Abstract
Acute aquatic toxicity is a complex phenomenon. Experimental measurement of endpoints related to the phenomenon is expensive and takes time. However, data on the above endpoints are needed to solve practical tasks of ecology in cooperation with industry and agriculture control. Optimal descriptors calculated with simplified molecular input-line entry system were used to build up quantitative structure–activity relationships for acute toxicity of zebrafish embryo, expressed via negative decimal logarithm of molar concentration of the dose leading to death in 50% organisms (pLC50). The index of ideality of correlation has been used to improve the predictive potential of the model. Mechanistic interpretation of the model in terms of promoters (molecular alerts) of increase or decrease of the endpoint is suggested. The average statistical characteristics of the model for the external validation sets are the following: average correlation coefficient equal to 0.697; and average root mean squared error equal to 0.93. The predictive potential of the model has been confirmed with three random splits into the training and validation sets.
Article Highlights
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The Monte Carlo method is used to build up predictive models for zebrafish toxicity;
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The CORAL software available on the Internet used for corresponding calculations;
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The structural alerts related to the toxicity are suggested;
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The index of ideality of correlation applied to improve CORAL models.
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References
Achary PGR (2014) Simplified molecular input line entry system-based optimal descriptors: QSAR modelling for voltage-gated potassium channel subunit Kv7.2. SAR QSAR Environ Res 25(1):73–90. https://doi.org/10.1080/1062936X.2013.842930
Chakraborty C, Agoramoorthy G (2010) Why zebrafish? Riv Biol 103:25–27
Chakraborty C, Hsu CH, Wen ZH, Lin CS, Agoramoorthy G (2009) Zebrafish: a complete animal model for in vivo drug discovery and development. Curr Drug Metab 10:116–124. https://doi.org/10.2174/138920009787522197
Chakraborty C, Sharma AR, Sharma G, Lee S-S (2016) Zebrafish: a complete animal model to enumerate the nanoparticle toxicity. J Nanobiotechnol 14:65–78. https://doi.org/10.1186/s12951-016-0217-6
Embry MR, Belanger SE, Braunbeck TA, Galay-Burgos M, Halder M, Hinton DE, Léonard MA, Lillicrap A, Norberg-King T, Whale G (2010) The fish embryo toxicity test as an animal alternative method in hazard and risk assessment and scientific research. Aquat Toxicol 97(2):79–87. https://doi.org/10.1016/j.aquatox.2009.12.008
Ghaedi A (2015) Predicting the cytotoxicity of ionic liquids using QSAR model based on SMILES optimal descriptors. J Mol Liq 208:269–279. https://doi.org/10.1016/j.molliq.2015.04.049
Hanigan D, Truong L, Simonich M, Tanguay R, Westerhoff P (2017) Zebrafish embryo toxicity of 15 chlorinated, brominated, and iodinated disinfection by-products. J Environ Sci 58:302–310. https://doi.org/10.1016/j.jes.2017.05.008
Hazlerigg CRE, Tyler CR, Lorenzen K, Wheeler JR, Thorbek P (2014) Population relevance of toxicant mediated changes in sex ratio in fish: an assessment using an individual-based zebrafish (Danio rerio) model. Ecol Model 280:76–88. https://doi.org/10.1016/j.ecolmodel.2013.12.016
I-Kuei Lin L (1989) A concordance correlation coefficient to evaluate reproducibility. Biometrics 45(1):255–268. https://doi.org/10.2307/2532051
Islam MA, Pillay TS (2016) Simplified molecular input line entry system-based descriptors in QSAR modeling for HIV-protease inhibitors. Chemom Intell Lab Syst 153:67–74. https://doi.org/10.1016/j.chemolab.2016.02.008
Kar S, Ghosh S, Leszczynski J (2018) Single or mixture halogenated chemicals? Risk assessment and developmental toxicity prediction on zebrafish embryos based on weighted descriptors approach. Chemosphere 210:588–596. https://doi.org/10.1016/j.chemosphere.2018.07.051
Keddig N, Schubert S, Wosniok W (2015) Optimal test design for binary response data: the example of the fish embryo toxicity test. Environ Sci Eur 27:15. https://doi.org/10.1186/s12302-015-0046-5
Klüver N, Vogs C, Altenburger R, Escher BI, Scholz S (2016) Development of a general baseline toxicity QSAR model for the fish embryo acute toxicity test. Chemosphere 164:164–173. https://doi.org/10.1016/j.chemosphere.2016.08.079
Kumar A, Chauhan S (2017a) Use of the Monte Carlo method for OECD principles-guided QSAR modeling of SIRT1 inhibitors. Arch Pharm. https://doi.org/10.1002/ardp.201600268
Kumar A, Chauhan S (2017b) QSAR differential model for prediction of SIRT1 modulation using Monte Carlo method. Drug Res 67(3):156–162. https://doi.org/10.1055/s-0042-119725
