABA antagonism on the larval nervous system was equally expressed on both susceptible and cyclodiene-resistant rdl preparations. Acutely isolated neurons from Periplaneta americana were studied under patch clamp and inhibition of GABA-induced currents with an IC50 value of about 1 μM were observed. In contrast, bicuculline did not reverse the effects of GABA on cockroach neurons, as expected. In silico molecular models suggested reasonable structural concordance of liriodenine and bicuculline and isosteric hydrogen bond acceptor sites. This study is the first assessing of the toxicology of liriodenine on insects and implicates the GABA receptor as one likely neuronal target, where liriodenine might be considered an active chemical analog of bicuculline.
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Affiliation(s)
- Quentin R R Coquerel
- Entomology & Nematology Department, Emerging Pathogens Institute, University of Florida, P.O. Box 100009, 2055 Mowry Road, Gainesville, FL, 32610, USA.
| | - Fabien Démares
- Entomology & Nematology Department, Emerging Pathogens Institute, University of Florida, P.O. Box 100009, 2055 Mowry Road, Gainesville, FL, 32610, USA.
| | - Werner J Geldenhuys
- Department of Pharmaceutical Sciences, School of Pharmacy, West Virginia University, Morgantown, WV, 26506, USA.
| | - Anne-Marie Le Ray
- SONAS, EA921, UNIV Angers, SFR QUASAV, Faculty of Health Sciences, Dpt Pharmacy, 16 Bd Daviers, 49045, Angers, Cedex 01, France
| | - Dimitri Bréard
- SONAS, EA921, UNIV Angers, SFR QUASAV, Faculty of Health Sciences, Dpt Pharmacy, 16 Bd Daviers, 49045, Angers, Cedex 01, France
| | - Pascal Richomme
- SONAS, EA921, UNIV Angers, SFR QUASAV, Faculty of Health Sciences, Dpt Pharmacy, 16 Bd Daviers, 49045, Angers, Cedex 01, France.
| | - Christian Legros
- CNRS UMR6015, INSERM U1083, Mitochondrial and Cardiovascular Pathophysiology Institute, Angers, France.
| | - Edmund Norris
- United States Department of Agriculture, Center for Medical, Agricultural, and Veterinary Entomology, Gainesville, FL, 32610, USA.
| | - Jeffrey R Bloomquist
- Entomology & Nematology Department, Emerging Pathogens Institute, University of Florida, P.O. Box 100009, 2055 Mowry Road, Gainesville, FL, 32610, USA.
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4
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Paul Friedman K, Gagne M, Loo LH, Karamertzanis P, Netzeva T, Sobanski T, Franzosa JA, Richard AM, Lougee RR, Gissi A, Lee JYJ, Angrish M, Dorne JL, Foster S, Raffaele K, Bahadori T, Gwinn MR, Lambert J, Whelan M, Rasenberg M, Barton-Maclaren T, Thomas RS. Utility of In Vitro Bioactivity as a Lower Bound Estimate of In Vivo Adverse Effect Levels and in Risk-Based Prioritization. Toxicol Sci 2021; 173:202-225. [PMID: 31532525 DOI: 10.1093/toxsci/kfz201] [Citation(s) in RCA: 116] [Impact Index Per Article: 38.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Use of high-throughput, in vitro bioactivity data in setting a point-of-departure (POD) has the potential to accelerate the pace of human health safety evaluation by informing screening-level assessments. The primary objective of this work was to compare PODs based on high-throughput predictions of bioactivity, exposure predictions, and traditional hazard information for 448 chemicals. PODs derived from new approach methodologies (NAMs) were obtained for this comparison using the 50th (PODNAM, 50) and the 95th (PODNAM, 95) percentile credible interval estimates for the steady-state plasma concentration used in in vitro to in vivo extrapolation of administered equivalent doses. Of the 448 substances, 89% had a PODNAM, 95 that was less than the traditional POD (PODtraditional) value. For the 48 substances for which PODtraditional < PODNAM, 95, the PODNAM and PODtraditional were typically within a factor of 10 of each other, and there was an enrichment of chemical structural features associated with organophosphate and carbamate insecticides. When PODtraditional < PODNAM, 95, it did not appear to result from an enrichment of PODtraditional based on a particular study type (eg, developmental, reproductive, and chronic studies). Bioactivity:exposure ratios, useful for identification of substances with potential priority, demonstrated that high-throughput exposure predictions were greater than the PODNAM, 95 for 11 substances. When compared with threshold of toxicological concern (TTC) values, the PODNAM, 95 was greater than the corresponding TTC value 90% of the time. This work demonstrates the feasibility, and continuing challenges, of using in vitro bioactivity as a protective estimate of POD in screening-level assessments via a case study.
