Multiplexed VaxArray immunoassay for rapid antigen quantification in measles and rubella vaccine manufacturing

Measles-containing vaccines (MCV), specifically vaccines against measles and rubella (MR), are extremely effective and critical for the eradication of measles and rubella diseases. In developed countries, vaccination rates are high and vaccines are readily available, but continued high prevalence of both diseases in developing countries and surges in measles deaths in recent years have highlighted the need to expand vaccination efforts. To meet demand for additional vaccines at a globally affordable price, it is highly desirable to streamline vaccine production thereby reducing cost and speeding up time to delivery. MR vaccine characterization currently relies on the 50% cell culture infectious dose (CCID(50)) assay, an endpoint assay with low reproducibility that requires 10–14 days to complete. For streamlining bioprocess analysis and improving measurement precision relative to CCID(50), we developed the VaxArray Measles and Rubella assay kit, which is based on a multiplexed microarray immunoassay with a 5-hour time to result. Here we demonstrate vaccine-relevant sensitivity ranging from 345 to 800 IFU/mL up to 100,000 IFU/mL (infectious units per mL) and specificity that allows simultaneous analysis in bivalent vaccine samples. The assay is sensitive to antigen stability and has minimal interference from common vaccine additives. The assay exhibits high reproducibility and repeatability, with 15% CV, much lower than the typical 0.3 log(10) error (∼65%) observed for the CCID(50) assay. The intact protein concentration measured by VaxArray is reasonably correlated to, but not equivalent to, CCID(50) infectivity measurements for harvest samples. However, the measured protein concentration exhibits equivalency to CCID(50) for more purified samples, including concentrated virus pools and monovalent bulks, making the assay a useful new tool for same-day analysis of vaccine samples for bioprocess development, optimization, and monitoring.