Multivariate analysis applied to complex biological medicines.

Area of Science:

• Analytical Chemistry
• Biotechnology
• Pharmacology

Background:

• Biological medicines are complex compounds derived from living organisms, posing analytical challenges.
• Heparin, a complex polysaccharide, was infamously adulterated in 2008, leading to a public health crisis.
• Traditional analytical methods struggle with the heterogeneity and complexity of biologicals.

Purpose of the Study:

• To develop and apply advanced analytical techniques for monitoring the quality and authenticity of biological medicines.
• To address the challenges posed by the heterogeneity and potential adulteration of complex biological drugs like heparin.
• To establish a robust method for distinguishing genuine biological products from contaminants or substandard versions.

Main Methods:

• Utilized Nuclear Magnetic Resonance (NMR) spectroscopy combined with multivariate data analysis.
• Applied unsupervised methods like Principal Component Analysis (PCA) for initial data exploration.
• Developed and implemented a 'spectral filtering' approach using a library of bona fide spectra for detecting anomalies.

Main Results:

• Principal Component Analysis successfully differentiated animal heparins and identified adulterated samples.
• Spectral filtering effectively extracted spectral features of adulterants in heparin.
• The method demonstrated success in differentiating between supposedly biosimilar biological products.

Conclusions:

• Spectral filtering is a powerful and universal approach for analyzing complex biological medicines.
• This technique enhances the ability to detect adulterants and ensure the quality of biologicals like heparin, vaccines, and monoclonal antibodies.
• Modern analytical techniques are crucial for maintaining public health and safety in the context of complex pharmaceuticals.