Development of an Enhanced Mechanistically Driven Mode of Action Classification Scheme for Adverse Effects on Environmental Spec

Maria Sapounidou, David J. Ebbrell, Mark A. Bonnell, Bruno Campos, James W. Firman, Steve Gutsell, Geoff Hodges, Jayne Roberts, and Mark T. D. Cronin*
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Shaping the toxicity profile of chemicals to avoid animal testing

To determine the toxicity of a substance to the environment or human health, standardised toxicity tests using animals can be performed. However, there is increasing international, societal pressure on industry to replace, reduce and refine animal experimentation according to the principles of the ‘3Rs’, first described by Russell and Burch in 1959.1
In some regulatory spheres, such as cosmetics, vertebrate animal testing has been banned altogether and the regulatory pressure to move to alternative testing methods is increasing in others, like REACH. Among the alternatives, the use of in silico methods such as (quantitative) structure-activity relationships ((Q)SARs) and appropriately chosen read-across strategies are powerful tools to predict the toxicity of chemicals without the need for further animal testing.
QSARs are mathematical models relating a toxicological activity to molecular or physico-chemical parameters, or ‘descriptors’, for a group of compounds. Read-across strategies use one or several analogues with available toxicological data, and to consider that these data are also applicable to the target substance.
These approaches all employ the first of the ‘3Rs’:



Franklin BAUER, Melanie DELANNOY, Carole CHARMEAU-GENEVOIS, Paul THOMAS


Speciality Chemicals magazine, p64, May 2019

ECHA dissemination database

European Chemicals Agency

Quantitative Structure-Activity Relationships Project [(Q)SARs]


OECD
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Guidance document on aquatic toxicity testing of difficult substances and mixtures

OECD Series on Testing and Assessment no. 23

Best Practices for QSAR Model Development, Validation, and Exploitation.


Tropsha A (2010)
Molecular Informatics 29, 476-488

The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR Mode


Tropsha A, Gramatica P, Gombar VK (2003)
QSAR & Comb. Sci. 22, 69–77

The physicochemical basis of QSARs for baseline toxicity


Mackay D, Arnot JA, Petkova EP, Wallace KB, Call DJ, Brooke LT, Veith GD (2009)
SAR QSAR Environ Res. 20 (3-4), 393-414

Model validation in aquatic toxicity testing: implications for regulatory practice


McCarty LS (2012)
Regul Toxicol Pharmacol. 63 (3), 353-362

Classifying environmental pollutants. 1. Structure-activity relationships for prediction of aquatic toxicity.

Verhaar HJM, van Leeuwen CJ and Hermens JLM (1992)
Chemosphere 25, 471-491

Classifying environmental pollutants: Part 3. External validation of the classification system


Verhaar HJM, Solbe J, Speksnijder J, van Leeuwen CJ and Hermens JLM (2000)
Chemosphere 40, 875-883

The use of chemical potentials as indices of toxicity


Ferguson J (1939)
Proc. R. Soc. Lond. B. 127, 387-404

A systematic approach for evaluating the quality of experimental toxicological and ecotoxicological data


Klimisch HJ, Andreae M, Tillmann U (1997)
Regul Toxicol Pharmacol. 25 (1), 1-5

Can highly hydrophobic organic substances cause aquatic baseline toxicity and can they contribute to mixture toxicity?


Mayer P, Reichenberg F (2006)
Environ Toxicol Chem. 25 (10), 2639-2644

Prediction of aqueous solubility of organic compounds by the general solubility equation (GSE)


Ran Y, Jain N, Yalkowsky SH
J Chem Inf Comput Sci. 41 (5), 1208-1217

On the Reliability of Calculated Log P-values: Rekker, Hansch/Leo and Suzuki Approach


Rekker RF, ter Laak AM, Mannhold R (1993)
Quant Struct-Act Relat 12,152-157

Enquête : vers la fin de l'expérimentation animale ? (2021)


Carole CHARMEAU interviewée par Eléonore Solé, pour FUTURA planète, Janvier 2021

How not to develop a quantitative structure-activity or structure-property relationship (QSAR/QSPR)


Dearden JC, Cronin MT, Kaiser KL (2009)
SAR QSAR Environ Res. 20 (3-4), 241-266

The log KOW Controversy

Renner (2002)
Environ. Sci. Technol. 36 (21), 410A-413A