On the prediction of cytotoxicity of diverse chemicals for topminnow (Poeciliopsis lucida) hepatoma cell line, PLHC-1^$

References

• J.M. Armitage, F. Wania, and J.A. Arnot, Application of mass balance models and the chemical activity concept to facilitate the use of in vitro toxicity data for risk assessment, Environ. Sci. Technol. 48 (2014), pp. 9770–9779.
   PubMed Web of Science ®Google Scholar
• M. Halder, A. Kienzler, M. Whelan, and A. Worth, EURL ECVAM strategy to replace, reduce and refine the use of fish in aquatic toxicity and bioaccumulation testing, JRC Science and Policy Report, JRC93222, EUR, 2014.
   Google Scholar
• J. Stadnicka-Michalak, K. Tanneberger, K. Schirmer, and R. Ashauer, Measured and modeled toxicokinetics in cultured fish cells and application to in vitro-in vivo toxicity extrapolation, PloS ONE 9 (2014), e92303.
   Google Scholar
• N.R. Bury, S. Schnell, and C. Hogstrand, Gill cell culture systems as models for aquatic environmental monitoring, J. Exp. Biol. 217 (2014), pp. 639–650.
   Google Scholar
• H. Babich, J.A. Puerner, and E. Borenfreund, In vitro cytotoxicity of metals to bluegill (BF-2) cells, Arch. Environ. Contam. Toxicol. 15 (1986), pp. 31–37.
   Web of Science ®Google Scholar
• H. Babich and E. Borenfreund, Structure–activity relationship (SAR) models established in vitro with the neutral red cytotoxicity assay, Toxicol. In Vitro 1 (1987), pp. 3–9.
   Google Scholar
• J.C. Brandão, H.H. Bohets, I.E. Van De Vyver, and P.J. Dierickx, Correlation between the in vitro cytotoxicity to cultured fathead minnow fish cells and fish lethality data for 50 chemicals, Chemosphere 25 (1992), pp. 553–562.
   Google Scholar
• A. Castaño, M.J. Cantarino, P. Castillo, and J.V. Tarazona, Correlations between the RTG-2 cytotoxicity test EC50 and in vivo LC50 rainbow trout bioassay, Chemosphere 32 (1996), pp. 2141–2157.
   Google Scholar
• K. Schirmer, Proposal to improve vertebrate cell cultures to establish them as substitutes for the regulatory testing of chemicals and effluents using fish, Toxicology 224 (2006), pp. 163–183.
   Google Scholar
• N.C. Bols, V.R. Dayeh, L.E.J. Lee, and K. Schirmer, Use of fish cell lines in the toxicology and ecotoxicology of fish. Piscine cell lines in environmental toxicology, in Biochemistry and Molecular Biology of Fishes-Environmental Toxicology, Vol. 6, T.P. Mommsen and T.W. Moon, eds., Elsevier Science, Amsterdam, 2005, pp. 43–84.
   Google Scholar
• E. Borenfreund and C. Shopsis, Toxicity monitored with a correlated set of cell-culture assays, Xenobiotica 15 (1985), pp. 705–711.
   Google Scholar
• A. Forsby, M. Andersson, L. Lewan, and O. Sterner, The cytotoxicity of 22 sesquiterpenoid unsaturated dialdehydes, as determined by the neutral red absorption assay and by protein determination, Toxicol. In Vitro 5 (1991), pp. 9–14.
   Google Scholar
• K. Fent and J. Hunn, Cytotoxicity of organic environmental chemicals to fish liver cells (PLHC-1), Mar. Environ. Res. 42 (1996), pp. 377–382.
   Web of Science ®Google Scholar
• K. Knauer, C. Lampert, and J. Gonzalez-Valero, Comparison of in vitro and in vivo acute fish toxicity in relation to toxicant mode of action, Chemosphere 68 (2007), pp. 1435–1441.
   Google Scholar
• D. Caminada, C. Escher, and K. Fent, Cytotoxicity of pharmaceuticals found in aquatic systems: Comparison of PLHC-1 and RTG-2 fish cell lines, Aquat. Toxicol. 79 (2006), pp. 114–123.
   Google Scholar
• T. Mosmann, Rapid colorimetric assay for cellular growth and survival: Application to proliferation and cytotoxicity assays, J. Immunol. Meth. 65 (1983), pp. 55–63.
   Google Scholar
• P. Senthilraja and K. Kathiresan, In vitro cytotoxicity MTT assay in Vero, HepG2 and MCF-7 cell lines study of marine yeast, J. Appl. Pharm. Sci. 5 (2015), pp. 80–84.
   Google Scholar
• E. Zvinavashe, H. van den Berg, A.E. Soffers, J. Vervoort, A. Freidig, A.J. Murk, and I.M. Rietjens, QSAR models for predicting in vivo aquatic toxicity of chlorinated alkanes to fish, Chem. Res. Toxicol. 21 (2008), pp. 739–745.
   PubMed Web of Science ®Google Scholar
• OECD, Guidance document on the validation of (quantitative) structure–activity relationship [(Q)SAR] models, ENV/JM/MONO (2007)2, OECD Environment Health and Safety Publications Series of Testing and Assessment No. 69, Organization for Economic Co-operation and Development, Paris, France, 2007.
   Google Scholar
• SPARTAN 10, Wavefunction Inc., Irvine, USA, 2010; software available at https://www.wavefun.com/products/windows/Spartan10/win_spartan.html.
   Google Scholar
• DRAGON for Windows 6.0, Talete srl, Milan, Italy, 2014; software available at http://www.talete.mi.it/.
