Detection of low numbers of bacterial cells in pharmaceutical drug product using Raman Spectroscopy and PLS-DA multivariate analysis

Abstract

Sterility testing is a laborious and slow process to detect contaminants present in drug products. Raman spectroscopy is a promising label-free tool to detect microorganisms and thus gaining relevance as future alternative culture-free method for sterility testing in pharmaceutical industry. However, reaching detection limits similar to standard procedures while keeping a high accuracy remains challenging, due to weak bacterial Raman signal. In this work, we show a new non-invasive approach focusing on detect different bacteria in concentrations below 100 CFU/ml within drug product containers using Raman spectroscopy and multivariate data analysis. Even though Raman spectra form drug product with and without bacteria are similar, a partial least squared discriminant analysis (PLS-DA) model shows great performance to distinguish samples with bacteria contaminants in limits below 10 CFU/ml. We use spiked samples with bacteria spores for independent validation achieving a detection accuracy of 99%. Our results indicate a great potential of this rapid, and cost-effective approach to be use in quality control of pharmaceutical industry.

Recent advances of Raman spectroscopy for the analysis of bacteria

“…Traditional linear classification models are challenging when the Raman spectra of bacteria are highly similar. Different multivariate data analysis methods have been developed for the identification and discrimination of bacteria with Raman spectroscopy, such as principal component analysis (PCA) in Figure 3A , 61 , 62 , 63 , 64 partial least square discriminant analysis (PLS‐DA), 63 , 65 , 66 and discriminant function analysis (DFA). 64 These methods have been applied to discriminate waterborne pathogen species in drinking water, 62 to discriminate bacterial strains with different biofilm forming abilities, 63 to identify bacteria and yeast species in blueberries (Figure 3B ), 64 and to differentiate common microbes in urine.…”