Comparison of several curve resolution methods for drug impurity profiling using high-performance liquid chromatography with diode array detection Original Research Article

The performance of five curve resolution methods was compared systematically for the identification and quantification of impurities in drug impurity profiling. These methods are alternating least-squares (ALS) with either random or iterative key-set factor analysis (IKSFA) initialisation, iterative target transformation factor analysis (ITTFA), evolving factor analysis (EFA), and heuristic evolving latent projections (HELP). Real and simulated high-performance liquid chromatography diode array detection (HPLC-DAD) data were obtained for drug mixtures containing one main compound and two impurities. The elution order of the main compound and the impurities was varied. Furthermore, resolutions were varied from 0.56 to 3.36 and impurity levels from 30% down to 0.1%. For simulated data, ALS with IKSFA initialisation and HELP perform better than ITTFA and EFA, which perform better than ALS with random initialisation. ITTFA works better than EFA for almost completely separated data, while the opposite is true for moderately or strongly overlapping data. Only ALS with IKSFA initialisation and HELP were found to resolve the required 0.1% level for moderately overlapping data. For real data, comparison of the methods provides similar results. ITTFA performs clearly better than EFA. However, none of the curve resolution methods can identify or quantify impurities at the required 0.1% level. The results for real data are worse than for simulated data because of heteroscedasticity, nonlinearity, and the acquisition resolution of the A/D-converter.