   Google Scholar
• J.J.P. Stewart, Optimization of parameters for semiempirical methods V: Modification of NDDO approximations and application to 70 elements, J. Mol. Model. 13 (2007), pp. 1173–1213.
   PubMed Web of Science ®Google Scholar
• P. Gramatica, N. Chirico, E. Papa, S. Kovarich, and S. Cassani, QSARINS: A new software for the development, analysis, and validation of QSAR MLR models, J. Comput. Chem. 34 (2013), pp. 2121–2132.
   Google Scholar
• P. Gramatica, S. Cassani, and N. Chirico. QSARINS-Chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS, J. Comput. Chem., Software News and Updates 35 (2014), pp. 1036– 1044.
   Google Scholar
• IBM SPSS Statistics for Windows, Version 25.0, IBM Corp. Armonk, NY, 2017; software available at https://www.ibm.com/us-en/marketplace?lnk=mp.
   Google Scholar
• P. Gramatica and A. Sangion, A historical excursus on the statistical validation parameters for QSAR models: A clarification concerning metrics and terminology, J. Chem. Inf. Model. 56 (2016), pp. 1127–1131.
   PubMed Web of Science ®Google Scholar
• K. Roy, R.D. Das, P. Ambure, and R.B. Aher, Be aware of error measures. Further studies on validation of predictive QSAR models, Chemom. Intell. Lab Syst. 152 (2016). pp. 18–33.
   Google Scholar
• J.G. Topliss and R.P. Edwards, Chance factors in studies of quantitative structure–activity relationships, J. Med. Chem. 22 (1979), pp. 1238–1244.
   Web of Science ®Google Scholar
• S. Wold and L. Eriksson, Statistical validation of QSAR results, in Chemometric Methods in Molecular Design, H. van de Waterbeemd, ed., Wiley-VCH, Weinheim, 1995, pp. 309–318.
   Google Scholar
• E. Papa, J.C. Dearden and P. Gramatica, Linear QSAR regression models for the prediction of bioconcentration factors by physicochemical properties and structural theoretical molecular descriptors, Chemosphere 6 (2007), pp. 351–358.
   Google Scholar
• Results of aquatic toxicity tests of chemicals conducted by Ministry of the Environment in Japan (March 2018. Available at http://www.env.go.jp/en/chemi/sesaku/aquatic_Mar_2018.pdf.
   Google Scholar
• A. Golbraikh and A. Tropsha, Beware of q2!, J. Mol. Graph. Model. 20 (2002), pp. 269–276.
   PubMed Web of Science ®Google Scholar
• P.K. Ojha, I. Mitra, R.N. Das, and K. Roy, Further exploring r2m metrics for validation of QSPR models, Chemom. Int. Lab. Syst. 107 (2011), pp. 194–205.
   Web of Science ®Google Scholar
• N. Chirico and P. Gramatica, Real external predictivity of QSAR models: How to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient, J. Chem. Inf. Model. 51 (2011), pp. 2320–2335.
   PubMed Web of Science ®Google Scholar
• J.F. Contrera, E.J. Matthews and R.D. Benz, Predicting the carcinogenic potential of pharmaceuticals in rodents using molecular structural similarity and E-state indices, Regul. Toxicol. Pharmacol. 38 (2003), pp. 243–259.
   Google Scholar
• R. Todeschini and V. Consonni, Handbook of Molecular Descriptors, John Wiley and Sons, New York, 2008.
   Google Scholar
• G. Chen, S. Zheng, X. Luo, J. Shen, W. Zhu, H. Liu, C. Gui, J. Zhang, M. Zheng, C. M. Puah, K. Chen, and H. Jiang, Focused combinatorial library design based on structural diversity, druglikeness and binding affinity score, J. Comb. Chem. 7 (2005), pp. 398–406.
   PubMedGoogle Scholar
• C. Oksel, D.A. Winkler, C.Y. Ma, T. Wilkins, and X.Z. Wang, Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches, Nanotoxicology 10 (2016), pp. 1001–1012.
   Google Scholar
• S. Önlü and M.T. Saçan, An in silico algal toxicity model with a wide applicability potential for industrial chemicals and pharmaceuticals, Environ. Toxicol. Chem. 36 (2017), pp. 1012–1019.
   PubMed Web of Science ®Google Scholar
• G. Tuğcu, M.T. Saçan, M. Vracko, M. Novic, and N. Minovski, QSTR modelling of the acute toxicity of pharmaceuticals to fish, SAR QSAR Environ. Res. 23 (2012), pp. 297–310.
   Google Scholar
• J. Caballero and M. Fernández, Linear and nonlinear modelling of antifungal activity of some heterocyclic ring derivatives using multiple linear regression and Bayesian-regularized neural networks, J. Mol. Model. 12 (2006), pp. 168–181.
   PubMed Web of Science ®Google Scholar
• B.J. Brüschweiler, F.E. Würglera and K. Fent, Cytotoxicity in vitro of organotin compounds to fish hepatoma cells PLHC-1 (Poeciliopsis lucida), Aquat. Toxicol. 32 (1995), pp. 143–160.
   Google Scholar
• J. Paasivirta, Chemical Ecotoxicology, CRC Press, Boca Raton, FL, 1995.
   Google Scholar
• 2’, 6, 6’-tetrabromo-4, 4’-isopropylidenediphenol (tetrabromobisphenol-A or TBBP-A) European Union Risk Assessment Report-2. (2006). Available at https://echa.europa.eu/documents/10162/32b000fe-b4fe-4828-b3d3-93c24c1cdd51.
   Google Scholar