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Affiliation(s)
- Katie Paul Friedman
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, 27711
| | - Matthew Gagne
- Healthy Environments and Consumer Safety Branch, Health Canada, Government of Canada, Ottawa, Ontario, Canada, K1A0K9
| | - Lit-Hsin Loo
- Innovations in Food and Chemical Safety Programme and Bioinformatics Institute, Agency for Science, Technology and Research, Singapore, 138671, Singapore
| | - Panagiotis Karamertzanis
- Computational Assessment Unit, European Chemicals Agency, European Chemicals Agency Annankatu 18, P.O. Box 400, FI-00121 Helsinki, Uusimaa, Finland
| | - Tatiana Netzeva
- Computational Assessment Unit, European Chemicals Agency, European Chemicals Agency Annankatu 18, P.O. Box 400, FI-00121 Helsinki, Uusimaa, Finland
| | - Tomasz Sobanski
- Computational Assessment Unit, European Chemicals Agency, European Chemicals Agency Annankatu 18, P.O. Box 400, FI-00121 Helsinki, Uusimaa, Finland
| | - Jill A Franzosa
- National Health and Environmental Effects Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, 27711
| | - Ann M Richard
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, 27711
| | - Ryan R Lougee
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, 27711.,Oak Ridge Institute for Science and Education, U.S. Department of Energy, Oak Ridge, TN 37831, USA
| | - Andrea Gissi
- Computational Assessment Unit, European Chemicals Agency, European Chemicals Agency Annankatu 18, P.O. Box 400, FI-00121 Helsinki, Uusimaa, Finland
| | - Jia-Ying Joey Lee
- Innovations in Food and Chemical Safety Programme and Bioinformatics Institute, Agency for Science, Technology and Research, Singapore, 138671, Singapore
| | - Michelle Angrish
- National Center for Environmental Assessment, Office of Research and Development, US Environmental Protection Agency, Washington, DC, 20004 and Research Triangle Park, NC 27711
| | - Jean Lou Dorne
- Scientific Committee and Emerging Risks Unit Department of Risk Assessment and Scientific Assistance, Via Carlo Magno 1A, 43126 Parma, Italy
| | - Stiven Foster
- Office of Land and Emergency Management, U.S. Environmental Protection Agency, Washington, DC, 20004
| | - Kathleen Raffaele
- Office of Land and Emergency Management, U.S. Environmental Protection Agency, Washington, DC, 20004
| | - Tina Bahadori
- Oak Ridge Institute for Science and Education, U.S. Department of Energy, Oak Ridge, TN 37831, USA
| | - Maureen R Gwinn
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, 27711
| | - Jason Lambert
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, 27711
| | - Maurice Whelan
- European Commission, Joint Research Centre (JRC), Via Enrico Fermi, 2749, I - 21027 Ispra, Italy
| | - Mike Rasenberg
- Computational Assessment Unit, European Chemicals Agency, European Chemicals Agency Annankatu 18, P.O. Box 400, FI-00121 Helsinki, Uusimaa, Finland
| | - Tara Barton-Maclaren
- Healthy Environments and Consumer Safety Branch, Health Canada, Government of Canada, Ottawa, Ontario, Canada, K1A0K9
| | - Russell S Thomas
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, 27711
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Tolosa L, Martínez-Sena T, Schimming JP, Moro E, Escher SE, Ter Braak B, van der Water B, Miranda MA, van Vugt-Lussenburg BMA, Castell JV. The in vitro assessment of the toxicity of volatile, oxidisable, redox-cycling compounds: phenols as an example. Arch Toxicol 2021; 95:2109-21. [PMID: 34032869 DOI: 10.1007/s00204-021-03036-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 03/22/2021] [Indexed: 10/27/2022]
Abstract
