Abstract
Non-human primate (NHP) efficacy data for several Ebola virus (EBOV) vaccine candidates exist, but definitive correlates of protection (CoP) have not been demonstrated, although antibodies to the filovirus glycoprotein (GP) antigen and other immunological endpoints have been proposed as potential CoPs. Accordingly, studies that could elucidate biomarker(s) that statistically correlate, whether mechanistically or not, with protection are warranted. The primary objective of this study was to evaluate potential CoP for Novavax EBOV GP vaccine candidate administered at different doses to cynomolgus macaques using the combined data from two separate, related studies containing a total of 44 cynomolgus macaques. Neutralizing antibodies measured by pseudovirion neutralization assay (PsVNA) and anti-GP IgG binding antibodies were evaluated as potential CoP using logistic regression models. The predictive ability of these models was assessed using the area under the receiver operating characteristic (ROC) curve (AUC). Fitted models indicated a statistically significant relationship between survival and log base 10 (log10) transformed anti-GP IgG antibodies, with good predictive ability of the model. Neither (log10 transformed) PsVNT50 nor PsVNT80 titers were statistically significant predictors of survival, though predictive ability of both models was good. Predictive ability was not statistically different between any pair of models. Models that included immunization dose in addition to anti-GP IgG antibodies failed to detect statistically significant effects of immunization dose. These results support anti-GP IgG antibodies as a correlate of protection. Total assay variabilities and geometric coefficients of variation (GCVs) based on the study data appeared to be greater for both PsVNA readouts, suggesting the increased assay variability may account for non-significant model results for PsVNA despite the good predictive ability of the models. The statistical approach to evaluating CoP for this EBOV vaccine may prove useful for advancing research for Sudan virus (SUDV) and Marburg virus (MARV) candidate vaccines.