Modelling biodegradability based on OECD 301D data for the design of mineralising ionic liquids

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Modelling biodegradability based on OECD 301D data for the design of mineralising ionic liquids. / Amsel, Ann Kathrin; Chakravarti, Suman; Olsson, Oliver et al.
In: Green Chemistry , Vol. 26, No. 12, 22.05.2024, p. 7363-7376.

Research output: Journal contributionsJournal articlesResearchpeer-review

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@article{3e4a7410ac4d401494a73de24e8e6e3e,
title = "Modelling biodegradability based on OECD 301D data for the design of mineralising ionic liquids",
abstract = "Ionic liquids (ILs) are increasingly used, e.g. as solvents, electrolytes, active pharmaceutical ingredients and herbicides. If ILs enter the environment due to their use or accidental spills at industry sites, they can pollute the environment. To avoid adverse side effects of persistent ILs in the environment, they should be designed to fully mineralise in the environment after they fulfilled their function during application. (Quantitative) structure-biodegradability relationship models ((Q)SBRs) have been successfully applied in the design of benign chemicals. However, (Q)SBR models have not been widely applied to design mineralising ILs. Therefore, in this study we developed five quantitative structure-biodegradability relationship (QSBR) models based on OECD 301D data from the literature and our own in-house biodegradation experiments. These models can potentially be part of a test battery for designing fully mineralising ILs to increase the overall reliability of the biodegradability assessment and reduce uncertainties. Two datasets were formed and randomly divided into a training set with 233 and 321 compounds and a test set with 26 and 36 compounds, respectively. Both classification and regression models were built using molecular fragments with the aim to predict the classification and continuous biodegradation rate, respectively. The internal and external validations produced a R2 of 0.620-0.854 for the regression models and accuracy, true positive rate, and true negative rate were between 62 and 100% for the classification models indicating an adequate performance but also a need for improvement. For the models and the test battery presented in this study, further research is needed to demonstrate their applicability.",
keywords = "Chemistry",
author = "Amsel, {Ann Kathrin} and Suman Chakravarti and Oliver Olsson and Klaus K{\"u}mmerer",
note = "Publisher Copyright: {\textcopyright} 2024 The Royal Society of Chemistry.",
year = "2024",
month = may,
day = "22",
doi = "10.1039/d4gc00889h",
language = "English",
volume = "26",
pages = "7363--7376",
journal = "Green Chemistry ",
issn = "1463-9262",
publisher = "Royal Society of Chemistry",
number = "12",

}

RIS

TY - JOUR

T1 - Modelling biodegradability based on OECD 301D data for the design of mineralising ionic liquids

AU - Amsel, Ann Kathrin

AU - Chakravarti, Suman

AU - Olsson, Oliver

AU - Kümmerer, Klaus

N1 - Publisher Copyright: © 2024 The Royal Society of Chemistry.

PY - 2024/5/22

Y1 - 2024/5/22

N2 - Ionic liquids (ILs) are increasingly used, e.g. as solvents, electrolytes, active pharmaceutical ingredients and herbicides. If ILs enter the environment due to their use or accidental spills at industry sites, they can pollute the environment. To avoid adverse side effects of persistent ILs in the environment, they should be designed to fully mineralise in the environment after they fulfilled their function during application. (Quantitative) structure-biodegradability relationship models ((Q)SBRs) have been successfully applied in the design of benign chemicals. However, (Q)SBR models have not been widely applied to design mineralising ILs. Therefore, in this study we developed five quantitative structure-biodegradability relationship (QSBR) models based on OECD 301D data from the literature and our own in-house biodegradation experiments. These models can potentially be part of a test battery for designing fully mineralising ILs to increase the overall reliability of the biodegradability assessment and reduce uncertainties. Two datasets were formed and randomly divided into a training set with 233 and 321 compounds and a test set with 26 and 36 compounds, respectively. Both classification and regression models were built using molecular fragments with the aim to predict the classification and continuous biodegradation rate, respectively. The internal and external validations produced a R2 of 0.620-0.854 for the regression models and accuracy, true positive rate, and true negative rate were between 62 and 100% for the classification models indicating an adequate performance but also a need for improvement. For the models and the test battery presented in this study, further research is needed to demonstrate their applicability.

AB - Ionic liquids (ILs) are increasingly used, e.g. as solvents, electrolytes, active pharmaceutical ingredients and herbicides. If ILs enter the environment due to their use or accidental spills at industry sites, they can pollute the environment. To avoid adverse side effects of persistent ILs in the environment, they should be designed to fully mineralise in the environment after they fulfilled their function during application. (Quantitative) structure-biodegradability relationship models ((Q)SBRs) have been successfully applied in the design of benign chemicals. However, (Q)SBR models have not been widely applied to design mineralising ILs. Therefore, in this study we developed five quantitative structure-biodegradability relationship (QSBR) models based on OECD 301D data from the literature and our own in-house biodegradation experiments. These models can potentially be part of a test battery for designing fully mineralising ILs to increase the overall reliability of the biodegradability assessment and reduce uncertainties. Two datasets were formed and randomly divided into a training set with 233 and 321 compounds and a test set with 26 and 36 compounds, respectively. Both classification and regression models were built using molecular fragments with the aim to predict the classification and continuous biodegradation rate, respectively. The internal and external validations produced a R2 of 0.620-0.854 for the regression models and accuracy, true positive rate, and true negative rate were between 62 and 100% for the classification models indicating an adequate performance but also a need for improvement. For the models and the test battery presented in this study, further research is needed to demonstrate their applicability.

KW - Chemistry

UR - http://www.scopus.com/inward/record.url?scp=85194902610&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/97c62723-ebad-32e0-a083-1951df9ed63f/

U2 - 10.1039/d4gc00889h

DO - 10.1039/d4gc00889h

M3 - Journal articles

AN - SCOPUS:85194902610

VL - 26

SP - 7363

EP - 7376

JO - Green Chemistry

JF - Green Chemistry

SN - 1463-9262

IS - 12

ER -

DOI