독성발현경로(Adverse Outcome Pathway)를 활용한 In Silico 예측기술 연구동향 분석
Trend of In Silico Prediction Research Using Adverse Outcome Pathway
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Background: The increasing need to minimize animal testing has sparked interest in alternative methods with more humane, cost-effective, and time-saving attributes. In particular, in silico -based computational toxicology is gaining prominence. Adverse outcome pathway (AOP) is a biological map depicting toxicological mechanisms, composed of molecular initiating events (MIEs), key events (KEs), and adverse outcomes (AOs). To understand toxicological mechanisms, predictive models are essential for AOP components in computational toxicology, including molecular structures. Objectives: This study reviewed the literature and investigated previous research cases related to AOP and in silico methodologies. We describe the results obtained from the analysis, including predictive techniques and approaches that can be used for future in silico -based alternative methods to animal testing using AOP. Methods: We analyzed in silico methods and databases used in the literature to identify trends in research on in silico prediction models. Results: We reviewed 26 studies related to AOP and in silico methodologies. The ToxCast/Tox21 database was commonly used for toxicity studies, and MIE was the most frequently used predictive factor among the AOP components. Machine learning was most widely used among prediction techniques, and various in silico methods, such as deep learning, molecular docking, and molecular dynamics, were also utilized. Conclusions: We analyzed the current research trends regarding in silico -based alternative methods for animal testing using AOPs. Developing predictive techniques that ref lect toxicological mechanisms will be essential to replace animal testing with in silico methods. In the future, since the applicability of various predictive techniques is increasing, it will be necessary to continue monitoring the trend of predictive techniques and in silico -based approaches.
Abstract
Background: The increasing need to minimize animal testing has sparked interest in alternative methods with more humane, cost-effective, and time-saving attributes. In particular, in silico -based computational toxicology is gaining prominence. Adverse outcome pathway (AOP) is a biological map depicting toxicological mechanisms, composed of molecular initiating events (MIEs), key events (KEs), and adverse outcomes (AOs). To understand toxicological mechanisms, predictive models are essential for AOP components in computational toxicology, including molecular structures. Objectives: This study reviewed the literature and investigated previous research cases related to AOP and in silico methodologies. We describe the results obtained from the analysis, including predictive techniques and approaches that can be used for future in silico -based alternative methods to animal testing using AOP. Methods: We analyzed in silico methods and databases used in the literature to identify trends in research on in silico prediction models. Results: We reviewed 26 studies related to AOP and in silico methodologies. The ToxCast/Tox21 database was commonly used for toxicity studies, and MIE was the most frequently used predictive factor among the AOP components. Machine learning was most widely used among prediction techniques, and various in silico methods, such as deep learning, molecular docking, and molecular dynamics, were also utilized. Conclusions: We analyzed the current research trends regarding in silico -based alternative methods for animal testing using AOPs. Developing predictive techniques that ref lect toxicological mechanisms will be essential to replace animal testing with in silico methods. In the future, since the applicability of various predictive techniques is increasing, it will be necessary to continue monitoring the trend of predictive techniques and in silico -based approaches.
