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Toxics
Volume 9
Issue 3
10.3390/toxics9030059
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Article

Novel QSAR Models for Molecular Initiating Event Modeling in Two Intersecting Adverse Outcome Pathways Based Pulmonary Fibrosis Prediction for Biocidal Mixtures

by
Myungwon Seo
1,
Chong Hak Chae
2,
Yuno Lee
3,
Ha Ryong Kim
4,* and
Jongwoon Kim
1,*
1
Chemical Safety Research Center, Korea Research Institute of Chemical Technology, Daejeon 34114, Korea
2
Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, Daejeon 34114, Korea
3
Drug Information Platform Center, Korea Research Institute of Chemical Technology, Daejeon 34114, Korea
4
College of Pharmacy, Daegu Catholic University, Gyeongsan 38430, Korea
*
Authors to whom correspondence should be addressed.
Academic Editor: Martí Nadal
Toxics 2021, 9(3), 59; https://doi.org/10.3390/toxics9030059
Received: 8 December 2020 / Revised: 19 February 2021 / Accepted: 12 March 2021 / Published: 16 March 2021
(This article belongs to the Section Risk Assessment and Management)
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Abstract

The adverse outcome pathway (AOP) was introduced as an alternative method to avoid unnecessary animal tests. Under the AOP framework, an in silico methods, molecular initiating event (MIE) modeling is used based on the ligand-receptor interaction. Recently, the intersecting AOPs (AOP 347), including two MIEs, namely peroxisome proliferator-activated receptor-gamma (PPAR-γ) and toll-like receptor 4 (TLR4), associated with pulmonary fibrosis was proposed. Based on the AOP 347, this study developed two novel quantitative structure-activity relationship (QSAR) models for the two MIEs. The prediction performances of different MIE modeling methods (e.g., molecular dynamics, pharmacophore model, and QSAR) were compared and validated with in vitro test data. Results showed that the QSAR method had high accuracy compared with other modeling methods, and the QSAR method is suitable for the MIE modeling in the AOP 347. Therefore, the two QSAR models based on the AOP 347 can be powerful models to screen biocidal mixture related to pulmonary fibrosis. View Full-Text
Keywords: adverse outcome pathway network; molecular initiating event; biocide; mixture toxicity; pulmonary fibrosis; quantitative structure–activity relationship adverse outcome pathway network; molecular initiating event; biocide; mixture toxicity; pulmonary fibrosis; quantitative structure–activity relationship
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MDPI and ACS Style

Seo, M.; Chae, C.H.; Lee, Y.; Kim, H.R.; Kim, J. Novel QSAR Models for Molecular Initiating Event Modeling in Two Intersecting Adverse Outcome Pathways Based Pulmonary Fibrosis Prediction for Biocidal Mixtures. Toxics 2021, 9, 59. https://doi.org/10.3390/toxics9030059

AMA Style

Seo M, Chae CH, Lee Y, Kim HR, Kim J. Novel QSAR Models for Molecular Initiating Event Modeling in Two Intersecting Adverse Outcome Pathways Based Pulmonary Fibrosis Prediction for Biocidal Mixtures. Toxics. 2021; 9(3):59. https://doi.org/10.3390/toxics9030059

Chicago/Turabian Style

Seo, Myungwon, Chong H. Chae, Yuno Lee, Ha R. Kim, and Jongwoon Kim. 2021. "Novel QSAR Models for Molecular Initiating Event Modeling in Two Intersecting Adverse Outcome Pathways Based Pulmonary Fibrosis Prediction for Biocidal Mixtures" Toxics 9, no. 3: 59. https://doi.org/10.3390/toxics9030059

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