PubMed Health⌕ Search

Biomedical subjects

Patrik L Andersson

Publications and source records attributed to Patrik L Andersson.

3 recordsLinked to original sources

Multivariate physicochemical characterisation and quantitative structure-property relationship modelling of polybrominated diphenyl ethers.

Levels of polybrominated diphenyl ethers (PBDEs) are increasing in the environment and may cause long-term environmental problems. Developing a model describing the chemical variation among the 209 possible congeners would be a useful step in any systematic approach for assessing the fate and risk posed by the PBDEs. Therefore, 40 physicochemical descriptors were derived for all PBDEs using a semi-empirical method (AM1), molecular mechanics, and empirically estimated parameters. Descriptors included heats of formation, frontier molecular orbital energies, atomic charges, dipole moments, logP values, and molecular surface areas. Principal component analysis (PCA) was used to evaluate the descriptors. The first four PCs, explaining 76% of the variation in the data, described the size, charge distribution and symmetric elements of the congeners. A quantitative structure-activity relationship model was constructed based on data for dioxin-like activity (using the luciferase bioassay) for 17 PBDEs with the partial least squares method. In addition, quantitative structure-property relationship models for gas chromatographic relative retention times on four capillary columns were developed. These models proved suitable to assist in the identification of untested PBDEs. Based on the results of the PCA, a factorial design was applied for selecting 21 representative congeners. PBDEs 11, 13, 17, 32, 35, 47, 53, 77, 85, 99, 119, 135, 153, 155, 156, 169, 176, 181, 190, 192, and 209. The spacing of these congeners in the physicochemical domain maximises the coverage of key factors such as molecular size and substitution pattern. Consideration of the selected congeners should be useful for guiding the synthesis of new compounds for use in future studies of the fate and biological effects of PBDEs.

Biological Assay↗

General and class specific models for prediction of soil sorption using various physicochemical descriptors.

Diverse chemical descriptors were explored for use in QSAR models aimed to screen the soil sorption potential of organic compounds. The descriptors included logP, HyperChem QSARProperties descriptors, a combination of connectivity indices, geometrical, and quantum chemical measures, and two sets from the DRAGON and CODESSA program packages, respectively. Generally, the univariate logP models were capable of capturing most of the variation and give an indication of the sorption potential. The multivariate models required refined variable selection procedures but were shown to include crucial descriptors for modeling compound classes with specific chemical characteristics.

Models, Chemical↗

QSPR treatment of the soil sorption coefficients of organic pollutants.

In this study, general and class-specific QSPR models for soil sorption, logK(OC), of 344 organic pollutants (0 < logK(OC) < 4.94) were developed using a large variety of theoretical molecular descriptors based only on molecular structure. Two general models were obtained. The first model was derived for a structurally representative set of 68 chemicals (R2=0.76, s=0.44), whereas the second involved a total of 344 compounds (R2=0.76, s=0.41). The first was validated using the data for the remaining 276 pollutants (R2=0.70, s=0.45). An additional validation of both models was performed using an independent set of 48 pollutants. Both models predict the logK(OC) at the level of experimental precision, while the theoretical molecular descriptors appearing in the QSPR models give further insight into the mechanisms of soil sorption. The analysis of the distribution of the residuals of the logK(OC) values calculated by both general models indicated the need and possible advantages of modeling soil sorption for smaller data sets related to individual classes of chemicals. Accordingly, QSPR models were also developed for 14 chemical classes. The descriptors appearing in these models were discussed as related to the possible interaction mechanisms in soil sorption.

Journal Article↗