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Royston Goodacre - Articles and news items
In recent years, Raman spectroscopy has been successfully applied to bioprocessing, including industrial processes. Raman studies have typically been aimed at measuring accurately both product yields and the presence of secondary products; including glucose and ethanol levels as well as secondary metabolites present in complex non-fractionated fermentation broths1,2. However, Raman spectroscopy as a tool for monitoring the complex structural changes occurring during protein production has to date been under utilised, particularly the use of the extremely sensitive structural technique of UV resonance Raman (UVRR) spectroscopy.
The biopharmaceutical market, although currently a relatively small percentage of conventional pharmaceuticals, continues to expand with predicted revenues of over USD 100 billion for 20103-5. With continual demand for high fidelity products, from recombinant therapeutic proteins to nucleic acid-based medicinal products, the need for reliable, cost effective and appropriate analytical techniques for assessing the conformity of the products is still paramount3,5.
Biotechnological expertise is becoming increasingly important within the pharmaceutical industry, and will play a pivotal role in the monitoring of fermentations, particularly their optimisation within the framework of Process Analytical Technologies (PAT). The ability to harness biological processes for the development of drug therapies, so called ‘biopharmaceuticals’ provides treatments that range from small molecule antibiotics to large recombinant proteins. Typically, synthesis of these drug products is enabled through the exploitation of bacterial, yeast, mammalian or plant cells. One of the earliest examples of protein biopharmaceuticals was the use of recombinant DNA technology to modify ‘Escherichia coli’ for the production of Human Insulin, which was followed by the development of Human Growth Hormone and Human Blood Clotting Factor1.
Raman spectroscopy is a highly versatile tool that provides chemical fingerprints from biological material that can be interpreted using chemometrics and machine learning. In combination this powerful approach is being developed for the quantitative determination of multiple determinands in bioprocesses and for the characterisation of microorganisms.
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