Εξειδίκευση τύπου :	Άρθρο σε επιστημονικό περιοδικό
Τίτλος:	Computational Tools to Facilitate Early Warning of New Emerging Risk Chemicals
Δημιουργός/Συγγραφέας:	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 [EL] Σαρηγιάννης, Δημοσθένης[EN] Sarigiannis, Dimosthenis Selvestrel, Gianluca Taboureau, Olivier Vorkamp, Katrin Andersson, Patrik L
Ημερομηνία:	2024-10-12
Γλώσσα:	Αγγλικά
ISSN:	2305-6304
DOI:	10.3390/toxics12100736
Άλλο:	39453156
Περίληψη:	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.
Τίτλος πηγής δημοσίευσης:	Toxics
Τόμος/Κεφάλαιο:	12
Τεύχος:	10
Θεματική Κατηγορία:	[EL] Χημεία[EN] Chemistry [EL] Τοξικολογία[EN] Toxicology. Poisons
Λέξεις-Κλειδιά:	QSAR artificial intelligence (AI) computational toxicology early warning system (EWS) effect assessment exposure assessment new and emerging risk chemicals (NERCs) risk assessment
EU Grant:	PARC Horizon Europe (HORIZON)
EU Grant identifier:	101057014 HORIZON-HLTH-2021-ENVHLTH-03HE
Ηλεκτρονική διεύθυνση στον εκδότη (link):	https://doi.org/10.3390/toxics12100736
Εμφανίζεται στις συλλογές:	Άλλες δράσεις