[논문]화학물질 독성 빅데이터와 심층학습 모델을 활용한 내분비계 장애물질 선별 방법-세정제품과 세탁제품을 중심으로

이인혜; 이수진; 지경희

doi:10.5668/jehs.2021.47.5.462

화학물질 독성 빅데이터와 심층학습 모델을 활용한 내분비계 장애물질 선별 방법-세정제품과 세탁제품을 중심으로
A Screening Method to Identify Potential Endocrine Disruptors Using Chemical Toxicity Big Data and a Deep Learning Model with a Focus on Cleaning and Laundry Products 원문보기

韓國環境保健學會誌 = Journal of environmental health sciences, v.47 no.5, 2021년, pp.462 - 471

이인혜 (용인대학교 자연과학연구소) , 이수진 (용인대학교 일반대학원 환경보건학과) , 지경희 (용인대학교 일반대학원 환경보건학과)

Abstract ▼ AI-Helper

Background: The number of synthesized chemicals has rapidly increased over the past decade. For many chemicals, there is a lack of information on toxicity. With the current movement toward reducing animal testing, the use of toxicity big data and deep learning could be a promising tool to screen potential toxicants. Objectives: This study identified potential chemicals related to reproductive and estrogen receptor (ER)-mediated toxicities for 1135 cleaning products and 886 laundry products. Methods: We listed chemicals contained in cleaning and laundry products from a publicly available database. Then, chemicals that potentially exhibited reproductive and ER-mediated toxicities were identified using the European Union Classification, Labeling and Packaging classification and ToxCast database, respectively. For chemicals absent from the ToxCast database, ER activity was predicted using deep learning models. Results: Among the 783 listed chemicals, there were 53 with potential reproductive toxicity and 310 with potential ER-mediated toxicity. Among the 473 chemicals not tested with ToxCast assays, deep learning models indicated that 42 chemicals exhibited ER-mediated toxicity. A total of 13 chemicals were identified as causing reproductive toxicity by reacting with the ER. Conclusions: We demonstrated a screening method to identify potential chemicals related to reproductive and ER-mediated toxicities utilizing chemical toxicity big data and deep learning. Integrating toxicity data from in vivo, in vitro, and deep learning models may contribute to screening chemicals in consumer products.

주제어

표/그림 (7)

그림 Fig. 1. Workflow for screening potential chemicals related to reproductive and estrogen receptor (ER)-mediated toxicities using EU CLP classification, ToxCast database, and deep learning models
그림 Fig. 2. Number of household chemical products and ingredients. (A) Number of cleaning and laundry products after data curation, (B) Number of ingredients after matching with CAS number, (C) Number of ingredients classified as reproductive toxicants from EU CLP classification, (D) Number of ingredients with active ER data from ToxCast, (E) Number of ingredients with active ER data from deep learning model
표 Table 1. Top fifteen most frequently occurred chemicals in cleaning and laundry products, synonyms appearing in product ingredient lists, and ingredient usage
표 Table 2. Number of active and inactive chemicals based on 18 ToxCast in vitro assays
표 Table 3. Description of deep learning models based on the ToxCast assays
표 Table 4. Potential assays related to reproductive toxicity analyzed by phi-coefficient correlation
표 Table 5. Substances that are positive for both reproductive toxicity and ER-mediated toxicity among target chemicals

참고문헌 (38)

Lee D, Lim H, Kim JH, Kim T, Hwang M, Seok K, et al. An investigation of consumer product co-use patterns - focusing on air-fresheners and deodorizer. J Environ Health Sci. 2018; 44(3): 275-282.
Gabb HA, Blake C. An informatics approach to evaluating combined chemical exposures from consumer products: a case study of asthma-associated chemicals and potential endocrine disruptors. Environ Health Perspect. 2016; 124(8): 1155-1165.

상세보기
Sanderson H, Greggs W, Cowan-Ellsberry C, DeLeo P, Sedlak R. Collection and dissemination of exposure data throughout the chemical value chain: a case study from a global consumer product industry. Hum Ecol Risk Assess Int J. 2013; 19(4): 999-1013.

