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Title: Use of the molecular connectivity index to predict chemical biotransfer

Abstract

Chemicals released into the environment can pose a danger to organisms if exposure occurs. In order to assess the level of risk, it is necessary to first determine if a chemical is capable of biotransfer from a given environmental medium into a particular biological system. Experimental determination of biotransfer factors (BTF), defined as the ratio of the concentration of a chemical in an organism or tissue to that in the exposure medium, is usually difficult, expensive, and time consuming. Since an accurate measurement of BTF is crucial to exposure and risk assessment, it would be advantageous if BTF could be estimated from a chemical property that is quantifiable with high precision. The molecular connectivity index (MCI) is such a chemical property, which in theory encodes information about molecular size, branching, cyclization, saturation, and heteroatom content. MCI`s are readily obtainable from chemical structure and the periodic table, requiring no experimental measurement. The results indicate a strong correlation between the MCI and BTF values for animal tissue, milk, and vegetation. Using MCI to estimate BTF could provide a faster, more cost effective, and more accurate method for predicting chemical biotransfer.

Authors:
; ;  [1]
  1. Univ. of California, Davis, CA (United States). Dept. of Environmental Toxicology
Publication Date:
OSTI Identifier:
40018
Report Number(s):
CONF-9410273-
TRN: IM9520%%213
Resource Type:
Conference
Resource Relation:
Conference: 15. annual meeting of the Society of Environmental Toxicology and Chemistry (SETAC), Denver, CO (United States), 30 Oct - 3 Nov 1994; Other Information: PBD: 1994; Related Information: Is Part Of Society of Environmental Toxicology and Chemistry 15th annual meeting: Abstract book. Ecological risk: Science, policy, law, and perception; PB: 286 p.
Country of Publication:
United States
Language:
English
Subject:
56 BIOLOGY AND MEDICINE, APPLIED STUDIES; TOXIC MATERIALS; BIOLOGICAL ACCUMULATION; ANIMAL TISSUES; SENSITIVITY; PLANT TISSUES; MILK; CONTAMINATION; FOOD CHAINS; FORECASTING

Citation Formats

Dowdy, D L, McKone, T E, and Hsieh, D P.H. Use of the molecular connectivity index to predict chemical biotransfer. United States: N. p., 1994. Web.
Dowdy, D L, McKone, T E, & Hsieh, D P.H. Use of the molecular connectivity index to predict chemical biotransfer. United States.
Dowdy, D L, McKone, T E, and Hsieh, D P.H. 1994. "Use of the molecular connectivity index to predict chemical biotransfer". United States.
@article{osti_40018,
title = {Use of the molecular connectivity index to predict chemical biotransfer},
author = {Dowdy, D L and McKone, T E and Hsieh, D P.H.},
abstractNote = {Chemicals released into the environment can pose a danger to organisms if exposure occurs. In order to assess the level of risk, it is necessary to first determine if a chemical is capable of biotransfer from a given environmental medium into a particular biological system. Experimental determination of biotransfer factors (BTF), defined as the ratio of the concentration of a chemical in an organism or tissue to that in the exposure medium, is usually difficult, expensive, and time consuming. Since an accurate measurement of BTF is crucial to exposure and risk assessment, it would be advantageous if BTF could be estimated from a chemical property that is quantifiable with high precision. The molecular connectivity index (MCI) is such a chemical property, which in theory encodes information about molecular size, branching, cyclization, saturation, and heteroatom content. MCI`s are readily obtainable from chemical structure and the periodic table, requiring no experimental measurement. The results indicate a strong correlation between the MCI and BTF values for animal tissue, milk, and vegetation. Using MCI to estimate BTF could provide a faster, more cost effective, and more accurate method for predicting chemical biotransfer.},
doi = {},
url = {https://www.osti.gov/biblio/40018}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Sat Dec 31 00:00:00 EST 1994},
month = {Sat Dec 31 00:00:00 EST 1994}
}

Conference:
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