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Publikationstyp
Wissenschaftlicher Artikel
Erscheinungsjahr
2022
Development of new QSAR models for water, sediment, and soil half-life
Development of new QSAR models for water, sediment, and soil half-life
Autor:innen
Herausgeber
Quelle
The Science of the Total Environment
838, Part1 (2022), Heft 156004
838, Part1 (2022), Heft 156004
Schlagwörter
Persistenz, QSAR-Modell, REACH-System
Zitation
LOMBARDO, Anna, Jürgen ARNING und Alberto MANGANARO, 2022. Development of new QSAR models for water, sediment, and soil half-life. The Science of the Total Environment [online]. 2022. Bd. 838, Part1 (2022), Heft 156004. DOI 10.60810/openumwelt-593. Verfügbar unter: https://openumwelt.de/handle/123456789/2420
Zusammenfassung englisch
Checking the persistence of a chemical in the environment is extremely important. Regulations like REACH, the European one on chemicals, require the measurements or estimates of the half-life of the chemical in water, sediment, and soil. The use of non-testing methods, like quantitative structure-activity relationship (QSAR) models, is encouraged because it reduces costs and time. To our knowledge, there are very few freely available models for these properties and some are for specific chemical classes. Here, we present three new semi-quantitative models, one for each of the required environmental compartments (water, sediment, and soil). Using literature and REACH registration data, we developed three new counter-propagation artificial neural network models using the CPANNatNIC tool. We calculated the VEGA descriptors, and selected the relevant ones using an internal method in R based on the forward selection technique. The best model for each compartment was implemented in two open-source stand-alone tools, the VEGA platform, and the JANUS tool (https://www.vegahub.eu/). These models were also used by ECHA to build their PBT profiler available in the OECD QSAR toolbox (https://qsartoolbox.org/). Screening and prioritization are also our main target. The models perform well, with R2 always above 0.8 in training and validation. The only exception is the validation set of the soil compartment, with R2 0.68, that is above 0.8 only for compounds inside the applicability domain (automatically calculated by the system). The root mean square error (RMSE) is good, 0.34 or less in log units (again, for soil validation it is higher but it reaches 0.21 when considering only the compounds in the applicability domain). Compared with one of the most widely used tools, BIOWIN3, the proposed models give better results in terms of R2 and RMSE. For the classification, the performance is better for water and soil, and comparable or lower for sediment. © 2022 Elsevier