Prediction of tissue: air partition coefficients — theoretical vs. experimental methods

Abstract

Predictive QSAR models for rat and human tissue : air partition coefficients, namely blood : air, fat : air, brain : air, liver : air, muscle : air, and kidney : air were developed utilizing experimentally determined partition coefficients for 131 chemicals obtained from the literature and molecular descriptors based solely on chemical structure. The descriptors were partitioned into four hierarchical classes, including topostructural, topochemical, 3-dimensional, and ab initio quantum chemical. Three types of regression methodologies — ridge regression, principal components regression, and partial least squares regression — were used comparatively in the development of the structure-based models. In addition to the structure-based models, ordinary least squares regression was used to develop comparative models based on experimentally determined properties including saline : air and olive oil : air partition coefficients. The results of the study indicate that many of the structure-based models are comparable or superior to their respective property-based models. This is an important result considering that structural descriptors can be calculated quickly and inexpensively for both existing chemicals and those not yet synthesized. It was also found that ridge regression outperformed principal components regression and partial least squares regression, with respect to the structure-based models, and that generally the topochemical descriptors alone produced models of good predictive ability.

Keywords:

• Tissue : air partition coefficient
• Volatile organic chemicals (VOCs)
• Hierarchical quantitative structure-activity relationship (HiQSAR)
• Topological indices
• Ridge regression

Acknowledgements

This paper represents contribution number 404 from the Center for Water and the Environment of the Natural Resources Research Institute. Research was supported by the United States Air Force (Grant F49620-02-1-0138) and the Agency for Toxic Substances and Disease Registry (Cooperative Agreement Number 572112). The US Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Air Force Office of Scientific Research or the US Government.

Please note, symbols, definitions and structural molecular descriptors for this paper are posted online on the journal website http://www.tandf.co.uk/journals/listings/s.asp under the heading “Supplementary material”.