An automated toxicity based prioritization framework for fast chemical characterization in non-targeted analysis

Identification of environmental pollutants with harmful effects is commonly conducted by non-targeted analysis (NTA) using liquid chromatography coupled with high-resolution mass spectrometry. Prioritization of possible candidates is important yet challenging because of the large number of candidate...

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Main Authors: Yang, Junjie, Zhao, Fanrong, Zheng, Jie, Wang, Yulan, Fei, Xunchang, Xiao, Yongjun, Fang, Mingliang
Other Authors: School of Civil and Environmental Engineering
Format: Article
Language:English
Published: 2023
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Online Access:https://hdl.handle.net/10356/170040
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1700402023-08-22T06:06:50Z An automated toxicity based prioritization framework for fast chemical characterization in non-targeted analysis Yang, Junjie Zhao, Fanrong Zheng, Jie Wang, Yulan Fei, Xunchang Xiao, Yongjun Fang, Mingliang School of Civil and Environmental Engineering Lee Kong Chian School of Medicine (LKCMedicine) Singapore Phenome Center Engineering::Environmental engineering Non-targeted Analysis Automatic Prioritization Identification of environmental pollutants with harmful effects is commonly conducted by non-targeted analysis (NTA) using liquid chromatography coupled with high-resolution mass spectrometry. Prioritization of possible candidates is important yet challenging because of the large number of candidates from MS acquisitions. We aimed to prioritize candidates to the exposure potential of organic chemicals by their toxicity and identification evidence in the matrix. We have developed an R package application, "NTAprioritization.R", for fast prioritization of suspect lists. In this workflow, the identification levels of candidates were first rated according to spectral matching and retention time prediction. The toxicity levels were rated according to candidates' toxicity of different endpoints or ToxPi score. Finally, the various levels of candidates were identified as Tier 1 - 5 descending in priority. For validation, we used this workflow to identify pollutants in a sludge water sample spiked with 28 environmental pollutants. The workflow reduced the candidate list of over 6,982 candidates to a final list of 2,779 compounds and prioritized them to 5 tiers (Tier 1 - 5), including 21 out of 28 spiked standards. Overall, this study shows the added value of an automated prioritization R package for the fast screening of environmental pollutants based on the NTA method. 2023-08-22T06:06:50Z 2023-08-22T06:06:50Z 2023 Journal Article Yang, J., Zhao, F., Zheng, J., Wang, Y., Fei, X., Xiao, Y. & Fang, M. (2023). An automated toxicity based prioritization framework for fast chemical characterization in non-targeted analysis. Journal of Hazardous Materials, 448, 130893-. https://dx.doi.org/10.1016/j.jhazmat.2023.130893 0304-3894 https://hdl.handle.net/10356/170040 10.1016/j.jhazmat.2023.130893 36746086 2-s2.0-85147581782 448 130893 en Journal of Hazardous Materials © 2023 Elsevier B.V. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Environmental engineering
Non-targeted Analysis
Automatic Prioritization
spellingShingle Engineering::Environmental engineering
Non-targeted Analysis
Automatic Prioritization
Yang, Junjie
Zhao, Fanrong
Zheng, Jie
Wang, Yulan
Fei, Xunchang
Xiao, Yongjun
Fang, Mingliang
An automated toxicity based prioritization framework for fast chemical characterization in non-targeted analysis
description Identification of environmental pollutants with harmful effects is commonly conducted by non-targeted analysis (NTA) using liquid chromatography coupled with high-resolution mass spectrometry. Prioritization of possible candidates is important yet challenging because of the large number of candidates from MS acquisitions. We aimed to prioritize candidates to the exposure potential of organic chemicals by their toxicity and identification evidence in the matrix. We have developed an R package application, "NTAprioritization.R", for fast prioritization of suspect lists. In this workflow, the identification levels of candidates were first rated according to spectral matching and retention time prediction. The toxicity levels were rated according to candidates' toxicity of different endpoints or ToxPi score. Finally, the various levels of candidates were identified as Tier 1 - 5 descending in priority. For validation, we used this workflow to identify pollutants in a sludge water sample spiked with 28 environmental pollutants. The workflow reduced the candidate list of over 6,982 candidates to a final list of 2,779 compounds and prioritized them to 5 tiers (Tier 1 - 5), including 21 out of 28 spiked standards. Overall, this study shows the added value of an automated prioritization R package for the fast screening of environmental pollutants based on the NTA method.
author2 School of Civil and Environmental Engineering
author_facet School of Civil and Environmental Engineering
Yang, Junjie
Zhao, Fanrong
Zheng, Jie
Wang, Yulan
Fei, Xunchang
Xiao, Yongjun
Fang, Mingliang
format Article
author Yang, Junjie
Zhao, Fanrong
Zheng, Jie
Wang, Yulan
Fei, Xunchang
Xiao, Yongjun
Fang, Mingliang
author_sort Yang, Junjie
title An automated toxicity based prioritization framework for fast chemical characterization in non-targeted analysis
title_short An automated toxicity based prioritization framework for fast chemical characterization in non-targeted analysis
title_full An automated toxicity based prioritization framework for fast chemical characterization in non-targeted analysis
title_fullStr An automated toxicity based prioritization framework for fast chemical characterization in non-targeted analysis
title_full_unstemmed An automated toxicity based prioritization framework for fast chemical characterization in non-targeted analysis
title_sort automated toxicity based prioritization framework for fast chemical characterization in non-targeted analysis
publishDate 2023
url https://hdl.handle.net/10356/170040
_version_ 1779156561114955776