Abstract
The occurrence of contaminants in natural waters is a potential threat to the environment. Since contaminants are commonly present as mixtures, numerous interactions may occur, resulting in lower or, more dangerously, higher toxicity, by comparison with single substances. The toxicity of multicomponent systems can be determined experimentally, but toxicity prediction by suitable models is faster, environmentally friendly and less expensive. Here we review approaches and models, which can be utilized in assessing toxicity of chemical mixtures. In the first part, the assessment of toxicity of chemical mixtures and possible interactions between mixture constituents are discussed. The second part covers conventional modeling, including the simplest, and most common toxicity models, namely concentration addition and independent action models, and derived integrated models. The third part presents advanced toxicity modeling. We review the quantitative structure–activity relationship (QSAR) approach and its elements: calculation of molecular descriptors and their selection with principal component analysis and genetic algorithm. Modeling with artificial neural networks is also discussed. We present hybrid models which combine the fuzzy set theory approach with the conventional concentration addition and independent action models. We conclude that conventional models: concentration addition and independent action model, are still most commonly used; integrated models are more accurate compared to conventional ones, even though their application requires more data; advanced numerical methods such as genetic algorithm, neural networks, and fuzzy set theory give a new perspective on toxicity prediction, and no universal tool for toxicity assessment has been developed so far.
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Abbreviations
- c i :
-
Molar concentration of the ith toxicant
- c MIX :
-
Total molar concentration of monitored toxicants
- D i :
-
Molecular descriptor of pure ith component
- D MIX :
-
Mixture descriptor value
- ECp,i :
-
Molar concentration of the toxicant i that elicits p percent of microbe inhibition
- ECp,MIX :
-
Total molar concentration of mixture that elicits p percent of microbe inhibition
- ECx,MIX,exp :
-
Concentration of a mixture eliciting x% effect
- EC50 :
-
Concentration of chemical i that causes a 50% response (median effective concentration)
- F :
-
Fisher criterion value
- f i :
-
Weibull function
- F(Z):
-
Concentration–response function
- IC50 :
-
Half maximal inhibitory concentration
- LC50 :
-
Lethal concentration 50
- p :
-
Inhibition level
- p′ :
-
Slope
- P D :
-
Dying probability
- P S :
-
Survival probability
- P S,MIX :
-
Joint survival probability
- Q 2 (or \(R_{\text{CV}}^{2}\)):
-
Squared cross-validated correlation coefficients
- \(Q_{\text{LOO}}^{2}\) :
-
Leave one out correlation coefficient
- \(Q_{\text{LMO}}^{2}\) :
-
Leave many out correlation coefficient
- R 2 :
-
Squared correlation coefficients
- R MIX (c):
-
Mixture response function
- s :
-
Standard deviation
- TUi :
-
Toxicity unit of the ith toxicant
- TUMIX :
-
Toxicity unit of the mixture
- x i :
-
Molar fraction of ith component
- y :
-
Response function
- β :
-
Response shape
- ε LUMO+1 :
-
Energy of second-lowest unoccupied molecular orbital
- λ :
-
Eigenvalue
- µ(x) :
-
Triangular fuzzy function
- σ :
-
Standard deviation
- ICIM:
-
Integrated concentration addition with independent action based on the multiple linear regression model
- INFCIM:
-
Integrated fuzzy concentration addition–independent action model
- LOEC:
-
Lowest observed effect concentration
- MNDO:
-
Modified neglect of differential overlap
- NOEC:
-
No-observed-effect concentration
- PPSA:
-
Partial positive surface area
- QSAR:
-
Quantitative structure–activity relationship
- REACH:
-
Registration, evaluation, authorization and restriction of chemicals
References
Adhikari C, Mishra BK (2018) Quantitative structure-activity relationships of aquatic narcosis: a review. Curr Comput Aided Drug Des 14(1):7–28. https://doi.org/10.2174/1573409913666170711130304
Affek K, Zaleska-Radziwill M, Doskocz N, Debek K (2018) Mixture toxicity of pharmaceuticals present in wastewater to aquatic organisms. Desalin Water Treat 117:15–20. https://doi.org/10.5004/dwt.2018.21964
Ahlers J, Riedhammer C, Vogliano M, Ebert RU, Kuhne R, Schuurmann G (2006) Acute to chronic ratios in aquatic toxicity: variation across trophic levels and relationship with chemical structure. Environ Toxicol Chem 25(11):2937–2945. https://doi.org/10.1897/05-701r.1
Ajmani S, Jadhav K, Kulkarni SA (2006) Three-dimensional QSAR using the k-nearest neighbor method and its interpretation. J Chem Inf Model 46(1):24–31. https://doi.org/10.1021/ci0501286
