Sabarubicin Hydrochloride

To support a novel anthracycline agent - Sabarubicin's pharmacokinetics study in Chinese small cell lung cancer patients, a rapid, sensitive, and high throughput ultra-performance liquid chromatography tandem mass spectrometry method using Doxorubicin hydrochloride as internal standard (IS) was developed and validated for simultaneously quantifying Sabarubicin and its alcohol metabolite M3 in human plasma. Plasma samples were pre-extracted with n-hexane to remove hydrophobic interferences and the target compounds were extracted into a 1ml mixture of chloroform and isopropanol (1:1, v/v) and separated on an ACQUITY UPLC BEH Shield RP18 (100mm×2.1mm, 1.7μm) column with gradient mobile phase composed of acetonitrile and water containing 0.1% formic acid. Detection was performed by electrospray ionization in the positive ionization mode under multiple reaction monitoring of the transitions at m/z 644→130 for Sabarubicin, m/z 646→333.2 for M3, and m/z 544→360 for IS. For Sabarubicin and M3, calibration curves over 2-400ng/ml and 0.5-100ng/ml could achieve excellent linearity respectively(r>0.99). Intra- and inter-day precisions were 1.5%-9.1% and 2.2%-12.8%, and accuracy were -9.6% to 0.7% and -4.8% to 5.9% for Sabarubicin and M3 respectively at four concentration levels. The mean recovery for Sabarubicin was 62.4%, 71.9% for M3, and 58.8% for IS. This method was completely validated and successfully applied in the pharmacokinetics study of Sabarubicin and M3 in Chinese small cell lung cancer patients.

Studies on the intercalation of anticancer compounds with DNA can provide useful suggestions and guidance for the design of new and more efficient anticancer drugs.A quant. structure-property relationship (QSPR) study of a series of anticancer and candidate anticancer drugs with calf thymus DNA (ct-DNA) was performed.Constitutional, Topol., and WHIM descriptors, as well as GETAWAY, 3D-MoRSE, and Aromaticity Indexes descriptors generated from Dragon, were selected to describe the mols.The resampling by half-means method was used to detect the outlier mols.Self-organizing map was used to split the original dataset into training and test set.Genetic algorithm-multiple linear regression technique was used to establish QSPR model for training set.Finally, the best four-mol. descriptor model was developed on a training set of mols. and the external validation was performed on test set of mols.The stability and predictability of QSPR model were determined with the leave-one-out cross-validated variance and the external-validated variance.This QSPR approach can contribute to a better understanding of structural factors of the compounds responsible for drug-DNA intercalations, and be useful in predicting the binding affinity of other compounds with DNA.