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Issue 3, 2017
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3D-QSAR predictions for bovine serum albumin–water partition coefficients of organic anions using quantum mechanically based descriptors

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Abstract

Ionic organic chemicals are a class of chemicals that is released in the environment in a large amount from anthropogenic sources. Among various chemical and biological processes, binding to serum albumin is particularly relevant for the toxicokinetic behavior of ionic chemicals. Several experimental studies showed that steric effects have a crucial influence on the sorption to bovine serum albumin (BSA). In this study, we investigated whether a 3D quantitative structure–activity relationship (3D-QSAR) model can accurately account for these steric effects by predicting the BSA–water partition coefficients (KBSA/water) of neutral and anionic organic chemicals. The 3D-QSAR tested here uses quantum mechanically derived local sigma profiles as descriptors. In general, the 3D-QSAR model was able to predict the partition coefficients of neutral and anionic chemicals with an acceptable quality (RMSEtest set 0.63 ± 0.10, Rtest set2 0.52 ± 0.15, both for log KBSA/water). Particularly notable is that steric effects that cause a large difference in the log KBSA/water values between isomers were successfully reproduced by the model. The prediction of unknown KBSA/water values with the proposed model should contribute to improved environmental and toxicological assessments of chemicals.

Graphical abstract: 3D-QSAR predictions for bovine serum albumin–water partition coefficients of organic anions using quantum mechanically based descriptors

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Publication details

The article was received on 10 Oct 2016, accepted on 01 Dec 2016 and first published on 01 Dec 2016


Article type: Paper
DOI: 10.1039/C6EM00555A
Citation: Environ. Sci.: Processes Impacts, 2017,19, 261-269
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    3D-QSAR predictions for bovine serum albumin–water partition coefficients of organic anions using quantum mechanically based descriptors

    L. Linden, K. Goss and S. Endo, Environ. Sci.: Processes Impacts, 2017, 19, 261
    DOI: 10.1039/C6EM00555A

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