DOI QR코드

DOI QR Code

화평법에 따른 급성 수생독성 예측을 위한 QSAR 모델의 활용 가능성 연구

Applicability of QSAR Models for Acute Aquatic Toxicity under the Act on Registration, Evaluation, etc. of Chemicals in the Republic of Korea

  • 강동진 (국립환경과학원 환경건강연구부 위해성평가연구과 화학물질등록평가팀) ;
  • 장석원 (국립환경과학원 환경건강연구부 위해성평가연구과 화학물질등록평가팀) ;
  • 이시원 (국립환경과학원 환경건강연구부 위해성평가연구과 화학물질등록평가팀) ;
  • 이재현 (국립환경과학원 환경건강연구부 위해성평가연구과 화학물질등록평가팀) ;
  • 이상희 (국립환경과학원 환경건강연구부 위해성평가연구과 화학물질등록평가팀) ;
  • 김필제 (국립환경과학원 환경건강연구부 위해성평가연구과 화학물질등록평가팀) ;
  • 정현미 (국립환경과학원 환경건강연구부 위해성평가연구과 화학물질등록평가팀) ;
  • 성창호 (국립환경과학원 환경건강연구부 위해성평가연구과 화학물질등록평가팀)
  • Kang, Dongjin (Chemicals Registration & Evaluation Team, Risk Assessment Division, Environmental Health Research Department, National Institute of Environmental Research) ;
  • Jang, Seok-Won (Chemicals Registration & Evaluation Team, Risk Assessment Division, Environmental Health Research Department, National Institute of Environmental Research) ;
  • Lee, Si-Won (Chemicals Registration & Evaluation Team, Risk Assessment Division, Environmental Health Research Department, National Institute of Environmental Research) ;
  • Lee, Jae-Hyun (Chemicals Registration & Evaluation Team, Risk Assessment Division, Environmental Health Research Department, National Institute of Environmental Research) ;
  • Lee, Sang Hee (Chemicals Registration & Evaluation Team, Risk Assessment Division, Environmental Health Research Department, National Institute of Environmental Research) ;
  • Kim, Pilje (Chemicals Registration & Evaluation Team, Risk Assessment Division, Environmental Health Research Department, National Institute of Environmental Research) ;
  • Chung, Hyen-Mi (Chemicals Registration & Evaluation Team, Risk Assessment Division, Environmental Health Research Department, National Institute of Environmental Research) ;
  • Seong, Chang-Ho (Chemicals Registration & Evaluation Team, Risk Assessment Division, Environmental Health Research Department, National Institute of Environmental Research)
  • 투고 : 2022.05.20
  • 심사 : 2022.06.08
  • 발행 : 2022.06.30

초록

Background: A quantitative structure-activity relationship (QSAR) model was adopted in the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH, EU) regulations as well as the Act on Registration, Evaluation, etc. of Chemicals (AREC, Republic of Korea). It has been previously used in the registration of chemicals. Objectives: In this study, we investigated the correlation between the predicted data provided by three prediction programs using a QSAR model and actual experimental results (acute fish, daphnia magna toxicity). Through this approach, we aimed to effectively conjecture on the performance and determine the most applicable programs when designating toxic substances through the AREC. Methods: Chemicals that had been registered and evaluated in the Toxic Chemicals Control Act (TCCA, Republic of Korea) were selected for this study. Two prediction programs developed and operated by the U.S. EPA - the Ecological Structure-Activity Relationship (ECOSAR) and Toxicity Estimation Software Tool (T.E.S.T.) models - were utilized along with the TOPKAT (Toxicity Prediction by Komputer Assisted Technology) commercial program. The applicability of these three programs was evaluated according to three parameters: accuracy, sensitivity, and specificity. Results: The prediction analysis on fish and daphnia magna in the three programs showed that the TOPKAT program had better sensitivity than the others. Conclusions: Although the predictive performance of the TOPKAT program when using a single predictive program was found to perform well in toxic substance designation, using a single program involves many restrictions. It is necessary to validate the reliability of predictions by utilizing multiple methods when applying the prediction program to the regulation of chemicals.

키워드

과제정보

본 논문은 환경부의 재원으로 국립환경과학원의 지원을 받아 수행하였습니다(NIER-2019-01-01-012).

