Development and clinical validation of an automated cell cytotoxicity neutralization assay for detecting Clostridioides difficile toxins in clinically relevant stools samples. (October 2021) - Elfassy, Arik; Kalina, Warren V.; French, Roger; Nguyen, Ha; Tan, Charles; Sebastian, Shite; Wilcox, Mark H.; Davies, Kerrie; Kutzler, Michele A.; Jansen, Kathrin U.; Anderson, Annaliesa; Pride, Michael W.
Abstract: Objectives: To improve the diagnostic accuracy of Clostridioides difficile infection, current U.S. and E.U. guidelines recommend multistep testing that detects the presence of C. difficile and toxin in clinically relevant stool samples to confirm active disease. An accepted gold standard to detect C. difficile toxins is the cell cytotoxicity neutralization assay (CCNA). Although highly sensitive, the traditional CCNA has limitations. One such limitation is the subjective interpretation of an analyst to recognize cytopathic effects in cultured cells exposed to a fecal sample containing toxin. To overcome this limitation, an automated CCNA was developed that replaces most human pipetting steps with robotics and incorporates CellTiterGlo® for a semi-quantitative, non-subjective measure of cell viability instead of microscopy. Methods: To determine sample positivity and control for non-specific cytopathic effects, two thresholds were defined and validated by evaluating the sample with/without antitoxin antisera (sample-antitoxin/sample + antitoxin): 1) a >70% cell viability threshold was validated with samples containing anti-toxin, and 2) a >1.2-fold difference cut-off where sample results above the cut-off are considered positive. Results: Assay validation demonstrated excellent accuracy, precision, and sample linearity with an LOD of 126.9 pg/mL toxin-B in stool. The positivity cut-offs were clinically validated by comparing 322 diarrheal stool sample results with those run in a predicate, microscopic readout-based CCNA. The automated CCNA demonstrated 96% sensitivity and 100% specificity compared with the predicate CCNA. Conclusions: Overall, the automated CCNA provides a specific, sensitive, and reproducible tool to support determination of CDI epidemiology or the efficacy of interventions such as vaccines. Highlights: The CCNA is the most sensitive method for detecting C. difficile free toxins in stool. Conventional CCNA methods are manual-based and the readout is subjective. Automation and luminescence readout improve assay performance. Coupled with a screening assay, the automated CCNA has utility in large vaccine trials.