4.4 Article

Ab initio computational study of positron emission tomography ligands interacting with lipid molecule for the prediction of nonspecific binding

期刊

JOURNAL OF COMPUTATIONAL CHEMISTRY
卷 29, 期 14, 页码 2397-2405

出版社

WILEY
DOI: 10.1002/jcc.20972

关键词

ab initio; brain imaging; nonspecific binding; quantum chemical predictor; compartmental model; positron emission tomography; QSAR

资金

  1. GlaxoSmithKline

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Nonspecific binding is a poorly understood biological phenomenon of relevance in the study of small molecules interactions in vivo and in drug development. Nonspecific binding is thought to be correlated in part to a molecule's lipophilicity, typically estimated by measuring (or calculating) octanol-water partition coefficient. This is, however, a gross simplification of a complex phenomenon. In this article, we present a computational method whose aim is to help identify positron emission tomography (PET) ligands with low nonspecific binding characteristics by investigating the molecular basis of ligand-membrane interaction. We considered a set consisting of 10 well-studied central nervous system PET radiotracers acting on a variety of molecular targets. Quantum mechanical calculations were used to estimate the strength of the interaction between each drug molecule and one phospholipid molecule commonly present in mammalian membranes. The results indicate a correlation between the computed drug-lipid interaction energy and the in vivo nonspecific distribution volume relative to the free tracer plasma concentration, calculated using standard compartmental modeling for the analysis of PET data. Significantly, the drugs whose interaction with the lipid molecule more favorably possessed, in general, a higher nonspecific binding value, whereas for the drugs taken in consideration in this study, the water-octanol partition coefficient, log P, did not show good predictive power of the nonspecific binding. This study also illustrates how ab initio chemical methods may offer meaningful and unbiased insights for the understanding of the underlying chemical mechanisms in biological systems. (C) 2008 Wiley Periodicals, Inc.

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