NMR spectroscopy and chemometric models to detect a specific non-porcine ruminant contaminant in pharmaceutical heparin

Abstract

Heparin has been used successfully as a clinical antithrombotic for almost one century. Its isolation from animal sources (mostly porcine intestinal mucosa) involves multistep purification processes starting from the slaughterhouse (as mucosa) to the pharmaceutical plant (as the API). This complex supply chain increases the risk of contamination and adulteration, mainly with non-porcine ruminant material. The structural similarity of heparins from different origins, the natural variability of the heparin within samples from each source as well as the structural changes induced by manufacturing processes, require increasingly sophisticated methods capable of detecting low levels of contamination. The application of suitable multivariate classification approaches on API 1H NMRspectra serve as rapid and reliable tools for product authentication and the detection of contaminants. Soft Independent Modeling of Class Analogies (SIMCA), Discriminant Analysis (DA), Partial Least Square Discriminant Analysis (PLS-DA) and local classification methods (kNN, BNN and N3) were tested on about one hundred certified heparin samples produced by 14 different manufacturers revealing that Partial Least Squares Discriminant Analysis (PLS-DA) provided the best discrimination of contaminated batches, with a balanced accuracy of 97%.