참고문헌

  1. Ministry of Environment. Toxic Chemicals Control Act, Rep. Sejong: Ministry of Environment; 2012.
  2. Ministry of Environment. Act on Registration, Evaluation, ETC. of Chemicals. Sejong: Ministry of Environment; 2018.
  3. Pizzo F, Lombardo A, Manganaro A, Benfenati E. In silico models for predicting ready biodegradability under REACH: a comparative study. Sci Total Environ. 2013; 463-464: 161-168. https://doi.org/10.1016/j.scitotenv.2013.05.060
  4. Dearden J. Prediction of environmental toxicity and fate using quantitative structure-activity relationships (QSARs). J Braz Chem Soc. 2002; 13(6): 754-762. https://doi.org/10.1590/S0103-50532002000600005
  5. Ock HS. Developing trend of QSAR modeling and pesticides. Korean J Pestic Sci. 2011; 15(1): 68-85.
  6. Lozano S, Lescot E, Halm MP, Lepailleur A, Bureau R, Rault S. Prediction of acute toxicity in fish by using QSAR methods and chemical modes of action. J Enzyme Inhib Med Chem. 2010; 25(2): 195-203. https://doi.org/10.3109/14756360903169857
  7. Kim J, Seo J, Kim T, Kim HK, Park S, Kim PJ. Prediction of human health and ecotoxicity of chemical substances using the OECD QSAR application toolbox. J Environ Health Sci. 2013; 39(2): 130-137. https://doi.org/10.5668/JEHS.2013.39.2.130
  8. de Roode D, Hoekzema C, de Vries-Buitenweg S, van de Waart B, van der Hoeven J. QSARs in ecotoxicological risk assessment. Regul Toxicol Pharmacol. 2006; 45(1): 24-35. https://doi.org/10.1016/j.yrtph.2006.01.012
  9. Khan K, Khan PM, Lavado G, Valsecchi C, Pasqualini J, Baderna D, et al. QSAR modeling of Daphnia magna and fish toxicities of biocides using 2D descriptors. Chemosphere. 2019; 229: 8-17. Erratum in: Chemosphere. 2019; 237: 124397. https://doi.org/10.1016/j.chemosphere.2019.04.204
  10. US EPA. Ecological Structure-Activity Relationships Program (ECOSAR) Methodology Document v2.0. Available: https://epa.gov/tsca-screening-tools/ecological-structure-activity-relationships-program-ecosar-methodology-document [accessed 8 April 2019].
  11. Melnikov F, Kostal J, Voutchkova-Kostal A, Zimmerman JB, Anastas PT. Assessment of predictive models for estimating the acute aquatic toxicity of organic chemicals. Green Chem. 2016; 18: 4432-4445. https://doi.org/10.1039/c6gc00720a
  12. Golbamaki A, Cassano A, Lombardo A, Moggio Y, Colafranceschi M, Benfenati E. Comparison of in silico models for prediction of Daphnia magna acute toxicity. SAR QSAR Environ Res. 2014; 25(8): 673-694. https://doi.org/10.1080/1062936x.2014.923041
  13. Kim HK, Kim JY, Park MY, Sung CH, Doo YK, Ki PJ. A Study on Structural Alerts and Application of (Q)SARs for Mutagenicity Screening. Incheon: National Institute of Environmental Research; 2011.
  14. Lee JW, Park S, Jang SW, Lee S, Moon S, Kim H, et al. Toxicity prediction using three quantitative structure-activity relationship(QSAR) programs (TOPKAT®, Derek®, OECD toolbox). J Environ Health Sci. 2019; 45(5): 457-464. https://doi.org/10.5668/JEHS.2019.45.5.457
  15. BIOVIA. Predictive Toxicology in Discovery Studio, Accelrys. Available: http://www.accelrys.com [accessed 12 April 2019].
  16. Netzeva T, Worth A. Classification of Phthalates According to Their (Q)SAR Predicted Acute Toxicity to Fish: A Case Study. Ispra: European Commission Directorate General Joint Research Centre; 2007.
  17. Rorije E, Loonen H, Muller M, Klopman G, Peijnenburg WJ. Evaluation and application of models for the prediction of ready biodegradability in the MITI-I test. Chemosphere. 1999; 38(6): 1409-1417. https://doi.org/10.1016/S0045-6535(98)00543-8
  18. Sung CH, Park SY, Choi YS, Kim HK, Sung MH, Moon SA, et al. Study on the Improvement of Genotoxicity Prediction Using QSARs Models. Incheon: National Institute of Environmental Research; 2017.
  19. Tunkel J, Mayo K, Austin C, Hickerson A, Howard P. Practical considerations on the use of predictive models for regulatory purposes. Environ Sci Technol. 2005; 39(7): 2188-2199. https://doi.org/10.1021/es049220t
  20. US EPA. User's Guide for T.E.S.T. 2016, Ver.4.2. Available: https://www.epa.gov/chemical-research/toxicity-estimation-software-tool-test [accessed 8 April 2019].
