RESUMEN
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.