Journal of Environmental Health Sciences (한국환경보건학회지)

Korean Society of Environmental Health (한국환경보건학회)
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Trend of In Silico Prediction Research Using Adverse Outcome Pathway

독성발현경로(Adverse Outcome Pathway)를 활용한 In Silico 예측기술 연구동향 분석

  • Sujin Lee (Chemical Analysis Center, Korea Institute of Chemical Technology (KRICT)) ;
  • Jongseo Park (Chemical Analysis Center, Korea Institute of Chemical Technology (KRICT)) ;
  • Sunmi Kim (Chemical Analysis Center, Korea Institute of Chemical Technology (KRICT)) ;
  • Myungwon Seo (Chemical Analysis Center, Korea Institute of Chemical Technology (KRICT))
  • 이수진 (한국화학연구원 화학분석센터) ;
  • 박종서 (한국화학연구원 화학분석센터) ;
  • 김선미 (한국화학연구원 화학분석센터) ;
  • 서명원 (한국화학연구원 화학분석센터)
  • Received : 2024.04.02
  • Accepted : 2024.04.24
  • Published : 2024.04.30
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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 reflect 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.

Keywords

Acknowledgement

This study was supported by Korea Environmental Industry & Technology Institute (KEITI; Project no. RS-2023-00215857). We are grateful to Jiwon Choi at Korea Research Institute of Chemical Technology for assistance with data collection.

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