Phenols are regarded as highly toxic chemicals. Their effects are difficult to study in in vitro systems because of their ambiguous fate (degradation, auto-oxidation and volatility). In the course of in vitro studies of a series of redox-cycling phenols, we found evidences of cross-contamination in several in vitro high-throughput test systems, in particular by trimethylbenzene-1, 4-diol/trimethylhydroquinone (TMHQ) and 2,6-di-tertbutyl-4-ethylphenol (DTBEP), and investigated in detail the physicochemical basis for such phenomenon and how to prevent it. TMHQ has fast degradation kinetics followed by significant diffusion rates of the resulting quinone to adjacent wells, other degradation products being able to air-diffuse as well. DTBEP showed lower degradation kinetics, but a higher diffusion rate. In both cases the in vitro toxicity was underestimated because of a decrease in concentration, in addition to cross-contamination to neighbouring wells. We identified four degradation products for TMHQ and five for DTBEP indicating that the current effects measured on cells are not only attributable to the parent phenolic compound. To overcome these drawbacks, we investigated in detail the physicochemical changes occurring in the course of the incubation and made use of gas-permeable and non-permeable plastic seals to prevent it. Diffusion was greatly prevented by the use of both plastic seals, as revealed by GC-MS analysis. Gas non-permeable plastic seals, reduced to a minimum compounds diffusion as well oxidation and did not affect the biological performance of cultured cells. Hence, no toxicological cross-contamination was observed in neighbouring wells, thus allowing a more reliable in vitro assessment of phenol-induced toxicity.
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de Boer A, Krul L, Fehr M, Geurts L, Kramer N, Tabernero Urbieta M, van der Harst J, van de Water B, Venema K, Schütte K, Hepburn PA. Animal-free strategies in food safety & nutrition: What are we waiting for? Part I: Food safety. Trends Food Sci Technol 2020. [DOI: 10.1016/j.tifs.2020.10.034] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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7
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Hisaki T, Kaneko MAN, Hirota M, Matsuoka M, Kouzuki H. Integration of read-across and artificial neural network-based QSAR models for predicting systemic toxicity: A case study for valproic acid. J Toxicol Sci 2020; 45:95-108. [PMID: 32062621 DOI: 10.2131/jts.45.95] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
We present a systematic, comprehensive and reproducible weight-of-evidence approach for predicting the no-observed-adverse-effect level (NOAEL) for systemic toxicity by using read-across and quantitative structure-activity relationship (QSAR) models to fill gaps in rat repeated-dose and developmental toxicity data. As a case study, we chose valproic acid, a developmental toxicant in humans and animals. High-quality in vivo oral rat repeated-dose and developmental toxicity data were available for five and nine analogues, respectively, and showed qualitative consistency, especially for developmental toxicity. Similarity between the target and analogues is readily defined computationally, and data uncertainties associated with the similarities in structural, physico-chemical and toxicological properties, including toxicophores, were low. Uncertainty associated with metabolic similarity is low-to-moderate, largely because the approach was limited to in silico prediction to enable systematic and objective data collection. Uncertainty associated with completeness of read-across was reduced by including in vitro and in silico metabolic data and expanding the experimental animal database. Taking the "worst-case" approach, the smallest NOAEL values among the analogs (i.e., 200 and 100 mg/kg/day for repeated-dose and developmental toxicity, respectively) were read-across to valproic acid. Our previous QSAR models predict repeated-dose NOAEL of 148 (males) and 228 (females) mg/kg/day, and developmental toxicity NOAEL of 390 mg/kg/day for valproic acid. Based on read-across and QSAR, the conservatively predicted NOAEL is 148 mg/kg/day for repeated-dose toxicity, and 100 mg/kg/day for developmental toxicity. Experimental values are 341 mg/kg/day and 100 mg/kg/day, respectively. The present approach appears promising for quantitative and qualitative in silico systemic toxicity prediction of untested chemicals.
