Abstract
Relationships between toxicity and chemical hydrophobicity have been known for nearly 100 years in mammals and fish, typically using the log of the octanol:water partition coefficient (Kow). The current study reassessed the influence of mode of action (MOA) on acute aquatic toxicity-log Kow relationships using a comprehensive database of 617 organic chemicals with curated and standardized acute toxicity data that did not exceed solubility limits, their consensus log Kow values, and weight of evidence-based MOA classifications (including 6 broad and 26 specific MOAs). A total of 166 significant (p < 0.05) log Kow-toxicity models were developed across six taxa groups that included QSARs for 5 of the broad and 13 of the specific MOAs. In this study, we demonstrate that QSARs based on MOAs can significantly increase LC50 prediction accuracy for specific acting chemicals. Prediction accuracy increases when QSARs are built based on highly specific MOAs, rather than broad MOA classifications. Additionally, we demonstrate that building QSAR models with chemicals in specific MOA groupings, rather than broader MOA groups leads to significantly better estimates. We also evaluated the differences between models developed from mass-based (µg/L) and mole-based (µmol/L) toxicity data and demonstrate that both are suitable for QSAR development with no clear trend in greater model accuracy. Overall, the results reveal that, despite high variance in all taxa and MOA groups, specific MOA-based models can improve the accuracy of aquatic toxicity predictions over more general groupings.Please check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary.The affiliations are correct.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Barron MG, Anderson MJ, Lipton J, Dixon DG (1997) Evaluation of critical body residue QSARs for predicting organic chemical toxicity to aquatic organisms. SAR QSAR Environ Res 6(1–2):47–62. https://doi.org/10.1080/10629369708031724
Barron MG, Lilavois CR, Martin TM (2015) MOAtox: A comprehensive mode of action and acute aquatic toxicity database for predictive model development. Aquat Toxicol 161:102–107
Böhme A, Laqua A, Schüürmann G (2016) Chemoavailability of organic electrophiles: impact of hydrophobicity and reactivity on their aquatic excess toxicity. Chem Res Toxicol 29:952–962
Bradbury SP, Russom CL, Ankley GT, Schultz TW, Walker JD (2003) Overview of data and conceptual approaches for derivation of quantitative structure-activity relationships for ecotoxicological effects of organic chemicals. Environ Toxicol Chem 22(8):1789–1798. https://doi.org/10.1897/01-234
Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Tropsha A (2014) QSAR modeling: where have you been? Where are you going to? J Med Chem 57(12):4977–5010. https://doi.org/10.1021/jm4004285
Cronin MT (2017) (Q)SARs to predict environmental toxicities: current status and future needs. Environ Sci Process Impacts 19(3):213–220. https://doi.org/10.1039/c6em00687f
Cronin MT, Dearden JC (1995) QSAR in toxicology. 1. Prediction of aquatic toxicity. Molec Inform 14(1):1–7.
Cronin D, Mark T (2006) The role of hydrophobicity in toxicity prediction. Current Comp - Aided Drug Design 2(4):405–413
Cronin MTD, Schultz TW (2003) Pitfalls in QSAR. J Mol Struct (thoechem) 622(1–2):39–51. https://doi.org/10.1016/s0166-1280(02)00616-4
Dearden JC, Cronin MT, Kaiser KL (2009) How not to develop a quantitative structure-activity or structure-property relationship (QSAR/QSPR). SAR QSAR Environ Res 20(3–4):241–266. https://doi.org/10.1080/10629360902949567
Ellison CM, Madden JC, Cronin MT, Enoch SJ (2015) Investigation of the Verhaar scheme for predicting acute aquatic toxicity: improving predictions obtained from Toxtree ver. 2.6. Chemosphere 139:146–154. https://doi.org/10.1016/j.chemosphere.2015.06.009
