Influence of methanol on retention of hydrophobic organic chemicals in soil leaching column chromatography

The influence of methanol in methanol-water mixed eluents on the capacity factor (P), an important parameter which could depict leaching potential of hydrophobic organic chemicals (HOCs) in soil leaching column chromatography (SLCC), was investigated. Two reference soils, GSE 17201 obtained from Bayer Landwirtschaftszentrum, Monheim, Germany and SP 14696 from LUFA, Spencer, Germany, were used as packing materials in soil columns, and isocratic elution with methanol-water mixtures at different volume fractions of methanol (phi) were tested. Shortterm exposure of the column (packed with the GSE 17201 soil) to the eluents increased solute retention by a certain (23% log-unit) degree evaluated through a correlation with the retention on the same soil column but unpreconditioned by methanol-containing eluents. Long-term exposure of soil columns to the eluents did not influence the solute retention. A log-linear equation, log k' = log k'(w) - Sphi, could well and generally describe the retention of HOCs in SLCC. For the compounds of homologous series, logk'(w), had good linear relationship with S, indicating the hydrophobic partition mechanism existing in the retention process. (C) 2002 Elsevier Science Ltd. All rights reserved.

An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network

P-glycoprotein (P-gp), an ATP-binding cassette (ABC) transporter, functions as a biological barrier by extruding cytotoxic agents out of cells, resulting in an obstacle in chemotherapeutic treatment of cancer. In order to aid in the development of potential P-gp inhibitors, we constructed a quantitative structure-activity relationship (QSAR) model of flavonoids as P-gp inhibitors based on Bayesian-regularized neural network (BRNN). A dataset of 57 flavonoids collected from a literature binding to the C-terminal nucleotide-binding domain of mouse P-gp was compiled. The predictive ability of the model was assessed using a test set that was independent of the training set, which showed a standard error of prediction of 0.146 +/- 0.006 (data scaled from 0 to 1). Meanwhile, two other mathematical tools, back-propagation neural network (BPNN) and partial least squares (PLS) were also attempted to build QSAR models. The BRNN provided slightly better results for the test set compared to BPNN, but the difference was not significant according to F-statistic at p = 0.05. The PLS failed to build a reliable model in the present study. Our study indicates that the BRNN-based in silico model has good potential in facilitating the prediction of P-gp flavonoid inhibitors and might be applied in further drug design.