참고문헌 (56)
- Reddy N, "Alternatives to animal testing in toxicity testing : current status and future perspectives in food safety assessments", Food Chem Toxicol, 179, pp.113944~ (2023)
- Russell WM, "The principles of humane experimental technique" (1959), Methuen
- "Animal replacement test"[URL]
- Doke SK, "Alternatives to animal testing : a review", Saudi Pharm J, 23(3), pp.223~229 (2015)
- "A study on structural alerts and application of (Q)SARs for mutagenicity screening"[URL]
- "Study on the improvement of genotoxicity prediction using QSARs models"[URL]
- Lee S, "Trends in research on alternative to animal experiments for safety evaluation of cosmetic ingredients", Korean Soc Cosmet Cosmetol, 13(2), pp.359~368 (2023)
- "Guidelines for submitting QSAR data for industry"[URL]
- European Chemical Agency (ECHA), "Guidance for the identification of endocrine disruptors in the context of Regulations (EU) No 528", 2012and (EC) No 1107, EFSA J, pp.2018 (2009), 16(6)[DOI]
- "A study for the improvement of a prediction using QSAR models to find out hazardous substances(I)"[URL]
- Kang D, "Applicability of QSAR models for acute aquatic toxicity under the act on registration, evaluation, etc. of chemicals in the Republic of Korea", J Environ Health Sci, 48(3), pp.159~166 (2022)
- Jeong J, "Artificial intelligence-based toxicity prediction of environmental chemicals : future directions for chemical management applications", Environ Sci Technol, 56(12), pp.7532~7543 (2022)
- Organisation for Economic Co-operation and Development (OECD), "Users’ handbook supplement to the guidance document for developing and assessing adverse outcome pathways" (2016), OECD Publishing
- Lee I, "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", J Environ Health Sci, 47(5), pp.462~471 (2021)
- Tan H, "Development, validation, and application of a human reproductive toxicity prediction model based on adverse outcome pathway", Environ Sci Technol, 56(17), pp.12391~12403 (2022)
- Troger F, "Identification of mitochondrial toxicants by combined in silico and in vitro studies-a structure-based view on the adverse outcome pathway", Comput Toxicol, 14, pp.100123~ (2020)
- Gadaleta D, "Prediction of the neurotoxic potential of chemicals based on modelling of molecular initiating events upstream of the adverse outcome pathways of(developmental)neurotoxicity", Int J Mol Sci, 23(6), pp.3053~ (2022)
- Wang CC, "SkinSens-DB : a curated database for skin sensitization assays", J Cheminform, 9, pp.5~ (2017)
- Allen TE, "Computational approaches for predicting molecular initiating events", Toxicol Lett, 295(Suppl 1), pp.S99~S100 (2018)
- Gadaleta D, "QSAR modeling of ToxCast assays relevant to the molecular initiating events of aops leading to hepatic steatosis", J Chem Inf Model, 58(8), pp.1501~1517 (2018)
- Allen TE, "Gaining insight into toxicity predicting machine learning algorithms", Toxicol Lett, 314, pp.S280~ (2019)
- Kurosaki K, "A toxicity prediction tool for potential agonist", antagonist activities in molecular initiating events based on chemical structures, 2020, pp.21(21), 7853~
- Uesawa Y, "AI-based QSAR modeling for prediction of active compounds in MIE", AOP, 2020, pp.140(4), 499~505
- Matsuzaka Y, "Molecular image-based prediction models of nuclear receptor agonists and antagonists using the DeepSnap-Deep Learning approach with the Tox21 10K library", Molecules, 25(12), pp.2764~ (2020)
- Allen TEH, "Quantitative predictions for molecular initiating events using three-dimensional quantitative structure-activity relationships", Chem Res Toxicol, 33(2), pp.324~332 (2020)
- Ivanova H, "Modeling hazard assessment of chemicals based on adducts formation. I. A basis for inclusion of kinetic factors in simulating skin sensitization", Comput Toxicol, 15, pp.100130~ (2020)
- Morger A, "KnowTox : pipeline and case study for confident prediction of potential toxic effects of compounds in early phases of development", J Cheminform, 12(1), pp.24~ (2020)