Altenburger R, Backhaus T, Boedeker W, Faust M, Scholze M, Grimme LH (2000) Predictability of the toxicity of multiple chemical mixtures to Vibrio fischeri: mixtures composed of similarly acting chemicals. Environ Toxicol Chem 19(9):2341–2347. https://doi.org/10.1002/etc.5620190926
Altenburger R, Nendza M, Schuurmann G (2003) Mixture toxicity and its modeling by quantitative structure-activity relationships. Environ Toxicol Chem 22(8):1900–1915. https://doi.org/10.1897/01-386
Altenburger R, Walter H, Grote M (2004) What contributes to the combined effect of a complex mixture? Environ Sci Technol 38(23):6353–6362. https://doi.org/10.1021/es049528k
Altenburger R, Schmitt H, Schüürmann G (2005) Algal toxicity of nitrobenzenes: combined effect analysis as a pharmacological probe for similar modes of interaction. Environ Toxicol Chem 24(2):324-333. https://doi.org/10.1897/04-032r.1
Andersen ME, Dennison JE (2004) Mechanistic approaches for mixture risk assessments: present capabilities with simple mixtures and future directions. Environ Toxicol Pharmacol 16(1–2):1–11. https://doi.org/10.1016/j.etap.2003.10.004
Arrhenius A, Gronvall F, Scholze M, Backhaus T, Blanck H (2004) Predictability of the mixture toxicity of 12 similarly acting congeneric inhibitors of photosystem II in marine periphyton and epipsammon communities. Aquat Toxicol 68(4):351–367. https://doi.org/10.1016/j.aquatox.2004.04.002
Backhaus T, Faust M (2012) Predictive environmental risk assessment of chemical mixtures: a conceptual framework. Environ Sci Technol 46(5):2564–2573. https://doi.org/10.1021/es2034125
Backhaus T, Altenburger R, Boedeker W, Faust M, Scholze M, Grimme LH (2000) Predictability of the toxicity of a multiple mixture of dissimilarly acting chemicals to Vibrio fischeri. Environ Toxicol Chem 19(9):2348–2356. https://doi.org/10.1002/etc.5620190927
Backhaus T, Arrhenius A, Blanck H (2004) Toxicity of a mixture of dissimilarly acting substances to natural algal communities: predictive power and limitations of independent action and concentration addition. Environ Sci Technol 38(23):6363–6370. https://doi.org/10.1021/es0497678
Backhaus T, Porsbring T, Arrhenius A, Brosche S, Johansson P, Blanck H (2011) Single-substance and mixture toxicity of five pharmaceuticals and personal care products to marine periphyton communities. Environ Toxicol Chem 30(9):2030–2040. https://doi.org/10.1002/etc.586
Baek IH, Kim Y, Baik S, Kim J (2019) Investigation of the synergistic toxicity of binary mixtures of pesticides and pharmaceuticals on Aliivibrio fischeri in Major River basins in South Korea. Int J Environ Res Public Health 16(2):16. https://doi.org/10.3390/ijerph16020208
Bagnis S, Fitzsimons MF, Snape J, Tappin A, Comber S (2018) Processes of distribution of pharmaceuticals in surface freshwaters: implications for risk assessment. Environ Chem Lett 16(4):1193–1216. https://doi.org/10.1007/s10311-018-0742-7
Belden JB, Gilliom RJ, Lydy MJ (2007) How well can we predict the toxicity of pesticide mixtures to aquatic life? 3(3):364–372. https://doi.org/10.1897/1551-3793(2007)3[e1:HWCWPT]2.0.CO;2
Bhagat P (1990) An introduction to neural nets. Chem Eng Prog 86(8):55–60
Bilal M, Iqbal HMN, Barcelo D (2019a) Persistence of pesticides-based contaminants in the environment and their effective degradation using laccase-assisted biocatalytic systems. Sci Total Environ 695:17. https://doi.org/10.1016/j.scitotenv.2019.133896
Bilal M, Rasheed T, Nabeel F, Iqbal HMN, Zhao YP (2019b) Hazardous contaminants in the environment and their laccase-assisted degradation: a review. J Environ Manag 234:253–264. https://doi.org/10.1016/j.jenvman.2019.01.001
Bliss CI (2008) The toxicity of poisons applied jointly. Ann Appl Biol 26:585–615. https://doi.org/10.1111/j.1744-7348.1939.tb06990.x
Brezovsek P, Elersek T, Filipic M (2014) Toxicities of four anti-neoplastic drugs and their binary mixtures tested on the green alga Pseudokirchneriella subcapitata and the cyanobacterium Synechococcus leopoliensis. Water Res 52:168–177. https://doi.org/10.1016/j.watres.2014.01.007
Calabrese EJ (2002) Hormesis: changing view of the dose-response, a personal account of the history and current status. Mutat Res Rev Mutat Res 511(3):181–189. https://doi.org/10.1016/s1383-5742(02)00013-3
Calabrese EJ (2008) Hormesis: why it is important to toxicology and toxicologists. Environ Toxicol Chem 27(7):1451–1474. https://doi.org/10.1897/07-541.1
Caliman FA, Gavrilescu M (2009) Pharmaceuticals, personal care products and endocrine disrupting agents in the environment: a review. Clean-Soil Air Water 37(4–5):277–303. https://doi.org/10.1002/clen.200900038
