Prediction of polydimethylsiloxane-water partition coefficients based on the pp-LFER and QSAR models

Tengyi Zhu; Wenxuan Chen; Haomiao Cheng; Yajun Wang; Rajendra Prasad Singh

doi:10.1016/j.ecoenv.2019.109374

Prediction of polydimethylsiloxane-water partition coefficients based on the pp-LFER and QSAR models

Ecotoxicol Environ Saf. 2019 Oct 30:182:109374. doi: 10.1016/j.ecoenv.2019.109374. Epub 2019 Jun 26.

Authors

Tengyi Zhu¹, Wenxuan Chen², Haomiao Cheng², Yajun Wang³, Rajendra Prasad Singh³

Affiliations

¹ School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China. Electronic address: tyzhu@yzu.edu.cn.
² School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China.
³ School of Civil Engineering, Southeast University, Nanjing, 210096, China.

PMID: 31254853
DOI: 10.1016/j.ecoenv.2019.109374

Abstract

Obtaining accurate measurements of the partition coefficients between sorbent materials and water is of major importance for the analysis of the adsorption behavior and dissolved concentrations of organic compounds in the environment. In the passive-sampling approach, polydimethylsiloxane (PDMS) has a wide range of applications. Therefore, we established a poly-parameter linear-free energy relationship (pp-LFER) and a quantitative structure-activity relationship (QSAR) model to predict the log K_PDMS-w values for a large dataset of 290 organic chemicals from 11 diverse classes. For the pp-LFER model, E (excess molar refractivity), A (molecular H-bond donor ability), V (McGowan volume), and B (the H-bond acceptor properties) were introduced as the main correlated variables. However, the obtained model is much limited in terms of acquiring available descriptors. For this reason, we developed a QSAR model, and CrippenLogP (Crippen octanol-water partition coefficient), RNCG (Relative negative charge-most negative charge/total negative charge), ATSC4e (Centered Broto-Moreau autocorrelation-lag4/weighted by Sanderson electronegativities) and GATS6p (Geary autocorrelation-lag6/weighted by polarizabilities) were selected as the significant parameters. The predictive power and functional reliability of the presented models were confirmed with validation methods as described in previous studies. The adjusted determination coefficients (R²_adj) of 0.851 and 0.922 and leave-one-out cross-validated (Q²_LOO) of 0.841 and 0.907 revealed that the models have good predictive power and generalizability. Thus, the proposed models are simple yet accurate tools for predicting the log K_PDMS-w values and providing new insights to further understand the adsorption mechanism of organic compounds.

Keywords: Applicability domain; Organic compounds; PDMS-Water partition coefficient; QSAR; pp-LFER.

MeSH terms

Dimethylpolysiloxanes / chemistry*
Models, Chemical*
Octanols / chemistry
Quantitative Structure-Activity Relationship
Reproducibility of Results
Water / chemistry*

Substances

Dimethylpolysiloxanes
Octanols
Water
baysilon