상세보기
Park JW. The role of environmental law in assessing and managing risk of chemical products and biocides. Environ Law Policy. 2018; 20: 55-85.

상세보기
Heo DA, Huh EH, Park JY, Moon KW, Lee K. An investigation of ingredients and hazardous substances in some consumer products - focusing on cleaners and disinfectants. J Environ Health Sci. 2015; 41(5): 314-326.

원문보기 상세보기
Heo JJ, Kim UJ, Oh JE. Simultaneous quantitative analysis of four isothiazolinones and 3-iodo-2-propynyl butyl carbamate in hygienic consumer products. Environ Eng Res. 2019; 24(1): 137-143.

원문보기 상세보기
Pyun DH, Kim YW, Hwang YW, Lee SY, Park SH. A hazard assessment of chemicals in liquid synthetic detergent using Green-Screen. Korean J Hazard Mater. 2021; 9(1): 30-40.

상세보기
Park JY, Lim M, Lee K, Ji K, Yang W, Shin HS, et al. Consumer exposure and risk assessment to selected chemicals of mold stain remover use in Korea. J Expo Sci Environ Epidemiol. 2020; 30(5): 888-897.

상세보기
Gore AC, Chappell VA, Fenton SE, Flaws JA, Nadal A, Prins GS, et al. EDC-2: the endocrine society's second scientific statement on endocrine-disrupting chemicals. Endocr Rev. 2015; 36(6): E1-E150.

상세보기
Dodson RE, Nishioka M, Standley LJ, Perovich LJ, Brody JG, Rudel RA. Endocrine disruptors and asthma-associated chemicals in consumer products. Environ Health Perspect. 2012; 120(7): 935-943.

상세보기
Rotroff DM, Dix DJ, Houck KA, Knudsen TB, Martin MT, McLaurin KW, et al. Using in vitro high throughput screening assays to identify potential endocrine-disrupting chemicals. Environ Health Perspect. 2013; 121(1): 7-14.

상세보기
United States Environmental Protection Agency. ToxCast Dashboard. Available: https://comptox.epa.gov/dashboard/chemical_lists/toxcast [accessed 20 August 2021].
National Institute of Environmental Health Sciences. National Toxicology Program. Tox21: Toxicology in the 21st Century. Available: https://ntp.niehs.nih.gov/whatwestudy/tox21/index.html [accessed 20 August 2021].
Judson RS, Magpantay FM, Chickarmane V, Haskell C, Tania N, Taylor J, et al. Integrated model of chemical perturbations of a biological pathway using 18 in vitro high-throughput screening assays for the estrogen receptor. Toxicol Sci. 2015; 148(1): 137-154.

상세보기
Andersson N, Arena M, Auteri D, Barmaz S, Grignard E, Kienzler A, et al. Guidance for the identification of endocrine disruptors in the context of Regulations (EU) No 528/2012 and (EC) No 1107/2009. EFSA J. 2018; 16(6): e05311.
To KT, Fry RC, Reif DM. Characterizing the effects of missing data and evaluating imputation methods for chemical prioritization applications using ToxPi. BioData Min. 2018; 11: 10.

상세보기
Jerez JM, Molina I, Garcia-Laencina PJ, Alba E, Ribelles N, Martin M, et al. Missing data imputation using statistical and machine learning methods in a real breast cancer problem. Artif Intell Med. 2010; 50(2): 105-115.

상세보기
Tang W, Chen J, Wang Z, Xie H, Hong H. Deep learning for predicting toxicity of chemicals: a mini review. J Environ Sci Health C Environ Carcinog Ecotoxicol Rev. 2018; 36(4): 252-271.

상세보기
Jeong J, Garcia-Reyero N, Burgoon L, Perkins E, Park T, Kim C, et al. Development of adverse outcome pathway for PPAR γ antagonism leading to pulmonary fibrosis and chemical selection for its validation: ToxCast database and a deep learning artificial neural network model-based approach. Chem Res Toxicol. 2019; 32(6): 1212-1222.