Carlsson C, Johansson AK, Alvan G, Bergman K, Kühler T (2006) Are pharmaceuticals potent environmental pollutants? Part I: environmental risk assessments of selected active pharmaceutical ingredients. Sci Total Environ 364:67–87
Cedergreen N (2014) Quantifying synergy: a systematic review of mixture toxicity studies within environmental toxicology. PLoS ONE 9(5):e96580. https://doi.org/10.1371/journal.pone.0096580
Cedergreen N, Streibig JC, Kudsk P, Mathiassen SK, Dukec SO (2007) The occurrence of hormesis in plants and algae. Dose Response 5(2):150–162. https://doi.org/10.2203/dose-response.06-008.Cedergreen
Cedergreen N, Christensen AM, Kamper A, Kudsk P, Solvejg KM, Streibig JC, Sørensen H (2008) A review of independent action compared to concentration addition as reference models for mixtures of compounds with different molecular target sites. Environ Toxicol Chem 27(7):1621–1632. https://doi.org/10.1897/07-474.1
Cedergreen N, Sorensen H, Svendsen C (2012) Can the joint effect of ternary mixtures be predicted from binary mixture toxicity results? Sci Total Environ 427:229–237. https://doi.org/10.1016/j.scitotenv.2012.03.086
Carles L, Joly M, Bonnemoy F, Leremboure M, Donnadieu F, Batisson I, Besse-Hoggan P (2018) Biodegradation and toxicity of a maize herbicide mixture: mesotrione, nicosulfuron and S-metolachlor. 354:42-53. https://doi.org/10.1016/j.jhazmat.2018.04.045
Chen L, Li SB, Zhou YM, Zhou X, Jiang H, Liu X, Yuan S (2020) Risk assessment for pesticide mixtures on aquatic ecosystems in China: a proposed framework. Pest Manag Sci 76:444–453. https://doi.org/10.1002/ps.5529
Cizmas L, Sharma VK, Gray CM, McDonald TJ (2015) Pharmaceuticals and personal care products in waters: occurrence, toxicity, and risk. Environ Chem Lett 13(4):381–394. https://doi.org/10.1007/s10311-015-0524-4
Cleuvers M (2004) Mixture toxicity of the anti-inflammatory drugs diclofenac, ibuprofen, naproxen, and acetylsalicylic acid. Ecotoxicol Environ Saf 59(3):309–315. https://doi.org/10.1016/s0147-6513(03)00141-6
Cvetnic M, Perisic DJ, Kovacic M, Ukic S, Bolanca T, Rasulev B, Kusic H, Loncaric Bozic A (2019) Toxicity of aromatic pollutants and photooxidative intermediates in water: a QSAR study. Ecotoxicol Environ Saf 169:918–927. https://doi.org/10.1016/j.ecoenv.2018.10.100
Deblonde T, Cossu-Leguille C, Hartemann P (2011) Emerging pollutants in wastewater: a review of the literature. Int J Hyg Environ Health 214(6):442–448. https://doi.org/10.1016/j.ijheh.2011.08.002
Dupraz V, Ménard D, Akcha F, Budzinski H, Stachowski-Haberkorn S (2019) Toxicity of binary mixtures of pesticides to the marine microalgae Tisochrysis lutea and Skeletonema marinoi: Substance interactions and physiological impacts. Aquat Toxicol 211:148–162. https://doi.org/10.1016/j.aquatox.2019.03.015
Gao Y, Feng J, Kang L, Xu X, Zhu L (2018) Concentration addition and independent action model: which is better in predicting the toxicity for metal mixtures on zebrafish larvae. Sci Total Environ 610–611:442–450. https://doi.org/10.1016/j.scitotenv.2017.08.058
Di Nica V, Villa S, Finizio A (2017) Toxicity of individual pharmaceuticals and their mixtures to Aliivibrio fischeri: evidence of toxicological interactions in binary combinations. Environ Toxicol Chem 36(3):815–822. https://doi.org/10.1002/etc.3686
Drgan V, Zuperl S, Vracko M, Como F, Novic M (2016) Robust modelling of acute toxicity towards fathead minnow (Pimephales promelas) using counter-propagation artificial neural networks and genetic algorithm. SAR QSAR Environ Res 27(7):501–519. https://doi.org/10.1080/1062936x.2016.1196388
Dsikowitzky L, Schwarzbauer J (2014) Industrial organic contaminants: identification, toxicity and fate in the environment. Environ Chem Lett 12(3):371–386. https://doi.org/10.1007/s10311-014-0467-1
EUR-Lex (2000) Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy. Off J Eur Union L 327:1–72
EUR-Lex (2006) Regulation (EC) No 1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), establishing a European Chemicals Agency, amending Directive 1999/45/EC and repealing Council Regulation (EEC) No 793/93 and Commission Regulation (EC) No 1488/94 as well as Council Directive 76/769/EEC and Commission Directives 91/155/EEC, 93/67/EEC, 93/105/EC and 2000/21/EC. Off J Eur Union L 396:1–527
EUR-Lex (2013) Directive 2013/39/EU of the European Parliament and of the Council of 12 August 2013 amending Directives 2000/60/EC and 2008/105/EC as regards priority substances in the field of water policy Text with EEA relevance. Off J Eur Union L 226:1–17
