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Quantitative structure‐activity relationships for kinetic parameters of polycyclic aromatic hydrocarbon biotransformation
Author(s) -
DimitriouChristidis Petros,
Autenrieth Robin L.,
Abraham Michael H.
Publication year - 2008
Publication title -
environmental toxicology and chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 171
eISSN - 1552-8618
pISSN - 0730-7268
DOI - 10.1897/07-498.1
Subject(s) - quantitative structure–activity relationship , biotransformation , sphingomonas paucimobilis , chemistry , polycyclic aromatic hydrocarbon , molecular descriptor , biological system , environmental chemistry , computational chemistry , organic chemistry , stereochemistry , biology , bacteria , genetics , enzyme
Quantitative structure‐activity relationships (QSARs) were developed for three Monod‐type parameters— q max , K S , and q max / K S —that express the kinetics of polycyclic aromatic hydrocarbon (PAH) biotransformation by Sphingomonas paucimobilis strain EPA505. The training sets contained high‐quality experimental values of the kinetic parameters for 20 unsubstituted and methylated PAHs as well as values of 41 meaningful molecular descriptors. A genetic function approximation algorithm was used to develop the QSARs. Statistical evaluation of the developed QSARs showed that the relationships are statistically significant and satisfy the assumptions of linear‐regression analysis. The Organization for Economic Cooperation and Development principles for (Q)SAR validation were followed to evaluate the developed QSARs, which showed that the QSARs are valid. The QSARs contain spatial, spatial and electronic, topological, and thermodynamic molecular descriptors. Whereas spatial descriptors were essential in explaining biotransformation kinetics, electronic descriptors were not. Mechanistic interpretation of the QSARs resulted in evidence that is consistent with the hypothesis of membrane transport as being the rate‐limiting process in PAH biotransformation by strain EPA505. The present study demonstrates the value of QSAR not only as a predictive tool but also as a framework for understanding the mechanisms governing biodegradation at the molecular level.