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An Empirical approach to sufficient similarity : Combining exposure data and mixtures toxicology data

Registro MARC
Tag12Valor
LDR  00000cab a2200000 4500
001  MAP20130037745
003  MAP
005  20131118121133.0
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24503‎$a‎An Empirical approach to sufficient similarity‎$b‎: Combining exposure data and mixtures toxicology data‎$c‎Scott Marshall...[et.al]
520  ‎$a‎When assessing risks posed by environmental chemical mixtures, whole mixture approaches are preferred to component approaches. When toxicological data on whole mixtures as they occur in the environment are not available, Environmental Protection Agency guidance states that toxicity data from a mixture considered sufficiently similar to the environmental mixture can serve as a surrogate. We propose a novel method to examine whether mixtures are sufficiently similar, when exposure data and mixture toxicity study data from at least one representative mixture are available. We define sufficient similarity using equivalence testing methodology comparing the distance between benchmark dose estimates for mixtures in both data-rich and data-poor cases. We construct a similar mixtures risk indicator(SMRI) (analogous to the hazard index) on sufficiently similar mixtures linking exposure data with mixtures toxicology data. The methods are illustrated using pyrethroid mixtures occurrence data collected in child care centers (CCC) and dose-response data examining acute neurobehavioral effects of pyrethroid mixtures in rats.
7730 ‎$w‎MAP20077000345‎$t‎Risk analysis : an international journal‎$d‎McLean, Virginia : Society for Risk Analysis, 1987-2015‎$x‎0272-4332‎$g‎02/09/2013 Volumen 33 Número 9 - septiembre 2013
856  ‎$y‎MÁS INFORMACIÓN‎$u‎mailto:centrodocumentacion@fundacionmapfre.org?subject=Consulta%20de%20una%20publicaci%C3%B3n%20&body=Necesito%20m%C3%A1s%20informaci%C3%B3n%20sobre%20este%20documento%3A%20%0A%0A%5Banote%20aqu%C3%AD%20el%20titulo%20completo%20del%20documento%20del%20que%20desea%20informaci%C3%B3n%20y%20nos%20pondremos%20en%20contacto%20con%20usted%5D%20%0A%0AGracias%20%0A