Faust M, Altenburger R, Backhaus T, Bodeker W, Scholze M, Grimme LH (2000) Predictive assessment of the aquatic toxicity of multiple chemical mixtures. J Environ Qual 29(4):1063–1068. https://doi.org/10.2134/jeq2000.00472425002900040005x
Gagne F, Blaise C (1997) Predicting the toxicity of complex mixtures using artificial neural networks. Chemosphere 35(6):1343–1363. https://doi.org/10.1016/s0045-6535(97)00178-1
Galatchi LD (2006) Environmental risk assessment. In: Simeonov LJ, Chirila E (eds) Chemicals as intentional and accidental global environmental threats. Springer, Dordrecht, pp 1–6
Gaudin T, Rotureau P, Fayet G (2015) Mixture descriptors toward the development of quantitative structure-property relationship models for the flash points of organic mixtures. Ind Eng Chem Res 54(25):6596–6604. https://doi.org/10.1021/acs.iecr.5b01457
Gevrey M, Dimopoulos L, Lek S (2003) Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecol Model 160(3):249–264. https://doi.org/10.1016/s0304-3800(02)00257-0
Godoy AA, de Oliveira AC, Silva JGM, Azevedo CCJ, Domingues I, Nogueira AJA, Kummrow F (2019) Single and mixture toxicity of four pharmaceuticals of environmental concern to aquatic organisms, including a behavioral assessment. Chemosphere 235:373–382. https://doi.org/10.1016/j.chemosphere.2019.06.200
Golbraikh A, Shen M, Xiao ZY, Xiao YD, Lee KH, Tropsha A (2003) Rational selection of training and test sets for the development of validated QSAR models. J Comput Aided Mol Des 17(2):241–253. https://doi.org/10.1023/a:1025386326946
Gonzalez-Pleiter M, Gonzalo S, Rodea-Palomares I, Leganés F, Rosal R, Boltes K, Marco E, Fernández-Piñas F (2013) Toxicity of five antibiotics and their mixtures towards photosynthetic aquatic organisms: implications for environmental risk assessment. Water Res 47(6):2050–2064. https://doi.org/10.1016/j.watres.2013.01.020
Gramatica P (2007) Principles of QSAR models validation: internal and external. QSAR Comb Sci 26:694–701. https://doi.org/10.1002/qsar.200610151
Gramatica P, Vighi M, Consolaro F, Todeschini R, Finizio A, Faust M (2001) QSAR approach for the selection of congeneric compounds with a similar toxicological mode of action. Chemosphere 42(8):873–883. https://doi.org/10.1016/s0045-6535(00)00180-6
Halder M, Kienzler A, Whelan M, Worth A (2014) EURL ECVAM strategyto replace, reduce and refinethe use of fish in aquatic toxicity and bioaccumulation testing. JRC science and policy reports. Publ Off Eur Union. https://doi.org/10.2788/084219
Hemken HG, Lehmann PA (1992) The use of computerized molecular-structure scanning and principal component analysis to calculate molecular descriptors for QSAR. Quant Struct Act Relat 11(3):332–338. https://doi.org/10.1002/qsar.2660110305
Heys KA, Shore RF, Pereira MG, Jones KC, Martin FL (2016) Risk assessment of environmental mixture effects. RSC Adv 6(53):47844–47857. https://doi.org/10.1039/c6ra05406d
Horton AA, Barnes DKA (2020) Microplastic pollution in a rapidly changing world: implications for remote and vulnerable marine ecosystems. Sci Total Environ 738:140349. https://doi.org/10.1016/j.scitotenv.2020.140349
Isidori M, Lavorgna M, Nardelli A, Pascarella L, Parrella A (2005) Toxic and genotoxic evaluation of six antibiotics on non-target organisms. Sci Total Environ 346(1–3):87–98. https://doi.org/10.1016/j.scitotenv.2004.11.017
Jantzen J (2013) Foundations of fuzzy control: a practical approach, 2nd edn. Blackwell Science, Oxford
Jha SK, Yoon TH (2020) Toxicity modelling of nanomaterials by origin evaluation of their physicochemical descriptors using a combination of principal component analysis and support vector machine methods. Expert Syst 37:e12492. https://doi.org/10.1111/exsy.12492
Jha SK, Yoon TH, Pan ZQ (2018) Multivariate statistical analysis for selecting optimal descriptors in the toxicity modeling of nanomaterials. Comput Biol Med 99:161–172. https://doi.org/10.1016/j.compbiomed.2018.06.012
Jin XQ, Jin MH, Sheng LX (2014) Three dimensional quantitative structure-toxicity relationship modeling and prediction of acute toxicity for organic contaminants to algae. Comput Biol Med 51:205–213. https://doi.org/10.1016/j.compbiomed.2014.05.009
Johansen HK, Jensen TG, Dessau RB, Lundgren B, Frimodt-Moller N (2000) Antagonism between penicillin and erythromycin against Streptococcus pneumoniae in vitro and in vivo. J Antimicrob Chemother 46(6):973–980. https://doi.org/10.1093/jac/46.6.973
Jolliffe IT, Cadima J (2016) Principal component analysis: a review and recent developments. Philos Trans R Soc A 374:20150202. https://doi.org/10.1098/rsta.2015.0202
Junghans M (2004) Studies on combination effects of environmentally relevant toxicants Validation of prognostic concepts for assessing the algal toxicity of realistic aquatic pesticide mixtures. Doctoral dissertation, University of Bremen, Bremen, Germany. https://pdfs.semanticscholar.org/9d27/d49be4161005682465af6df0a3842e362042.pdf. Accessed 9 Sept 2020
