Literature DB >> 28735337

Thresholds of Toxicological Concern - Setting a threshold for testing below which there is little concern.

Thomas Hartung1,2.   

Abstract

Low dose, low risk; very low dose, no real risk. Setting a pragmatic threshold below which concerns become negligible is the purpose of thresholds of toxicological concern (TTC). The idea is that such threshold values do not need to be established for each and every chemical based on experimental data, but that by analyzing the distribution of lowest or no-effect doses of many chemicals, a TTC can be defined - typically using the 5th percentile of this distribution and lowering it by an uncertainty factor of, e.g., 100. In doing so, TTC aims to compare exposure information (dose) with a threshold below which any hazard manifestation is very unlikely to occur. The history and current developments of this concept are reviewed and the application of TTC for different regulated products and their hazards is discussed. TTC lends itself as a pragmatic filter to deprioritize testing needs whenever real-life exposures are much lower than levels where hazard manifestation would be expected, a situation that is called "negligible exposure" in the REACH legislation, though the TTC concept has not been fully incorporated in its implementation (yet). Other areas and regulations - especially in the food sector and for pharmaceutical impurities - are more proactive. Large, curated databases on toxic effects of chemicals provide us with the opportunity to set TTC for many hazards and substance classes and thus offer a precautionary second tier for risk assessments if hazard cannot be excluded. This allows focusing testing efforts better on relevant exposures to chemicals.

Keywords:  alternative methods; computational toxicology; exposure; risk assessment; toxicity limits

Mesh:

Substances:

Year:  2017        PMID: 28735337     DOI: 10.14573/altex.1707011

Source DB:  PubMed          Journal:  ALTEX        ISSN: 1868-596X            Impact factor:   6.043


  9 in total

Review 1.  Big-data and machine learning to revamp computational toxicology and its use in risk assessment.

Authors:  Thomas Luechtefeld; Craig Rowlands; Thomas Hartung
Journal:  Toxicol Res (Camb)       Date:  2018-05-01       Impact factor: 3.524

Review 2.  Challenges in working towards an internal threshold of toxicological concern (iTTC) for use in the safety assessment of cosmetics: Discussions from the Cosmetics Europe iTTC Working Group workshop.

Authors:  Corie A Ellison; Karen L Blackburn; Paul L Carmichael; Harvey J Clewell; Mark T D Cronin; Bertrand Desprez; Sylvia E Escher; Steve S Ferguson; Sébastien Grégoire; Nicola J Hewitt; Heli M Hollnagel; Martina Klaric; Atish Patel; Sabrina Salhi; Andreas Schepky; Barbara G Schmitt; John F Wambaugh; Andrew Worth
Journal:  Regul Toxicol Pharmacol       Date:  2019-01-15       Impact factor: 3.271

3.  Avoiding Regrettable Substitutions: Green Toxicology for Sustainable Chemistry.

Authors:  Alexandra Maertens; Emily Golden; Thomas Hartung
Journal:  ACS Sustain Chem Eng       Date:  2021-06-01       Impact factor: 9.224

4.  3S - Systematic, systemic, and systems biology and toxicology.

Authors:  Lena Smirnova; Nicole Kleinstreuer; Raffaella Corvi; Andre Levchenko; Suzanne C Fitzpatrick; Thomas Hartung
Journal:  ALTEX       Date:  2018       Impact factor: 6.043

5.  Derivation of New Threshold of Toxicological Concern Values for Exposure via Inhalation for Environmentally-Relevant Chemicals.

Authors:  Mark D Nelms; Grace Patlewicz
Journal:  Front Toxicol       Date:  2020-10-16

Review 6.  Probabilistic risk assessment - the keystone for the future of toxicology.

Authors:  Alexandra Maertens; Emily Golden; Thomas H Luechtefeld; Sebastian Hoffmann; Katya Tsaioun; Thomas Hartung
Journal:  ALTEX       Date:  2022       Impact factor: 6.250

7.  VOC emissions from particle filtering half masks - methods, risks and need for further action.

Authors:  Saskia Kerkeling; Christian Sandten; Thomas Schupp; Martin Kreyenschmidt
Journal:  EXCLI J       Date:  2021-06-01       Impact factor: 4.068

8.  Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility.

Authors:  Thomas Luechtefeld; Dan Marsh; Craig Rowlands; Thomas Hartung
Journal:  Toxicol Sci       Date:  2018-09-01       Impact factor: 4.849

9.  Defining the Human-Biota Thresholds of Toxicological Concern for Organic Chemicals in Freshwater: The Proposed Strategy of the LIFE VERMEER Project Using VEGA Tools.

Authors:  Diego Baderna; Roberta Faoro; Gianluca Selvestrel; Adrien Troise; Davide Luciani; Sandrine Andres; Emilio Benfenati
Journal:  Molecules       Date:  2021-03-30       Impact factor: 4.411

  9 in total

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