Repositori institucional URV
| URV's Author/s: | Kumar, Vikas |
| Author, as appears in the article.: | Tariq, Farina; Ahrens, Lutz; Alygizakis, Nikiforos A; Audouze, Karine; Benfenati, Emilio; Carvalho, Pedro N; Chelcea, Ioana; Karakitsios, Spyros; Karakoltzidis, Achilleas; Kumar, Vikas; Mora Lagares, Liadys; Sarigiannis, Dimosthenis; Selvestrel, Gianluca; Taboureau, Olivier; Vorkamp, Katrin; Andersson, Patrik L |
| Author's mail: | vikas.kumar@urv.cat |
| Author identifier: | 0000-0002-9795-5967 |
| Journal publication year: | 2024 |
| Publication Type: | Journal Publications |
| APA: | Tariq, Farina; Ahrens, Lutz; Alygizakis, Nikiforos A; Audouze, Karine; Benfenati, Emilio; Carvalho, Pedro N; Chelcea, Ioana; Karakitsios, Spyros; Kara (2024). Computational Tools to Facilitate Early Warning of New Emerging Risk Chemicals. Toxics, 12(10), 736-. DOI: 10.3390/toxics12100736 |
| Papper original source: | Toxics. 12 (10): 736- |
| Abstract: | Innovative tools suitable for chemical risk assessment are being developed in numerous domains, such as non-target chemical analysis, omics, and computational approaches. These methods will also be critical components in an efficient early warning system (EWS) for the identification of potentially hazardous chemicals. Much knowledge is missing for current use chemicals and thus computational methodologies complemented with fast screening techniques will be critical. This paper reviews current computational tools, emphasizing those that are accessible and suitable for the screening of new and emerging risk chemicals (NERCs). The initial step in a computational EWS is an automatic and systematic search for NERCs in literature and database sources including grey literature, patents, experimental data, and various inventories. This step aims at reaching curated molecular structure data along with existing exposure and hazard data. Next, a parallel assessment of exposure and effects will be performed, which will input information into the weighting of an overall hazard score and, finally, the identification of a potential NERC. Several challenges are identified and discussed, such as the integration and scoring of several types of hazard data, ranging from chemical fate and distribution to subtle impacts in specific species and tissues. To conclude, there are many computational systems, and these can be used as a basis for an integrated computational EWS workflow that identifies NERCs automatically. |
| Article's DOI: | 10.3390/toxics12100736 |
| Link to the original source: | https://www.mdpi.com/2305-6304/12/10/736 |
| Papper version: | info:eu-repo/semantics/publishedVersion |
| licence for use: | https://creativecommons.org/licenses/by/3.0/es/ |
| Department: | Enginyeria Química |
| Licence document URL: | https://repositori.urv.cat/ca/proteccio-de-dades/ |
| Thematic Areas: | Chemical health and safety Environmental sciences Health, toxicology and mutagenesis Toxicology |
| Keywords: | Artificial intelligence (ai) Computational toxicology Early warning system (ews) Ecotoxicity Effect assessmen Effect assessment Exposure Exposure assessment Framework Model Multimedia New and emerging risk chemicals (nercs) Pollutio Prediction Qsar Risk assessment Substances Toxicity Water |
| Entity: | Universitat Rovira i Virgili |
| Record's date: | 2024-11-16 |
| Search your record at: |   |
| File | Description | Format | |
|---|---|---|---|
| DocumentPrincipal | DocumentPrincipal | application/pdf |
© 2011 Universitat Rovira i Virgili