Derivatives of 3′-Azidothymidine with 6-Cyanopyridone as Base or as Phosphoramidate Ester and their Antiretroviral Activity

Strongly pairing ethynylpyridone C-nucleosides are attractive surrogates for thymidine in oligonucleotides. Exploratory work on the antiviral activity of 3′-azidothymidine (AZT) derivatives with ethynylpyridone as base had identified strong lipophilicity as a limiting factor. Two strategies are being pursued to overcome this issue. In order to make the base more polar, the ethynyl group has been replaced with a cyano group, leading to a cyanopyridone C-nucleoside, whose eleven-step synthesis is reported here, together with the synthesis of a 3′-azido-2′,3′-dideoxynucleoside derivative. The base pairing with adenine in a DNA duplex was studied by UV melting analysis of a self-complementary hexamer containing the 6-cyano-2′-deoxynucleoside instead of thymidine. A melting point increase of 2 °C compared to the unmodified control was found. The other strategy employs a phosphoramidate prodrug design with less lipophilic amino acid esters. Here, anti-HIV test of the alaninyl and prolinyl methyl esters of AZT gave promising results in cell culture experiments, increasing the selectivity index up to 5.8-fold for the III_B strain and up to 5-fold for the ROD strain of the virus, as compared to the parent nucleoside. These findings help to design the next generation of pyridone C-nucleosides with potential applications as antivirals.     

Improvement of residual bilinearization by particle swarm optimization for achieving the second‐order advantage with unfolded partial least‐squares

The combination of unfolded partial least‐squares (U‐PLS) with residual bilinearization (RBL) provides a second‐order multivariate calibration method capable of achieving the second‐order advantage. RBL is performed by varying the test sample scores in order to minimize the residues of a combined U‐PLS model for the calibrated components and a principal component model for the potential interferents. The sample scores are then employed to predict the analyte concentration, with regression coefficients taken from the calibration step. When the contribution of multiple potential interferents is severe, particle swarm optimization (PSO) helps in preventing RBL to be trapped by false minima, restoring its predictive ability and making it comparable to the standard parallel factor (PARAFAC) analysis. Both simulated and experimental systems are analyzed in order to show the potentiality of the new technique. Copyright © 2007 John Wiley & Sons, Ltd.