Kaiser KLE (2003) The use of neural networks in QSARs for acute aquatic toxicological endpoints. Theochem J Mol Struct 622(1–2):85–95. https://doi.org/10.1016/s0166-1280(02)00620-6
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
Karelson M, Dobchev DA (2016) QSAR of heterocyclic compounds in large descriptor spaces. In: Scriven EFV, Ramsden CA (eds) heterocyclic chemistry in the 21st century: a tribute to Alan Katritzky, vol 120. Advances in heterocyclic chemistry. Elsevier Academic Press Inc., San Diego, pp 237–273
Katritzky AR, Kuanar M, Slavov S, Hall CD (2010) Quantitative correlation of physical and chemical properties with chemical structure: utility for prediction. Chem Rev 110(10):5714–5789. https://doi.org/10.1021/cr900238d
Khan K, Roy K (2019) Ecotoxicological QSAR modelling of organic chemicals against Pseudokirchneriella subcapitata using consensus predictions approach. SAR QSAR Environ Res 30:665–681. https://doi.org/10.1080/1062936x.2019.1648315
Kim J, Kim S, Schaumann GE (2013a) Development of QSAR-based two-stage prediction model for estimating mixture toxicity. SAR QSAR Environ Res 24(10):841–861. https://doi.org/10.1080/1062936x.2013.815654
Kim J, Kim S, Schaumann GE (2013b) Reliable predictive computational toxicology methods for mixture toxicity: toward the development of innovative integrated models for environmental risk assessment. Rev Environ Sci Bio-Technol 12(3):235–256. https://doi.org/10.1007/s11157-012-9286-7
Kortenkamp A, Altenburger R (1998) Synergisms with mixtures of xenoestrogens: a reevaluation using the method of isoboles. Sci Total Environ 221(1):59–73. https://doi.org/10.1016/s0048-9697(98)00261-7
Laetz CA, Baldwin DH, Collier TK, Hebert V, Stark JD, Scholz NL (2009) The synergistic toxicity of pesticide mixtures: implications for risk assessment and the conservation of endangered Pacific Salmon. Environ Health Perspect 117(3):348–353. https://doi.org/10.1289/ehp.0800096
Leardi R, Gonzalez AL (1998) Genetic algorithms applied to feature selection in PLS regression: how and when to use them. Chemom Intell Lab Syst 41(2):195–207. https://doi.org/10.1016/s0169-7439(98)00051-3
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. https://doi.org/10.1038/nature14539
Li FX, Fan DF, Wang H, Wang H, Yang H, Li W, Tang Y, Liu G (2017) In silico prediction of pesticide aquatic toxicity with chemical category approaches. Toxicol Res 6(6):831–842. https://doi.org/10.1039/c7tx00144d
Lin K (2009) Joint acute toxicity of tributyl phosphate and triphenyl phosphate to Daphnia magna. Environ Chem Lett 7(4):309–312. https://doi.org/10.1007/s10311-008-0170-1
Liu SS, Wang CL, Zhang J, Zhu XW, Li WY (2013) Combined toxicity of pesticide mixtures on green algae and photobacteria. Ecotoxicol Environ Saf 95:98–103. https://doi.org/10.1016/j.ecoenv.2013.05.018
Liu S, Lai C, Li B, Zhang C, Zhang M, Huang D, Qin L, Yi H, Liu X, Huang F, Zhou X, Chen L (2019) Role of radical and non-radical pathway in activating persulfate for degradation of p-nitrophenol by sulfur-doped ordered mesoporous carbon. Chem Eng J 384:123304. https://doi.org/10.1016/j.cej.2019.123304
Loewe S, Muischnek H (1926a) Effect of combinations: mathematical basis of problem. N S Arch Ex Path Ph 114:313–326
Loewe S, Muischnek H (1926b) Über kombinationswirkungen. 1. Mitteilung: Hilfsmittel der Fragestellung. Naunyn-Schmiedebergs Arch Exp Pathol Pharmakol 114:313–326. https://doi.org/10.1007/bf01952257
Logan DT, Wilson HT (1995) An ecological risk assessment method for species exposed to contaminant mixtures. Environ Toxicol Chem 14(2):351–359. https://doi.org/10.1002/etc.5620140222
Luan F, Xu X, Liu HT, Cordeiro M (2013) Prediction of the baseline toxicity of non-polar narcotic chemical mixtures by QSAR approach. Chemosphere 90(6):1980–1986. https://doi.org/10.1016/j.chemosphere.2012.10.065
Lydy MJ, Linck SL (2003) Assessing the impact of triazine herbicides on organophosphate insecticide toxicity to the earthworm Eisenia fetida. Arch Environ Contam Toxicol 45(3):343–349. https://doi.org/10.1007/s00244-002-0218-y
Malik LA, Bashir A, Qureashi A, Pandith AH (2019) Detection and removal of heavy metal ions: a review. Environ Chem Lett 17(4):1495–1521. https://doi.org/10.1007/s10311-019-00891-z
Martinez-Lopez Y, Marrero-Ponce Y, Barigye SJ, Teran E, Martinez-Santiago O, Zambrano CH, Torres FJ (2019) When global and local molecular descriptors are more than the sum of its parts: simple, but not simpler? Mol Divers. https://doi.org/10.1007/s11030-019-10002-3