  21. Moore DR, Breton RL, MacDonald DB. A comparison of model performance for six quantitative structure-activity relationship packages that predict acute toxicity to fish. Environ Toxicol Chem. 2003; 22(8): 1799-1809. https://doi.org/10.1897/00-361
  22. Cappelli CI, Cassano A, Golbamaki A, Moggio Y, Lombardo A, Colafranceschi M, et al. Assessment of in silico models for acute aquatic toxicity towards fish under REACH regulation. SAR QSAR Environ Res. 2015; 26(12): 977-999. https://doi.org/10.1080/1062936X.2015.1104519
  23. Burden N, Maynard SK, Weltje L, Wheeler JR. The utility of QSARs in predicting acute fish toxicity of pesticide metabolites: a retrospective validation approach. Regul Toxicol Pharmacol. 2016; 80: 241-246. https://doi.org/10.1016/j.yrtph.2016.05.032
  24. Talapatra SN, Konar S. Predictive acute toxicity comparison in Daphnia magna for common organic chemicals present in cosmetics by using two QSAR modeling softwares. World Sci News. 2016; 42: 101-118.
  25. Schuurmann G, Ebert RU, Kuhne R. Quantitative read-across for predicting the acute fish toxicity of organic compounds. Environ Sci Technol. 2011; 45(10): 4616-4622. https://doi.org/10.1021/es200361r
  26. Sheffield TY, Judson RS. Ensemble QSAR modeling to predict multispecies fish toxicity lethal concentrations and points of departure. Environ Sci Technol. 2019; 53(21): 12793-12802. https://doi.org/10.1021/acs.est.9b03957
  27. Ferrari T, Lombardo A, Benfenati E. QSARpy: a new flexible algorithm to generate QSAR models based on dissimilarities. The log Kow case study. Sci Total Environ. 2018; 637-638: 1158-1165. https://doi.org/10.1016/j.scitotenv.2018.05.072
  28. Zuriaga E, Giner B, Valero MS, Gomez M, Garcia CB, Lomba L. QSAR modelling for predicting the toxic effects of traditional and derived biomass solvents on a Danio rerio biomodel. Chemosphere. 2019; 227: 480-488. https://doi.org/10.1016/j.chemosphere.2019.04.054
  29. Di Marzio W, Saenz ME. Quantitative structure-activity relationship for aromatic hydrocarbons on freshwater fish. Ecotoxicol Environ Saf. 2004; 59(2): 256-262. https://doi.org/10.1016/j.ecoenv.2003.11.006
  30. Schultz TW. Relative toxicity of para-substituted phenols: log KOW and pKa-dependent structure-activity relationships. Bull Environ Contam Toxicol. 1987; 38(6): 994-999. https://doi.org/10.1007/BF01609086
  31. Reuschenbach P, Silvani M, Dammann M, Warnecke D, Knacker T. ECOSAR model performance with a large test set of industrial chemicals. Chemosphere. 2008; 71(10): 1986-1995. https://doi.org/10.1016/j.chemosphere.2007.12.006
  32. Hrovat M, Segner H, Jeram S. Variability of in vivo fish acute toxicity data. Regul Toxicol Pharmacol. 2009; 54(3): 294-300. https://doi.org/10.1016/j.yrtph.2009.05.013
  33. Devillers J, Mombelli E, Samsera R. Structural alerts for estimating the carcinogenicity of pesticides and biocides. SAR QSAR Environ Res. 2011; 22(1-2): 89-106. https://doi.org/10.1080/1062936X.2010.548349
  34. Cronin MT. (Q)SARs to predict environmental toxicities: current status and future needs. Environ Sci Process Impacts. 2017; 19(3): 213-220. https://doi.org/10.1039/C6EM00687F
  35. Pradeep P, Povinelli RJ, White S, Merrill SJ. An ensemble model of QSAR tools for regulatory risk assessment. J Cheminform. 2016; 8: 48. https://doi.org/10.1186/s13321-016-0164-0
  36. Kim KY, Shin SE, No KT. Assessment of quantitative structure-activity relationship of toxicity prediction models for Korean chemical substance control legislation. Environ Health Toxicol. 2015; 30 Suppl: s2015007.
  37. Kim HK, Kim JY, Cha HK, Park MY, Sung CH, Kim PJ. Study on the Best Application of (Q)SARs to Predict Aquatic Toxicity of Organic Chemicals. Incheon: National Institute of Environmental Research; 2010.
  38. Sung CH, Park SY, Kim KT, Kim KH, Sung MH, Moon SA, et al. Study on the Improvement of Mutagenicity Prediction Using QSAR Models. Incheon: National Institute of Environmental Research; 2016.
  39. Sung CH, Park SY, Lee JW, Kim PJ, Yu SD, Sung MH, et al. A Study for the Improvement of a Prediction Using QSAR Models to Find Out Hazardous Substances(I). Incheon: National Institute of Environmental Research; 2018.
  40. Kim J, Choi K, Kim K, Kim D. QSAR approach for toxicity prediction of chemicals used in electronics industries. J Environ Health Sci. 2014; 40(2): 105-113. https://doi.org/10.5668/JEHS.2014.40.2.105