In-silico prediction of dislodgeable foliar residues and regulatory implications for plant protection products.
Dermal Exposure
Dislodgeable Foliar Residues (DFR) Prediction
In-silico Model
Post-application Exposure
Random Forest
Worker Exposure
Journal
Journal of exposure science & environmental epidemiology
ISSN: 1559-064X
Titre abrégé: J Expo Sci Environ Epidemiol
Pays: United States
ID NLM: 101262796
Informations de publication
Date de publication:
27 Apr 2024
27 Apr 2024
Historique:
received:
09
11
2023
accepted:
10
04
2024
revised:
08
04
2024
medline:
28
4
2024
pubmed:
28
4
2024
entrez:
27
4
2024
Statut:
aheadofprint
Résumé
When experimentally determined dislodgeable foliar residue (DFR) values are not available, regulatory agencies use conservative default DFR values as a first-tier approach to assess post-application dermal exposures to plant protection products (PPPs). These default values are based on a limited set of field studies, are very conservative, and potentially overestimate exposures from DFRs. Use Random Forest to develop classification and regression-type ensemble models to predict DFR values after last application (DFR0) by considering experimentally-based variability due to differences in physical and chemical properties of PPPs, agronomic practices, crop type, and climatic conditions. Random Forest algorithm was used to develop in-silico ensemble DFR0 prediction models using more than 100 DFR studies from Corteva Agriscience The proposed ensemble models demonstrated 98% prediction accuracy that if a DFR0 is predicted to be less than the European Food Safety Authority (EFSA) default DFR0 value of 3 µg/cm This manuscript is expected to have significant impact globally as it provides: A framework for incorporating in silico DFR data into worker exposure assessment, A roadmap for a tiered approach for conducting re-entry exposure assessment, and A proof of concept for using existing DFR data to provide a read-across framework that can easily be harmonized across all regulatory agencies to provide more robust assessments for PPP exposures.
Sections du résumé
BACKGROUND
BACKGROUND
When experimentally determined dislodgeable foliar residue (DFR) values are not available, regulatory agencies use conservative default DFR values as a first-tier approach to assess post-application dermal exposures to plant protection products (PPPs). These default values are based on a limited set of field studies, are very conservative, and potentially overestimate exposures from DFRs.
OBJECTIVE
OBJECTIVE
Use Random Forest to develop classification and regression-type ensemble models to predict DFR values after last application (DFR0) by considering experimentally-based variability due to differences in physical and chemical properties of PPPs, agronomic practices, crop type, and climatic conditions.
METHODS
METHODS
Random Forest algorithm was used to develop in-silico ensemble DFR0 prediction models using more than 100 DFR studies from Corteva Agriscience
RESULTS
RESULTS
The proposed ensemble models demonstrated 98% prediction accuracy that if a DFR0 is predicted to be less than the European Food Safety Authority (EFSA) default DFR0 value of 3 µg/cm
IMPACT STATEMENT
UNASSIGNED
This manuscript is expected to have significant impact globally as it provides: A framework for incorporating in silico DFR data into worker exposure assessment, A roadmap for a tiered approach for conducting re-entry exposure assessment, and A proof of concept for using existing DFR data to provide a read-across framework that can easily be harmonized across all regulatory agencies to provide more robust assessments for PPP exposures.
Identifiants
pubmed: 38678132
doi: 10.1038/s41370-024-00675-w
pii: 10.1038/s41370-024-00675-w
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s).
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