Multivariate analysis applied to complex biological medicines. (24th May 2019)

• Record Type: Journal Article
• Authors: Rudd, Timothy R.; Mauri, Lucio; Marinozzi, Maria; Stancanelli, Eduardo; Yates, Edwin A.; Naggi, Annamaria; Guerrini, Marco
• Is Part Of: Faraday discussions. Volume 218(2019)
• Page Start: 303
• Page End: 316
• Publication Date: 2019-05-24
• Subjects: Chemistry -- Periodicals; Metallurgy -- Periodicals; Electrochemistry -- Periodicals; 540
• DOI: 10.1039/c9fd00009g
• Languages: English
• ISSNs: 1359-6640

Abstract

Abstract : Principal component analysis of 13 C– 1 H HSQC NMR spectra allows heparin from different animal sources to be differentiated, as well as spectral features that are specific to each heparin type to be identified. Abstract : A biological medicine (or biologicals) is a term for a medicinal compound that is derived from a living organism. By their very nature, they are complex and often heterogeneous in structure, composition and biological activity. Some of the oldest pharmaceutical products are biologicals, for example insulin and heparin. The former is now produced recombinantly, with technology being at a point where this can be considered a defined chemical entity. This is not the case for the latter, however. Heparin is a heterogeneous polysaccharide that is extracted from the intestinal mucosa of animals, primarily porcine, although there is also a significant market for non-porcine heparin due to social and economical reasons. In 2008 heparin was adulterated with another sulfated polysaccharide. Unfortunately this event was disastrous and resulted in a global public health emergency. This was the impetuous to apply modern analytical techniques, principally NMR spectroscopy, and multivariate analyses to monitor heparin. Initially, traditional unsupervised multivariate analysis (principal component analysis (PCA)) was applied to the problem. This was able to distinguish animal heparins from each other, and could also separate adulterated heparin from what was considered bona fide heparin. Taught multivariate analysis functions by training the analysis to look for specific patterns within the dataset of interest. If this approach was to be applied to heparin, or any other biological medicine, it would have to be taught to find every possible alien signal. The opposite approach would be more efficient; defining the complex heterogeneous material by a library of bona fide spectra and then filtering test samples with these spectra to reveal alien features that are not consistent with the reference library. This is the basis of an approach termed spectral filtering, which has been applied to 1D and 2D-NMR spectra, and has been very successful in extracting the spectral features of adulterants in heparin, as well as being able to differentiate supposedly biosimilar products. In essence, the filtered spectrum is determined by subtracting the covariance matrix of the library spectra from the covariance matrix of the library spectra plus the test spectrum. These approaches are universal and could be applied to biological medicines such as vaccine polysaccharides and monoclonal antibodies.