The Power of Liquid Chromatography–Mass Spectrometry in the Characterization of Protein Biopharmaceuticals

May 02, 2013
By LCGC Editors

Protein biopharmaceuticals, such as monoclonal antibodies and recombinant proteins, are currently widely used to treat various life-threatening conditions including cancer, anaemia, diabetes and autoimmune diseases. Protein therapeutics are far more complex than small molecule drugs and unravelling this complexity obviously represents an analytical challenge. This article highlights some selected examples of the power of liquid chromatography combined with mass spectrometry(LC–MS) in the development of protein biopharmaceuticals.

More than 30 years after the commercial introduction of the first recombinant protein to treat diabetes, namely human insulin, hundreds of protein biopharmaceuticals have been approved by the regulatory agencies and several have blockbuster status (1). Today the global protein therapeutics market is worth 100 billion dollars (which represents approximately 20% of the total pharmaceutical market) and it is expected that, within the current decade, more than 50% of the new drug approvals will be biologics (2). Monoclonal antibodies are expected to play a dominant role (3).

During the development and lifetime of these molecules, an in-depth characterization is required. Compared with small molecule drugs, protein biopharmaceuticals are large and heterogeneous (as a result of the biosynthetic process and subsequent manufacturing and storage), making their analysis very challenging. Liquid chromatography (LC) combined with mass spectrometry (MS) has become the principal enabling technology to characterize these macromolecules (4–7).

Using a monoclonal antibody (mAb) as an example, we will illustrate how characteristics such as amino acid sequence; molecular weight and structural integrity; N-glycosylation; N- and C-terminal processing; S-S bridges; deamidation (asparagine and glutamine); aspartate isomerization; and oxidation (methionine and tryptophan) can be extracted out of the LC–MS dataset. A detailed structural insight requires an assessment at protein, peptide and sugar levels.


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