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A strategy for developing chromatographic methods designed to determine impurities and degradation products in active pharmaceutical ingredients and drug products is presented. Selectivity is achieved by evaluating a chromatographic space comprised of 12 stationary/mobile phase combinations. Stationary phases predicted to be orthogonal based on their hydrophobic subtraction model parameters used. The particle sizes, column dimensions, and gradient times chosen provide high peak capacities and allow operation at backpressures that can be achieved with standard instrumentation. The mobile phases utilized are compatible with MS detection and cover a wide range of pH, solvent strength, and solvent selectivity. Analyte detection is accomplished using a combination of diode array and mass spectroscopic detectors which allow mixtures of project compounds to be injected and selectively detected. Automation of data acquisition and processing is accomplished using AutoChrom software from ACD\Labs. The strategy is illustrated with detailed data from two case studies and summary data from nineteen pharmaceutical projects., (Copyright © 2011 Elsevier B.V. All rights reserved.)

Quantitative structure-retention relationship (QSRR) models were studied for two databases: one with 151 compounds and the other with 1719 compounds. In both cases, the three modeling methods employed (multiple linear regression, partial least squares, and random forests) provided similar prediction results with regard to root-mean-square error of prediction. The reversed-phase retention related seven molecular descriptors provided better models for the smaller dataset, while the use of over 2000 molecular descriptors generated better models for the larger dataset. The QSRR models were then validated with a mixture of an active pharmaceutical ingredient and its four process/degradation impurities. Finally, classification of compounds based on similar logD profiles before QSRR modeling improved chromatographic predictability for the models used. The results showed that database composition had a desirable effect on prediction accuracy for certain input molecules.

The influence of two different di(1-pyrazolyl)alkane ligands on the rate constant of aqua ligand substitution of ruthenium(II) complexes with the formula [Ru(H2O)(L2)(tpmm)]2+ (L2 = di(1-pyrazolyl)methane (DPMet) or 2,2-di(1-pyrazolyl)propane (DPPro)) was investigated. A 9.4 x 10(5)-fold increase in the rate constant of ligand substitution at pH = 6.86 was observed when DPMet was replaced with DPPro. This remarkable increase was unexpected, considering that these bidentate ligands appear quite similar. To help lend insight into this dramatic spectator ligand effect, the activation parameters for the ligand substitution reactions were determined, and single-crystal X-ray data were collected on the structurally analogous (chloro)ruthenium(II) complexes, [Ru(Cl)(L2)(tpmm)]+. These results are discussed in the context of a heteroscorpionate effect exerted by the DPPro ligand.