- Garcia de Lomana M, "In silico models to predict the perturbation of molecular initiating events related to thyroid hormone homeostasis", Chem Res Toxicol, 34(2), pp.396~411 (2021)
- Ciallella HL, "Revealing adverse outcome pathways from public high-throughput screening data to evaluate new toxicants by a knowledge-based deep neural network approach", Environ Sci Technol, 55(15), pp.10875~10887 (2021)
- Jagiello K, "Transcriptomics-based and AOP-informed structure-activity relationships to predict pulmonary pathology induced by multiwalled carbon nanotubes", Small, 17(15), pp.e2003465~ (2021)
- Bringezu F, "Ensemble prediction of mitochondrial toxicity using machine learning technology", Comput Toxicol, 20, pp.100189~ (2021)
- Jeon B, "A development of a graph-based ensemble machine learning model for skin sensitization hazard and potency assessment", J Appl Toxicol, 42(11), pp.1832~1842 (2022)
- Spînu N, "Probabilistic modelling of developmental neurotoxicity based on a simplified adverse outcome pathway network", Comput Toxicol, 21, pp.100206~ (2022)
- Guan R, "The adverse outcome pathway(AOP)of estrogen interference effect induced by triphenyl phosphate(TPP) : integrated multi-omics and molecular dynamics approaches", Ecotoxicol Environ Saf, 234, pp.113387~ (2022)
- Sakhteman A, "EDTox : an R Shiny application to predict the endocrine disruption potential of compounds", Bioinformatics, 38(7), pp.2066~2069 (2022)
- Jin Y, "High throughput data-based, toxicity pathway-oriented development of a quantitative adverse outcome pathway network linking AHR activation to lung damages", J Hazard Mater, 425, pp.128041~ (2022)
- Paini A, "Towards a qAOP framework for predictive toxicology-linking data to decisions", Comput Toxicol, 21, pp.100195~ (2022)
- Belfield SJ, "A review of quantitative structure-activity relationship modelling approaches to predict the toxicity of mixtures", Comput Toxicol, 25, pp.100251~ (2023)
- Romano JD, "Exploring genetic influences on adverse outcome pathways using heuristic simulation and graph data science", Comput Toxicol, 25, pp.100261~ (2023)
- Bahl A, "PROTEOMAS : a workflow enabling harmonized proteomic metaanalysis and proteomic signature mapping", J Cheminform, 15(1), pp.34~ (2023)
- Kim S, "Research needs for mixture risk assessment and use of the mixture allocation factor (MAF)", J Environ Health Sci, 49(6), pp.i~ii (2023)
- Kim S, "Toxicity of organophosphorus flame retardants(OPFRs)and their mixtures in Aliivibrio fischeri and human hepatocyte HepG2", J Environ Health Sci, 49(2), pp.89~98 (2023)
- Kim MI, "Environmental health practice", pp.1523~1541 (2023), Shinkwang
- "ToxCast dashboard"[URL]
- "Tox21: toxicology in the 21st Century"[URL]
- Rotroff DM, "Using in vitro high throughput screening assays to identify potential endocrine-disrupting chemicals", Environ Health Perspect, 121(1), pp.7~14 (2013)
- Wang XH, "Relationship between acute and chronic toxicity for prevalent organic pollutants in Vibrio fischeri based upon chemical mode of action", J Hazard Mater, 338, pp.458~465 (2017)
- Breiman L, "Random forests", Mach Learn, 45, pp.5~32 (2001)
- Cortes C, "Support-vector networks", Mach Learn, 20, pp.273~297 (1995)
- Hinton GE, "A fast learning algorithm for deep belief nets", Neural Comput, 18(7), pp.1527~1554 (2006)
- Lecun Y, "Gradient-based learning applied to document recognition", Proc IEEE, 86(11), pp.2278~2324 (1998)
- Uesawa Y, "Quantitative structure-activity relationship analysis using deep learning based on a novel molecular image input technique", Bioorg Med Chem Lett, 28(20), pp.3400~3403 (2018)
- Mayr A, "DeepTox : toxicity prediction using deep learning", Front Environ Sci, 3, pp.80~ (2016)
- Jain S, "Large-scale modeling of multispecies acute toxicity end points using consensus of multitask deep learning methods", J Chem Inf Model, 61(2), pp.653~663 (2021)
- Tu W, "Permethrin is a potential thyroid-disrupting chemical : in vivo and in silico envidence", Aquat Toxicol, 175, pp.39~46 (2016)