Toxicity assessment of metal-ions binary mixtures: new computational approach for calculation toxicity indexes

TEKST Toxicity assessment of metal-ions binary mixtures: new computational approach for calculation toxicity indexes Proposed approach base on assumption that For test proposed approach we used dataset of 18 binary mixtures nanomaterials with ions. INTRODUCTION NEW APPROACH FOR CALCULATE TOXICITY INDEXES BINARY MIXTURES DATASET (NANOMATERIALS x IONS) TOXICITY INDEXES RESULTS Dawid Falkowski1,2, Alicja Mikolajczyk1,2, Tomasz Puzyn1,2 1 QSAR LAB, ul. Trzy Lipy 3, 80-172, Gdansk, Poland 2 Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, ul. Wita Stwosza 63, 80-308, Gdansk, Poland C. Ritz, F. Baty, J. C. Streibig, and D. Gerhard, “Dose-Response Analysis Using R,” PLoS ONE, vol. 10, no. 12, p. e0146021, 2015, doi: 10.1371/journal.pone.0146021. J. B. Sprague, “Lethal Concentrations of Copper and Zinc for Young Atlantic Salmon,” J. Fish. Board Can., vol. 21, no. 1, pp. 17–26, 1964, doi: 10.1139/f64-003. R. Altenburger, M. Nendza, and G. Schüürmann, “Mixture toxicity and its modeling by quantitative structure‐activity relationships,” Environ. Toxico l. Chem., vol. 22, no. 8, pp. 1900–1915, 2003, doi: 10.1897/01-386. H. Könemann, “Quantitative structure-activity relationships in fish toxicity studies Part 1: Relationship for 50 industrial pollutants,” To xico lo gy, vol. 19, no. 3, pp. 209– 221, 1981, doi: 10.1016/0300-483x(81)90130-x. J. B. Belden, R. J. Gilliom, and M. J. Lydy, “How well can we predict the toxicity of pesticide mixtures to aquatic life?,” Integr. Environ. Assess. Manag., vol. 3, no. 3, pp. 364–372, 2007, doi: 10.1002/ieam.5630030307. OVERALL TOXICITY INDEXES COMPARISON Model Deviation Ratio (MDR) Sum of Toxic Unit (STU) Additivity Index (AI) Mixture Toxicity Index (MTI) In the literature, the toxicity of nanomaterials mixtures is usually expressed as the EC50 dose. This index is useful in assessing toxicity against organisms but doesn’t indicate the nature of the interactions between the components of a mixture (synergism, antagonism, and additivism). Using the proposed approach, it is possible to calculate Toxicity Indexes based on data used to determine EC50 for the calculation of Toxicity Indexes. Knowledge about Toxicity Indexes, instead of the EC50 dose, allows for estimating the behavior of mixtures, which is especially important in environmental toxicity research. Analysi s Label Mixture component 1 (nanoparticle) Component 1 EC50 [μg/L] Mixture component 2 (ion) Component 2 EC50 [μg/L] Mixture EC50 [μg/L] 1NanoTiO2 (100 nm) 134,495 Ag+10,4 62,9 2NanoTiO2 (100 nm) 134,495 Ag+10,4 16,3 3NanoZnO (50 nm) 173,9 Ag+10,4 13,4 4NanoZnO (50 nm) 173,9 Ag+10,4 1,8 5NanoTiO2 (Anatase) (6 nm) 62,6 Cu2+ 48 173,9 6 NanoTiO2 (Anatase (75%) and Rutile (25%)) (21 nm) 55,7 Cu2+ 48 80 7NanoTiO2 (Rutile) (50 nm) 107 Cu2+ 48 240 8NanoTiO2 (Anatase) (6 nm) 62,6 Cu2+ 48 125 9 NanoTiO2 (Anatase (75%) and Rutile (25%)) (21 nm) 55,7 Cu2+ 48 94 10 NanoTiO2 (Rutile) (50 nm) 107 Cu2+ 48 226 11 NanoTiO2 (Anatase) (6 nm) 62,6 Cu2+ 48 212 12 NanoTiO2 (Anatase (75%)/Rutile (25%)) (21 nm) 55,7 Cu2+ 48 193 13 NanoTiO2 (Rutile) (50 nm) 107 Cu2+ 48 208 14 NanoFe3O4 (25 nm) 23,76 Zn2+ 4,89 4,12 15 NanoFe3O4 (25 nm) 23,76 Zn2+ 4,89 3,77 16 NanoFe3O4 (25 nm) 23,76 Zn2+ 4,89 2,69 17 NanoTiO2 (13,5 nm) 62,6 Cu2+ 101,87 2022,2 18 NanoTiO2 (13,5 nm) 62,6 Cu2+ 101,87 2034,6 Index Sum of Toxic Unit (STU) Additivity Index (AI) Mixture Toxicity Index (MTI) Model Deviation Ratio (MDR) Equation 𝑆𝑇𝑈 = ! 𝐶! 𝐸𝐶50! M = ! ! " # $ 𝐶! 𝐸𝐶50! If M = 1, then AI = M – 1; If M <1, then AI = 1/M – 1; If M >1, then AI = -M - 1 MTI =𝑙𝑜𝑔𝑀%−𝑙𝑜𝑔𝑀 𝑙𝑜𝑔𝑀% where M = ! ! " # $ 𝐶! 𝐸𝐶50! 𝑀%= 𝑀 𝑚𝑎𝑥 𝑖 ∈ 1, … , 𝑛 𝐶! 𝐸𝐶50! MDR =𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑A𝑉𝑎𝑙𝑢𝑒 𝑂𝑏𝑠𝑒𝑟𝑣𝑒𝑑A𝑉𝑎𝑙𝑢𝑒 Additivity 𝑆𝑇𝑈 = 1 AI = 0 MTI = 1 (if MTI = 0, then independent action) MDR = 1 Synergism 𝑆𝑇𝑈 < 1 AI > 0 MTI > 1 MDR > 1 Antagonism 𝑆𝑇𝑈 > 1 AI < 0 MTI < 0 MDR < 1 MIXTURE TOXICITY INDEXES Immobilization caused by whole mixture Concentrations of the mixture components MDR deviation: 6 (33%) coherent results: 12 (66%) An example for binary mixtures. Ag+ Cu2+ Zn2+ Grant agreement ID: 953152 Grant agreement ID: 101008099