Raman spectroscopy as an alternative rapid microbial bioburden test method for continuous, automated detection of contamination in biopharmaceutical drug substance manufacturing

Abstract

Aims To investigate an in-line Raman method capable of detecting accidental microbial contamination in pharmaceutical vessels, such as bioreactors producing monoclonal antibodies via cell culture. Methods and results The Raman method consists of a multivariate model built from Raman spectra collected in-line during reduced-scale bioreactor batches producing a monoclonal antibody, as well as a reduced-scale process with intentional spiking of representative compendial method microorganisms (n = 4). The orthogonal partial least squares regression discriminant analysis model (OPLS-DA) Area Under the Curve (AUC), specificity and sensitivity were 0.96, 0.99 and 0.95, respectively. Furthermore, the model successfully detected contamination in an accidentally contaminated manufacturing-scale batch. In all cases, the time to detection (TTD) for Raman was superior compared to offline, traditional microbiological culturing. Conclusions The Raman OPLS-DA method met acceptance criteria for equivalent decision making to be considered a viable alternative to the compendial method for in-process bioburden testing. The in-line method is automated, non-destructive, and provides a continuous assessment of bioburden compared to an offline compendial method, which is manual, results in loss of product, and in practice is only collected once daily and requires 3–5 days for enumeration.