Mitchell M (1998) An introduction to genetic algorithms. MIT Press, Cambridge
Moraglio A, Poli R (2011) Geometric crossover for the permutation representation. Intell Artif 5(2011):49–63. https://doi.org/10.3233/IA-2011-0004
Muratov EN, Varlamova EV, Artemenko AG, Polishchuk PG, Kuz’min VE (2012) Existing and developing approaches for QSAR analysis of mixtures. Mol Inf 31(3–4):202–221. https://doi.org/10.1002/minf.201100129
Mwense M, Wang XZ, Buontempo FV, Horan N, Young A, Osborn D (2004) Prediction of noninteractive mixture toxicity of organic compounds based on a fuzzy set method. J Chem Inf Comput Sci 44(5):1763–1773. https://doi.org/10.1021/ci0499368
Mwense M, Wang XZ, Buontempo FV, Horan N, Young A, Osborn D (2006) QSAR approach for mixture toxicity prediction using independent latent descriptors and fuzzy membership functions. SAR QSAR Environ Res 17(1):53–73. https://doi.org/10.1080/10659360600562202
Nagai T (2017) Predicting herbicide mixture effects on multiple algal species using mixture toxicity models. Environ Toxicol Chem 36(10):2624–2630. https://doi.org/10.1002/etc.3800
Neale PA, Leusch FDL, Escher BI (2017) Applying mixture toxicity modelling to predict bacterial bioluminescence inhibition by non-specifically acting pharmaceuticals and specifically acting antibiotics. Chemosphere 173:387–394. https://doi.org/10.1016/j.chemosphere.2017.01.018
Norgaard KB, Cedergreen N (2010) Pesticide cocktails can interact synergistically on aquatic crustaceans. Environ Sci Pollut Res 17(4):957–967. https://doi.org/10.1007/s11356-009-0284-4
Nowell LH, Norman JE, Moran PW, Martin JD, Stone WW (2014) Pesticide toxicity index: a tool for assessing potential toxicity of pesticide mixtures to freshwater aquatic organisms. Sci Total Environ 476:144–157. https://doi.org/10.1016/j.scitotenv.2013.12.088
Ocampo PS, Lazar V, Papp B, Arnoldini M, Wiesch PA, Busa-Fekete R, Fekete G, Pál C, Ackermann M, Bonhoeffer S (2014) Antagonism between bacteriostatic and bactericidal antibiotics is prevalent. Antimicrob Agents Chemother 58(8):4573–4582. https://doi.org/10.1128/aac.02463-14
Olmstead AW, LeBlanc GA (2005) Toxicity assessment of environmentally relevant pollutant mixtures using a heuristic model. Integr Environ Assess Manag 1(2):114–122. https://doi.org/10.1897/ieam_2004-005r.1
Papa E, Battaini F, Gramatica P (2005) Ranking of aquatic toxicity of esters modelled by QSAR. Chemosphere 58(5):559–570. https://doi.org/10.1016/j.chemosphere.2004.08.003
Pape-Lindstrom PA, Lydy MJ (1997) Synergistic toxicity of atrazine and organophosphate insecticides contravenes the response addition mixture model. Environ Toxicol Chem 16(11):2415–2420. https://doi.org/10.1002/etc.5620161130
Planson AG, Carbonell P, Paillard E, Pollet N, Faulon JL (2012) Compound toxicity screening and structure-activity relationship modeling in Escherichia coli. Biotechnol Bioeng 109(3):846–850. https://doi.org/10.1002/bit.24356
Puckowski A, Stolte S, Wagil M, Markiewicz M, Łukaszewicz P, Stepnowski P, Białk-Bielińska A (2017) Mixture toxicity of flubendazole and fenbendazole to Daphnia magna. Int J Hyg Environ Health 220(3):575–582. https://doi.org/10.1016/j.ijheh.2017.01.011
Qin LT, Liu SS, Zhang J, Xiao QF (2011) A novel model integrated concentration addition with independent action for the prediction of toxicity of multi-component mixture. Toxicology 280(3):164–172. https://doi.org/10.1016/j.tox.2010.12.007
Qin LT, Chen YH, Zhang X, Mo LY, Zeng HH, Liang YP (2018) QSAR prediction of additive and non-additive mixture toxicities of antibiotics and pesticide. Chemosphere 198:122–129. https://doi.org/10.1016/j.chemosphere.2018.01.142
Ra JS, Lee BC, Chang NI, Kim SD (2006) Estimating the combined toxicity by two-step prediction model on the complicated chemical mixtures from wastewater treatment plant effluents. Environ Toxicol Chem 25(8):2107–2113. https://doi.org/10.1897/05-484r.1
Rajabi M, Shafiei F (2019) QSAR models for predicting aquatic toxicity of esters using genetic algorithm-multiple linear regression methods. Comb Chem High Throughput Screen 22(5):317–325. https://doi.org/10.2174/1386207322666190618150856
Ranganathan S, Gribskov M, Nakai K, Schönbach C (2019) Encyclopedia of bioinformatics and computational biology. Elsevier, Amsterdam
Rasheed T, Bilal M, Nabeel F, Iqbal HMN, Li CL, Zhou YF (2018) Fluorescent sensor based models for the detection of environmentally-related toxic heavy metals. Sci Total Environ 615:476–485. https://doi.org/10.1016/j.scitotenv.2017.09.126
Rasulev B, Kušić H, Leszczynska D, Leszczynski J, Koprivanac N (2010) QSAR modeling of acute toxicity on mammals for aromatic compounds: the case study using oral LD50 for rats. J Environ Monit 12:1037–1044. https://doi.org/10.1039/b919489d