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Affiliation(s)
- Tomoka Hisaki
- Shiseido Global Innovation Center.,Department of Hygiene and Public Health, Tokyo Women's Medical University
| | | | | | - Masato Matsuoka
- Department of Hygiene and Public Health, Tokyo Women's Medical University
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Hemmerich J, Ecker GF. In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways. WIREs Comput Mol Sci 2020; 10:e1475. [PMID: 35866138 PMCID: PMC9286356 DOI: 10.1002/wcms.1475] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 03/09/2020] [Accepted: 03/10/2020] [Indexed: 12/18/2022]
Abstract
In silico toxicology is an emerging field. It gains increasing importance as research is aiming to decrease the use of animal experiments as suggested in the 3R principles by Russell and Burch. In silico toxicology is a means to identify hazards of compounds before synthesis, and thus in very early stages of drug development. For chemical industries, as well as regulatory agencies it can aid in gap‐filling and guide risk minimization strategies. Techniques such as structural alerts, read‐across, quantitative structure–activity relationship, machine learning, and deep learning allow to use in silico toxicology in many cases, some even when data is scarce. Especially the concept of adverse outcome pathways puts all techniques into a broader context and can elucidate predictions by mechanistic insights. This article is categorized under:Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Chemoinformatics
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Affiliation(s)
- Jennifer Hemmerich
- Department of Pharmaceutical Chemistry University of Vienna Vienna Austria
| | - Gerhard F. Ecker
- Department of Pharmaceutical Chemistry University of Vienna Vienna Austria
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Pham LL, Sheffield TY, Pradeep P, Brown J, Haggard DE, Wambaugh J, Judson RS, Paul Friedman K. Estimating uncertainty in the context of new approach methodologies for potential use in chemical safety evaluation. Current Opinion in Toxicology 2019; 15:40-7. [DOI: 10.1016/j.cotox.2019.04.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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10
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Webster F, Gagné M, Patlewicz G, Pradeep P, Trefiak N, Judson RS, Barton-Maclaren TS. Predicting estrogen receptor activation by a group of substituted phenols: An integrated approach to testing and assessment case study. Regul Toxicol Pharmacol 2019; 106:278-291. [PMID: 31121201 DOI: 10.1016/j.yrtph.2019.05.017] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 05/07/2019] [Accepted: 05/16/2019] [Indexed: 10/26/2022]
Abstract
Traditional approaches for chemical risk assessment cannot keep pace with the number of substances requiring assessment. Thus, in a global effort to expedite and modernize chemical risk assessment, New Approach Methodologies (NAMs) are being explored and developed. Included in this effort is the OECD Integrated Approaches for Testing and Assessment (IATA) program, which provides a forum for OECD member countries to develop and present case studies illustrating the application of NAM in various risk assessment contexts. Here, we present an IATA case study for the prediction of estrogenic potential of three target phenols: 4-tert-butylphenol, 2,4-di-tert-butylphenol and octabenzone. Key features of this IATA include the use of two computational approaches for analogue selection for read-across, data collected from traditional and NAM sources, and a workflow to generate predictions regarding the targets' ability to bind the estrogen receptor (ER). Endocrine disruption can occur when a chemical substance mimics the activity of natural estrogen by binding to the ER and, if potency and exposure are sufficient, alters the function of the endocrine system to cause adverse effects. The data indicated that of the three target substances that were considered herein, 4-tert-butylphenol is a potential endocrine disruptor. Further, this IATA illustrates that the NAM approach explored is health protective when compared to in vivo endpoints traditionally used for human health risk assessment.
