Quantitative structure-activity relationships for the toxicity of organophosphorus and carbamate pesticides to the Rainbow trout Onchorhyncus mykiss

This study has investigated the development of quantitative structure–activity relationships (QSARs) for the toxicity to rainbow trout Onchorhyncus mykiss Walbaum of 75 organophosphorus and carbamate pesticides. The toxicity data were obtained from an openly available toxicological database and were...

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Bibliographic Details
Published in:Pest management science 2006-09, Vol.62 (9), p.819-831
Main Authors: Bermúdez-Saldaña, José M, Cronin, Mark TD
Format: Article
Language:English
Subjects:
Agnatha. Pisces
Animal, plant and microbial ecology
Animals
Applied ecology
Biological and medical sciences
carbamate
Carbamate pesticides
Carbamates - chemistry
Carbamates - classification
Carbamates - toxicity
Cholinesterase Inhibitors - chemistry
Cholinesterase Inhibitors - toxicity
Ecotoxicology, biological effects of pollution
Effects of pollution and side effects of pesticides on vertebrates
Fundamental and applied biological sciences. Psychology
Linear Models
Multivariate Analysis
Onchorhyncus mykiss
Oncorhynchus mykiss
organophosphorus
Organophosphorus Compounds - chemistry
Organophosphorus Compounds - classification
Organophosphorus Compounds - toxicity
pesticide
Pesticide toxicity
Pesticides
Pesticides - chemistry
Pesticides - classification
Pesticides - toxicity
Quantitative Structure-Activity Relationship
quantitative structure-activity relationships
rainbow trout
Regression analysis
Risk Assessment
Toxicity
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Summary:This study has investigated the development of quantitative structure–activity relationships (QSARs) for the toxicity to rainbow trout Onchorhyncus mykiss Walbaum of 75 organophosphorus and carbamate pesticides. The toxicity data were obtained from an openly available toxicological database and were selected to be representative of a single endpoint. A large number of physicochemical and structural descriptors were calculated for the pesticides. QSAR models were developed using multiple linear regression and partial least‐squares analyses. Following the removal of a small number of outliers, predictive QSARs were developed on small numbers of mechanistically relevant descriptors. Applying mechanistic knowledge to the development of QSAR further improved predictivity. Copyright © 2006 Society of Chemical Industry
ISSN:1526-498X
1526-4998
DOI:10.1002/ps.1233