Rodea-Palomares I, Petre AL, Boltes K, Leganés F, Perdigón-Melón JA, Rosal R, Fernández-Piñas F (2010) Application of the combination index (CI)-isobologram equation to study the toxicological interactions of lipid regulators in two aquatic bioluminescent organisms. Water Res 44(2):427–438. https://doi.org/10.1016/j.watres.2009.07.026
Rodea-Palomares I, Gonzalez-Pleiter M, Martin-Betancor K, Rosal R, Fernandez-Pinas F (2015) Additivity and interactions in ecotoxicity of pollutant mixtures: some patterns, conclusions, and open questions. Toxics 3(4):342–369. https://doi.org/10.3390/toxics3040342
Rohr JR, Schotthoefer AM, Raffel TR, Carrick HJ, Halstead N, Hoverman JT, Johnson CM, Johnson LB, Lieske C, Piwoni MD, Schoff PK, Beasley VR (2008) Agrochemicals increase trematode infections in a declining amphibian species. Nature 455(7217):1235–1239. https://doi.org/10.1038/nature07281
Roque JV, Cardoso W, Peternelli LA, Teofilo RF (2019) Comprehensive new approaches for variable selection using ordered predictors selection. Anal Chim Acta 1075:57–70. https://doi.org/10.1016/j.aca.2019.05.039
Saldana DA, Starck L, Mougin P, Rousseau B, Creton B (2013) On the rational formulation of alternative fuels: melting point and net heat of combustion predictions for fuel compounds using machine learning methods. SAR QSAR Environ Res 24(4):259–277. https://doi.org/10.1080/1062936X.2013.766634
Satpathy R (2019) Quantitative structure-activity relationship methods for the prediction of the toxicity of pollutants. Environ Chem Lett 17(1):123–128. https://doi.org/10.1007/s10311-018-0780-1
Schultz TW, Cronin MTD, Walker JD, Aptula AO (2003) Quantitative structure-activity relationships (QSARs) in toxicology: a historical perspective. Theochem J Mol Struct 622(1–2):1–22. https://doi.org/10.1016/s0166-1280(02)00614-0
Science for Environment Policy (2015) Integrating environmental risk assessment. Thematic issue 53. Issue produced for the European commission DG environment by the science communication unit, UWE, Bristol. http://ec.europa.eu/science-environment-policy. Accessed 9 June 2020. https://doi.org/10.2779/98132
Seeger B, Mentz A, Knebel C, Schmidt F, Bednarz H, Niehaus K, Albaum S, Kalinowski J, Noll T, Steinberg P, Marx-Stoelting P, Heise T (2019) Assessment of mixture toxicity of (tri)azoles and their hepatotoxic effects in vitro by means of omics technologies. Arch Toxicol 93:2321–2333. https://doi.org/10.1007/s00204-019-02502-w
Shao Y, Chen Z, Hollert H, Zhou S, Deutschmann B, Seiler T-B (2019) Toxicity of 10 organic micropollutants and their mixture: implications for aquatic risk assessment. Sci Total Environ 666:1273–1282. https://doi.org/10.1016/j.scitotenv.2019.02.047
Siler W, Buckley JJ (2005) Fuzzy expert systems and fuzzy reasoning. Wiley, Hoboken
Silva E, Martins C, Pereira AS, Loureiro S, Cerejeira MJ (2018) Toxicity prediction and assessment of an environmentally realistic pesticide mixture to Daphnia magna and Raphidocelis subcapitata. Ecotoxicology 27(7):956–967. https://doi.org/10.1007/s10646-018-1938-0
Silva ARR, Cardoso DN, Cruz A, Mendo S, Soares A, Loureiro S (2019) Long-term exposure of Daphnia magna to carbendazim: how it affects toxicity to another chemical or mixture. Environ Sci Pollut Res 26(16):16289–16302. https://doi.org/10.1007/s11356-019-05040-1
Sigurnjak M, Ukić Š, Cvetnić M, Markić M, Novak Stankov M, Rasulev B, Kušić H, Lončarić Božić A, Rogošić M, Bolanča T (2020) Combined toxicities of binary mixtures of alachlor, chlorfenvinphos, diuron and isoproturon. Chemosphere 240:124973. https://doi.org/10.1016/j.chemosphere.2019.124973
Singh S, Singh N, Kumar V, Datta S, Wani AB, Singh D, Singh K, Singh J (2016) Toxicity, monitoring and biodegradation of the fungicide carbendazim. Environ Chem Lett 14(3):317–329. https://doi.org/10.1007/s10311-016-0566-2
Singh S, Kumar V, Chauhan A, Datta S, Wani AB, Singh N, Singh J (2018) Toxicity, degradation and analysis of the herbicide atrazine. Environ Chem Lett 16(1):211–237. https://doi.org/10.1007/s10311-017-0665-8
Sobati MA, Abooali D, Maghbooli B, Najafi H (2016) A new structure-based model for estimation of true critical volume of multi-component mixtures. Chemometr Intell Lab Syst 155:109–119. https://doi.org/10.1016/j.chemolab.2016.04.007
Tanaka Y, Tada M (2017) Generalized concentration addition approach for predicting mixture toxicity. Environ Toxicol Chem 36(1):265–275. https://doi.org/10.1002/etc.3503
Tang SY, Liang JH, Xiang CC, Xiao Y, Wang X, Wu J, Li G, Cheke RA (2019) A general model of hormesis in biological systems and its application to pest management. J R Soc Interface 16(157):11. https://doi.org/10.1098/rsif.2019.0468