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Schultz TW, Richarz AN, Cronin MT. Assessing uncertainty in read-across: Questions to evaluate toxicity predictions based on knowledge gained from case studies. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.comtox.2018.10.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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12
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Helman G, Shah I, Patlewicz G. Extending the Generalised Read-Across approach (GenRA): A systematic analysis of the impact of physicochemical property information on read-across performance. ACTA ACUST UNITED AC 2018; 8:34-50. [PMID: 31667446 DOI: 10.1016/j.comtox.2018.07.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Read-across is a useful data gap filling technique used within category and analogue approaches in regulatory hazard and risk assessment. Recently we developed an algorithmic, approach called Generalised Read-Across (GenRA) (Shah et al., 2016) which makes read-across predictions of toxicity effects using a similarity weighted average of source analogues characterised by their chemical and/or bioactivity descriptors. A default GenRA approach (termed baseline GenRA) relies on identifying 10 source analogues relative to a target substance that are structurally similar based on Morgan chemical fingerprints and computing an activity score to estimate presence or absence of in vivo toxicity. This current study investigated the impact that similarity in bioavailability plays in altering the local neighbourhood of source analogues as well as read-across performance relative to baseline GenRA using physicochemical property information as a surrogate for bioavailability. Two approaches were evaluated: 1) a filtering approach which restricted structurally related analogues based on their physicochemical properties; and 2) a search expansion approach which included additional analogues based on a combined structural and physicochemical similarity index. Filtering minimally improved performance, and was very dependent on the similarity threshold selected. The search expansion approach performed at least as well as the baseline GenRA, and showed up to a 9% improvement in read-across performance for at least 10 of the 50 organs considered. We summarise the overall impact that physicochemical information plays on GenRA performance, illustrate the improvement for a specific case study substance and describe how to select the most appropriate physicochemical similarity threshold to achieve optimal read-across performance depending on the toxicity effect and chemical of interest. The analyses show that physicochemical property information does result in a modest (up to 9% increase) improvement in structural based read-across predictions.
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Affiliation(s)
- George Helman
- Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, Tennessee, USA.,National Center for Computational Toxicology (NCCT), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park (RTP), NC 27711, USA
| | - Imran Shah
- National Center for Computational Toxicology (NCCT), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park (RTP), NC 27711, USA
| | - Grace Patlewicz
- National Center for Computational Toxicology (NCCT), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park (RTP), NC 27711, USA
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Patlewicz G, Wambaugh JF, Felter SP, Simon TW, Becker RA. Utilizing Threshold of Toxicological Concern (TTC) with High Throughput Exposure Predictions (HTE) as a Risk-Based Prioritization Approach for thousands of chemicals. ACTA ACUST UNITED AC 2018; 7:58-67. [PMID: 31338483 DOI: 10.1016/j.comtox.2018.07.002] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Regulatory agencies across the world are facing the challenge of performing risk-based prioritization of thousands of chemicals in commerce. Here, we present an approach using the Threshold of Toxicological Concern (TTC) combined with heuristic high-throughput exposure (HTE) modelling to rank order chemicals for further evaluation. Accordingly, for risk-based prioritization, chemicals with exposures > TTC would be ranked as higher priority for further evaluation whereas substances with exposures < TTC would be ranked as lower priority. An initial proof of concept, using a dataset of 7986 substances with previously modeled median and upper 95% credible interval (UCI) total daily median exposure rates showed fewer than 5% of substances had UCI exposures > the Cramer Class III TTC (1.5 μg/kg-day). We extended the analysis by profiling the same dataset through the TTC workflow published by Kroes et al (2004) which accounts for known exclusions to the TTC as well as structural alerts. UCI exposures were then compared to the appropriate class-specific TTC. None of the substances categorized as Cramer Class I or Cramer Class II exceeded their respective TTC values and no more than 2% of substances categorized as Cramer Class III or acetylcholinesterase inhibitors exceeded their respective TTC values. The modeled UCI exposures for the majority of the 1853 chemicals with genotoxicity structural alerts did exceed the TTC of 0.0025 μg/kg-day, but only 79 substances exceeded this TTC if median exposure values were used. For substances for which UCI exposures exceeded relevant TTC values, we highlight possible approaches for consideration to refine the HTE : TTC approach. Overall, coupling TTC with HTE offers promise as a pragmatic first step in ranking substances as part of a risk-based prioritization approach.
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Affiliation(s)
- Grace Patlewicz
- National Center for Computational Toxicology (NCCT), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park, NC 27711, USA
| | - John F Wambaugh
- National Center for Computational Toxicology (NCCT), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park, NC 27711, USA
| | - Susan P Felter
- Procter & Gamble, Central Product Safety, Mason, OH 45040, USA
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Patlewicz G, Cronin MT, Helman G, Lambert JC, Lizarraga LE, Shah I. Navigating through the minefield of read-across frameworks: A commentary perspective. ACTA ACUST UNITED AC 2018. [DOI: 10.1016/j.comtox.2018.04.002] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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