Escher BI, Hermens JL (2002) Modes of action in ecotoxicology: their role in body burdens, species sensitivity, QSARs, and mixture effects. Environ Sci Technol 36(20):4201–4217. https://doi.org/10.1021/es015848h
Ferguson J (1939) The use of chemical potentials as indices of toxicity. Proc Roy Soc B127:387–404
Hansch C, Dunn WJ (1972) Linear relationships between lipophilic character and biological activity of drugs. J Pharm Sci 61(1):1–19. https://doi.org/10.1002/jps.2600610102
Hansch C, Leo A, Taft RW (1991) A survey of Hammett substituent constants and resonance and field parameters. Chem Rev 91:165–195
Hendriks AJ, Traas TP, Huijbregts MA (2005) Critical body residues linked to octanol−water partitioning, organism composition, and LC50 QSARs: meta-analysis and model. Environ Sci Technol 39(9):3226–3236
Hilal SH, Karickhoff SW, Carreira LA (2004) Prediction of the Solubility, Activity Coefficient and Liquid/Liquid Partition Coefficient of Organic Compounds. QSAR Comb Sci 23(9):709–720. https://doi.org/10.1002/qsar.200430866
Kar S, Roy K (2010) QSAR modeling of toxicity of diverse organic chemicals to Daphnia magna using 2D and 3D descriptors. J Hazard Mater 177(1–3):344–351. https://doi.org/10.1016/j.jhazmat.2009.12.038
Kienzler A, Barron MG, Belanger SE, Beasley A, Embry MR (2017) Mode of Action (MOA) assignment classifications for ecotoxicology: an evaluation of approaches. Environ Sci Technol 51(17):10203–10211. https://doi.org/10.1021/acs.est.7b02337
Kienzler A, Connors KA, Bonnell M, Barron MG, Beasley A, Inglis CG, Embry MR (2019) Mode of action classifications in the EnviroTox Database: development and implementation of a consensus MOA classification. Environ Toxicol Chem 38(10):2294–2304. https://doi.org/10.1002/etc.4531
Könemann H (1981) Quantitative structure-activity relationships in fish toxicity studies Part 1: relationship for 50 industrial pollutants. Toxicology 19(3):209–221. https://doi.org/10.1016/0300-483x(81)90130-x
Könemann H, Musch A (1981) Quantitative structure-activity relationships in fish toxicity studies Part 2: The influence of pH on the QSAR of chlorophenols. Toxicology 19(3):223–228. https://doi.org/10.1016/0300-483x(81)90131-1
Lee S, Barron MG (2015) Development of 3D-QSAR model for acetylcholinesterase inhibitors using a combination of fingerprint, molecular docking, and structure-based pharmacophore approaches. Toxicol Sci 148(1):60–70. https://doi.org/10.1093/toxsci/kfv160
Levy G, Gucinski SP (1964) Studies on biologic membrane permeation kinetics and acute toxicity of drugs by means of goldfish. J Pharmacol Exp Ther 146(1):80–86
Lipnick RL (1989) Structure-Activity relationships in environmental toxicology and chemistry: Narcosis, electrophile and proelectrophile toxicity mechanisms: Application of SAR and QSAR. Env Tox Chem 8:1–12
Mansouri K, Grulke CM, Judson RS, Williams AJ (2018) OPERA models for predicting physicochemical properties and environmental fate endpoints. J Cheminform 10(1):10. https://doi.org/10.1186/s13321-018-0263-1
Martin TM, Harten P, Venkatapathy R, Das S, Young DM (2008) A hierarchical clustering methodology for the estimation of toxicity. Toxicol Mech Method 18(2–3):251–266. https://doi.org/10.1080/15376510701857353
Martin TM, Young DM, Lilavois CR, Barron MG (2015) Comparison of global and mode of action-based models for aquatic toxicity. SAR QSAR Environ Re 26(3):245–262. https://doi.org/10.1080/1062936X.2015.1018939
Mayo-Bean K, Moran K, Meylan B, Ranslow P (2012) Methodology document for the ecological structure-activity relationship model (ECOSAR) class program. Estimating toxicity of industrial chemicals to aquatic organisms using the ECOSAR (ecological structure activity relationship) class program. U.S. Environmental Protection Agency, Washington DC USA (pp. 46).
