Advanced chemometric methods as powerful tools for impurity profiling of drug substances and drug products: Application on bisoprolol and perindopril binary mixture

Abstract

Impurity profiling has a rising importance nowadays due to the increased health problems associated with impurities and degradation products found in several drug substances and formulations. Three advanced, accurate and precise chemometric methods were developed as impurity profiling methods for a mixture of bisoprolol fumarate (BIS) and perindopril arginine (PER) with their degradation products which represent drug impurity or a precursor to such impurity. The methods applied were Partial Least Squares (PLS-1), Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and Artificial Neural Networks (ANN). Genetic Algorithm (GA) was used as a variable selection tool to select the most significant wavelengths for the three chemometric models. For proper analysis, a 5-factor 5-level experimental design was used to establish a calibration set of 25 mixtures containing different ratios of the drugs and their degradation products (impurities). The validity of the proposed methods was assessed using an independent validation set. The designed models were able to predict the concentrations of the drugs and the degradation products/impurities in the validation set and pharmaceutical formulation. The proposed methods presented a powerful alternative to traditional and expensive chromatographic methods as impurity profiling tools.