Tijani JO, Fatoba OO, Babajide OO, Petrik LF (2016) Pharmaceuticals, endocrine disruptors, personal care products, nanomaterials and perfluorinated pollutants: a review. Environ Chem Lett 14(1):27–49. https://doi.org/10.1007/s10311-015-0537-z
Ukic S, Novak M, Vlahovic A, Avdalović N, Liu Y, Buszewski B, Bolanča T (2014) Development of gradient retention model in ion chromatography. Part II: artificial intelligence QSRR approach. Chromatographia 77(15–16):997–1007. https://doi.org/10.1007/s10337-014-2654-4
Ukić Š, Sigurnjak M, Cvetnić M, Markić M, Novak Stankov M, Rogošić M, Rasulev B, Lončarić Božić A, Kušić H, Bolanča T (2019) Toxicity of pharmaceuticals in binary mixtures: assessment by additive and non-additive toxicity models. Ecotoxicol Environ Saf 185:109696. https://doi.org/10.1016/j.ecoenv.2019.109696
United Nations (2011) Globally harmonized system of classification and labelling of chemicals (GSH), 4th edn. United Nations, New York
U.S. Environmental Protection Agency (1991) Guidelines for developmental toxicity risk assessment. Fed Regist 56(234):63798–63826
Verhaar HJM, Vanleeuwen CJ, Hermens JLM (1992) Classifying environmental-pollutants. 1. Structure-activity-relationships for prediction of aquatic toxicity. Chemosphere 25(4):471–491. https://doi.org/10.1016/0045-6535(92)90280-5
Wang Z, Chen JW, Huang LP, Wanga Y, Cai X, Qiao X, Dong Y (2009) Integrated fuzzy concentration addition-independent action (IFCA-IA) model outperforms two-stage prediction (TSP) for predicting mixture toxicity. Chemosphere 74(5):735–740. https://doi.org/10.1016/j.chemosphere.2008.08.023
Wang T, Lin Z, Yin D, Tian D, Zhang Y, Kong D (2011) Hydrophobicity-dependent QSARs to predict the toxicity of perfluorinated carboxylic acids and their mixtures. Environ Toxicol Pharmacol 32(2):259–265. https://doi.org/10.1016/j.etap.2011.05.011
Wang XH, Fan LY, Wang S, Wang Y, Yan LC, Zheng SS, Martyniuk CJ, Zhao YH (2017) Relationship between acute and chronic toxicity for prevalent organic pollutants in Vibrio fischeri based upon chemical mode of action. J Hazard Mater 338:458–465. https://doi.org/10.1016/j.jhazmat.2017.05.058
Wang D, Wu X, Lin Z, Ding Y (2018) A comparative study on the binary and ternary mixture toxicity of antibiotics towards three bacteria based on QSAR investigation. Environ Res 162:127–134. https://doi.org/10.1016/j.envres.2017.12.015
Wang CL, Yang Y, Wu NX, Gao M, Tan YF (2019) Combined toxicity of pyrethroid insecticides and heavy metals: a review. Environ Chem Lett 17(4):1693–1706. https://doi.org/10.1007/s10311-019-00905-w
Wieczerzak M, Kudlak B, Yotova G, Nedyalkova M, Tsakovski S, Simeonov V, Jacek Namieśnik J (2016) Modeling of pharmaceuticals mixtures toxicity with deviation ratio and best-fit functions models. Sci Total Environ 571:259–268. https://doi.org/10.1016/j.scitotenv.2016.07.186
Xue L, Bajorath J (2000) Molecular descriptors for effective classification of biologically active compounds based on principal component analysis identified by a genetic algorithm. J Chem Inf Comput Sci 40(3):801–809. https://doi.org/10.1021/ci000322m
Yi H, Yan M, Huang D, Zeng G, Lai C, Li M, Huo X, Qin L, Liu S, Liu X, Li B, Wang H, Shen M, Fu Y, Guo X (2019) Synergistic effect of artificial enzyme and 2D nano-structured Bi2WO6 for eco-friendly and efficient biomimetic photocatalysis. Appl Catal B Environ 250:52–62. https://doi.org/10.1016/j.apcatb.2019.03.008
Zeng YL, Wang L, Jiang L, Cai XY, Li Y (2015) Joint toxicity of lead, chromium, cobalt and nickel to Photobacterium phosphoreum at no observed effect concentration. Bull Environ Contam Toxicol 95(2):260–264. https://doi.org/10.1007/s00128-015-1568-7
Zhang J, Ding TT, Dong XQ, Bian ZQ (2018) Time-dependent and Pb-dependent antagonism and synergism towards Vibrio qinghaiensis sp.-Q67 within heavy metal mixtures. RSC Adv 8(46):26089–26098. https://doi.org/10.1039/c8ra04191a
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The authors would like to acknowledge the financial support of the Croatian Science Foundation through projects Modeling of Environmental Aspects of Advanced Water Treatment for Degradation of Priority Pollutants (MEAoWT; IP-2014-09-7992) and Advanced Water Treatment Technologies for Microplastics Removal (AdWaTMiR; IP-2019-04-9661).
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Sigurnjak Bureš, M., Cvetnić, M., Miloloža, M. et al. Modeling the toxicity of pollutants mixtures for risk assessment: a review. Environ Chem Lett 19, 1629–1655 (2021). https://doi.org/10.1007/s10311-020-01107-5
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DOI: https://doi.org/10.1007/s10311-020-01107-5