McCarty LS (1986) The relationship between aquatic toxicity QSARs and bioconcentration for some organic chemicals. Environ Toxicol Chem 5(12):1071–1080. https://doi.org/10.1002/etc.5620051207
Meyer H (1899) The theory of alcohol anesthesia. Arch Exp Path Pharm 42:109–118
Moore DR, Breton RL, MacDonald DB (2003) A comparison of model performance for six quantitative structure-activity relationship packages that predict acute toxicity to fish. Environ Toxicol Chem 22(8):1799–1809. https://doi.org/10.1897/00-361
Nendza M, Muller M (2007) Discriminating toxicant classes by mode of action: 3. Substructure Indicators SAR QSAR Environ Res 18(1–2):155–168. https://doi.org/10.1080/10629360601054354
Netzeva TI, Pavan M, Worth AP (2008) Review of (Quantitative) Structure-Activity Relationships for Acute Aquatic Toxicity. QSAR Comb Sci 27(1):77–90. https://doi.org/10.1002/qsar.200710099
Raimondo S, Jackson CR, Barron MG (2010) Influence of taxonomic relatedness and chemical mode of action in acute interspecies estimation models for aquatic species. Environ Sci Technol 44(19):7711–7716. https://doi.org/10.1021/es101630b
Russom CL, Bradbury SP, Broderius SJ, Hammermeister DE, Drummond RA (1997) Predicting modes of toxic action from chemical structure: Acute toxicity in the fathead minnow (Pimephales promelas). Environ Toxicol Chem 16(5):948–967. https://doi.org/10.1002/etc.5620160514
Schultz TW, Hewitt M, Netzeva TI, Cronin MTD (2007) Assessing Applicability Domains of Toxicological QSARs: Definition, Confidence in Predicted Values, and the Role of Mechanisms of Action. QSAR Comb Sci 26(2):238–254. https://doi.org/10.1002/qsar.200630020
Sushko I, Novotarskyi S, Korner R, Pandey AK, Rupp M, Teetz W, Tetko IV (2011) Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information. J Comput Aided Mol Des 25(6):533–554. https://doi.org/10.1007/s10822-011-9440-2
Tebes-Stevens C, Patel JM, Koopmans M, Olmstead J, Hilal SH, Pope N, Wolfe, (2018) Demonstration of a consensus approach for the calculation of physicochemical properties required for environmental fate assessments. Chemosphere 194:94–106. https://doi.org/10.1016/j.chemosphere.2017.11.137
Tetko IV, Tanchuk VY (2002) Application of associative neural networks for prediction of lipophilicity in ALOGPS 2.1 program. J Chem Inf Comput Sci 42(5):1136–1145. doi:https://doi.org/10.1021/ci025515j
USEPA (2012) Estimation Programs Interface Suite™ for Microsoft® Windows, v 4.11. . IUnited States Environmental Protection Agency. Washington, DC, USA.
Van Leeuwen CJ, Van Der Zandt PT, Aldenberg T, Verhaar HJ, Hermens JL (1992) Application of QSARs, extrapolation and equilibrium partitioning in aquatic effects assessment. I. Narcotic industrial pollutants. Environ Tox Chem 11(2):267–282.
Veith GD, Call DJ, Brooke LT (1983) Structure–toxicity relationships for the fathead minnow, Pimephales promelas: narcotic industrial chemicals. Canad J Fish Aquat Sci 40(6):743–748. https://doi.org/10.1139/f83-096
von der Ohe PC, Kuhne R, Ebert RU, Altenburger R, Liess M, Schuurmann G (2005) Structural alerts–a new classification model to discriminate excess toxicity from narcotic effect levels of organic compounds in the acute daphnid assay. Chem Res Toxicol 18(3):536–555. https://doi.org/10.1021/tx0497954
Willming MM, Lilavois CR, Barron MG, Raimond S (2016) Acute toxicity prediction to threatened and endangered species using interspecies correlation estimation (ICE) models. Environ Sci Technol 50(19):10700–10707. https://doi.org/10.1021/acs.est.6b03009
Zhang X, Qin W, He J, Wen Y, Su L, Sheng L, Zhao Y (2013) Discrimination of excess toxicity from narcotic effect: comparison of toxicity of class-based organic chemicals to Daphnia magna and Tetrahymena pyriformis. Chemosphere 93(2):397–407. https://doi.org/10.1016/j.chemosphere.2013.05.017
Acknowledgements
This paper is dedicated to Jean Barron who passed away during the development of this manuscript. The authors thank Crystal Lilavois for assistance in dataset development and quality assurance support, and Kellie Fay for a review of a draft of the manuscript and technical assistance. This project was supported in part by an appointment to the Research Participation Program at the Gulf Ecosystem Modeling and Management Division, U.S. Environmental Protection Agency, administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and EPA. The views expressed in this article are those of the authors and do not necessarily reflect the views or policies the U.S. Environmental Protection Agency (EPA). Any mention of trade names, products, or services does not imply endorsement by the U.S. Government or EPA. EPA does not endorse any commercial products, services, or enterprises.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interests. All funding was provided by the U.S. Government as part of the authors official duties.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Lambert, F.N., Vivian, D.N., Raimondo, S. et al. Relationships Between Aquatic Toxicity, Chemical Hydrophobicity, and Mode of Action: Log Kow Revisited. Arch Environ Contam Toxicol 83, 326–338 (2022). https://doi.org/10.1007/s00244-022-00944-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00244-022-00944-5