Kumar P, Kumar A, Sindhu J (2019) Design and development of novel focal adhesion kinase (FAK) inhibitors using Monte Carlo method with index of ideality of correlation to validate QSAR. SAR QSAR Environ Res 30(2):63–80. https://doi.org/10.1080/1062936X.2018.1564067
Li Q, Ding X, Si H, Gao H (2014) QSAR model based on SMILES of inhibitory rate of 2,3-diarylpropenoic acids on AKR1C3. Chemom Intell Lab Syst 139:132–138. https://doi.org/10.1016/j.chemolab.2014.09.013
Liu H, Sheng N, Zhang W, Dai J (2015) Toxic effects of perfluorononanoic acid on the development of Zebrafish (Danio rerio) embryos. J Environ Sci 32:26–34. https://doi.org/10.1016/j.jes.2014.11.008
Ojha PK, Mitra I, Das RN, Roy K (2011) Further exploring r 2m metrics for validation of QSPR models. Chemom Intell Lab Syst 107(1):194–205. https://doi.org/10.1016/j.chemolab.2011.03.011
Rescifina A, Floresta G, Marrazzo A, Parenti C, Prezzavento O, Nastasi G, Dichiara M, Amata E (2017) Development of a sigma-2 receptor affinity filter through a Monte Carlo based QSAR analysis. Eur J Pharm Sci 106:94–101. https://doi.org/10.1016/j.ejps.2017.05.061
Sokolović D, Stanković V, Toskić D, Lilić L, Ranković G, Ranković J, Nedin-Ranković G, Veselinović AM (2016) Monte Carlo-based QSAR modeling of dimeric pyridinium compounds and drug design of new potent acetylcholine esterase inhibitors for potential therapy of myasthenia gravis. Struct Chem 27(5):1511–1519. https://doi.org/10.1007/s11224-016-0776-z
Strahle U, Scholz S, Geisler R, Greiner P, Hollert H, Rastegar S, Schumacher A, Selderslaghs I, Weiss C, Witters H, Braunbeck T (2012) Zebrafish embryos as an alternative to animal experiments—a commentary on the definition of the onset of protected life stages in animal welfare regulations. Reprod Toxicol 33:128–132. https://doi.org/10.1016/j.reprotox.2011.06.121
Toropov AA, Toropova AP (2017) The index of ideality of correlation: a criterion of predictive potential of QSPR/QSAR models? Mutat Res Genet Toxicol Environ 819:31–37. https://doi.org/10.1016/j.mrgentox.2017.05.008
Toropov AA, Toropova AP (2019) Use of the index of ideality of correlation to improve predictive potential for biochemical endpoints. Toxicol Mech Methods 29(1):43–52. https://doi.org/10.1080/15376516.2018.1506851
Toropov AA, Toropova AP, Marzo M, Dorne JL, Georgiadis N, Benfenati E (2017) QSAR models for predicting acute toxicity of pesticides in rainbow trout using the CORAL software and EFSA’s OpenFoodTox database. Environ Toxicol Pharmacol 53:158–163. https://doi.org/10.1016/j.etap.2017.05.011
Toropov AA, Carbó-Dorca R, Toropova AP (2018) Index of ideality of correlation: new possibilities to validate QSAR: a case study. Struct Chem 29(1):33–38. https://doi.org/10.1007/s11224-017-0997-9
Toropova MA (2017) Drug metabolism as an object of computational analysis by the Monte Carlo method. Curr Drug Metab 18(12):1123–1131. https://doi.org/10.2174/1389200218666171010124733
Toropova AP, Toropov AA (2017) The index of ideality of correlation: a criterion of predictability of QSAR models for skin permeability? Sci Total Environ 586:466–472. https://doi.org/10.1016/j.scitotenv.2017.01.198
Toropova AP, Toropov AA, Lombardo A, Roncaglioni A, Benfenati E, Gini G (2012) CORAL: QSAR model for acute toxicity in Fathead Minnow (Pimephales promelas). J Comput Chem 33:1218–1223. https://doi.org/10.1002/jcc.22953
Toropova AP, Toropov AA, Benfenati E (2015) CORAL: prediction of binding affinity and efficacy of thyroid hormone receptor ligands. Eur J Med Chem 101:452–461. https://doi.org/10.1016/j.ejmech.2015.07.012
Toropova MA, Raska I Jr, Toropova AP, Raskova M (2017) CORAL software: analysis of impacts of pharmaceutical agents upon metabolism via the optimal descriptors. Curr Drug Metab 18(6):500–510. https://doi.org/10.2174/1389200218666170301105916
Worachartcheewan A, Nantasenamat C, Isarankura-Na-Ayudhya C, Prachayasittikul V (2014) QSAR study of H1N1 neuraminidase inhibitors from influenza a virus. Lett Drug Des Discov 11(4):420–427. https://doi.org/10.2174/15701808113106660085
Zhang L, Zhang XY, Hua Y, Zhang BJ (2018) Safety evaluation of the temporary consolidant based on a zebrafish embryo model. Toxicol Vitro 51:50–53. https://doi.org/10.1016/j.tiv.2018.05.005
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The authors are grateful for the contribution of the Project LIFE-VERMEER contract (LIFE16 ENV/ES/000167) for financial support.
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Toropov, A.A., Toropova, A.P. & Benfenati, E. The Index of Ideality of Correlation: QSAR Model of Acute Toxicity for Zebrafish (Danio rerio) Embryo. Int J Environ Res 13, 387–394 (2019). https://doi.org/10.1007/s41742-019-00183-y
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DOI: https://doi.org/10.1007/s41742-019-00183-y