상세보기
Jeong J, Choi J. Development of AOP relevant to microplastics based on toxicity mechanisms of chemical additives using ToxCast TM and deep learning models combined approach. Environ Int. 2020; 137: 105557.

상세보기
Ministry of Environment. Living Environment Safety Information System. Available: https://ecolife.me.go.kr/ecolife [accessed 20 August 2021].
National Institute of Environmental Research. National Chemicals Information System. Available: https://ncis.nier.go.kr [accessed 20 August 2021].
European Chemicals Agency. Information on Chemicals. Available: https://echa.europa.eu/information-on-chemicals [accessed 20 August 2021].
United States Environmental Protection Agency. CompTox Chemicals Dashboard. Available: https://comptox.epa.gov/dashboard [accessed 20 August 2021].
National Library of Medicine. PubChem. Available: https://pubchem.ncbi.nlm.nih.gov/ [accessed 20 August 2021].
Liu J, Mansouri K, Judson RS, Martin MT, Hong H, Chen M, et al. Predicting hepatotoxicity using ToxCast in vitro bioactivity and chemical structure. Chem Res Toxicol. 2015; 28(4): 738-751.

상세보기
Jeong J, Bae SY, Choi J. Identification of toxicity pathway of diesel particulate matter using AOP of PPARγ inactivation leading to pulmonary fibrosis. Environ Int. 2021; 147: 106339.

상세보기
Idakwo G, Thangapandian S, Luttrell J, Li Y, Wang N, Zhou Z, et al. Structure-activity relationship-based chemical classification of highly imbalanced Tox21 datasets. J Cheminform. 2020; 12(1): 66.

상세보기
RDKit. RDKit: Open-Source Cheminformatics Software. Available: http://www.rdkit.org [accessed 20 August 2021].
Perez-Enciso M, Zingaretti LM. A guide for using deep learning for complex trait genomic prediction. Genes (Basel). 2019; 10(7): 553.

상세보기
Akoglu H. User's guide to correlation coefficients. Turk J Emerg Med. 2018; 18(3): 91-93.

상세보기
Mukherjee A, Su A, Rajan K. Deep learning model for identifying critical structural motifs in potential endocrine disruptors. J Chem Inf Model. 2021; 61(5): 2187-2197.

상세보기
Burgoon LD. Autoencoder Predicting Estrogenic Chemical Substances (APECS): an improved approach for screening potentially estrogenic chemicals using in vitro assays and deep learning. Comput Toxicol. 2017; 2: 45-49.

상세보기
Zhang Z, Jia C, Hu Y, Sun L, Jiao J, Zhao L, et al. The estrogenic potential of salicylate esters and their possible risks in foods and cosmetics. Toxicol Lett. 2012; 209(2): 146-153.

상세보기
Bickers DR, Calow P, Greim HA, Hanifin JM, Rogers AE, Saurat JH, et al. The safety assessment of fragrance materials. Regul Toxicol Pharmacol. 2003; 37(2): 218-273.

상세보기
Audrain H, Kenward C, Lovell CR, Green C, Ormerod AD, Sansom J, et al. Allergy to oxidized limonene and linalool is frequent in the U.K. Br J Dermatol. 2014; 171(2): 292-297.

상세보기
Gunia-Krzyzak A, Sloczynska K, Popiol J, Koczurkiewicz P, Marona H, Pekala E. Cinnamic acid derivatives in cosmetics: current use and future prospects. Int J Cosmet Sci. 2018; 40(4): 356-366.

상세보기
Howes MJ, Houghton PJ, Barlow DJ, Pocock VJ, Milligan SR. Assessment of estrogenic activity in some common essential oil constituents. J Pharm Pharmacol. 2002; 54(11): 1521-1528.

상세보기

저자의 다른 논문 :

표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format