766. Modeling Specific Antibody Responses to Natural Immunization to Predict a Correlate of Protection Against Infection Before Commencing a Clinical Vaccine Trial
Session: Poster Abstract Session: Vaccines: Pediatric
Thursday, October 27, 2016
Room: Poster Hall
Background: Clinical trials of vaccines for children to prevent acute otitis media (AOM) infections caused by the bacteria Streptococcus pneumonia (Spn) are in Phase I. The objective of this study was to use serum antibody measurements to pneumococcal purified protein candidate antigens that occurred after natural immunization to predict a correlate of protection response needed following an injectable vaccine against AOM in children.

Methods: Serum samples were collected from healthy children at 6, 9, 15, 18, and 24 months of age when they were colonized with Spn and when the child developed AOM. Middle ear fluid to detect Spn was collected at every episode of AOM. Quantitative ELISA was used to determine serum IgG against Spn 7 vaccine antigens: PspA clade 3, PspA clade 5, PhtD, PhtE, LytB, PcpA and Ply. A correlate of protection (COP) was estimated by regressing AOM events against age adjusted antibody levels induced by nasopharyngeal colonization and AOM infections, using logistic regression and generalized estimating equation methods.

Results: A significant COP was found for SpnPhtD (p=0.01), PhtE (p=0.0002), LytB (p=0.004), PcpA (p=0.002), and Ply (p=0.006) between higher antibody levels and reduced frequency of AOM. We estimated that a 2-fold higher antibody level compared to the mean antibody level (after adjusting for subject age) to PhtD or PcpA or Ply reduced the likelihood of AOM by 14-21%, a 4-fold higher level reduced it by 25-38% and a 10-fold higher level reduced it by 39-54%.

Conclusion: We developed a model to predict the necessary level of serum antibody and fold change to PhtD, PhtE, LytB, PcpA and Ply that would correlate with a reduced likelihood of AOM in children age 6-24 months old if enrolled in a Phase III clinical efficacy trial. In light of the success with the model, further work is merited.

Supported by NIH NIDCD R01 08671 and Sanofi Pasteur.

Anthony Almudevar, PhD, Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY and Michael Pichichero, MD, Rochester General Hospital Research Institute, Rochester, NY

Disclosures:

A. Almudevar, Sanofi Pasteur: Grant Investigator , Research support

M. Pichichero, Sanofi Pasteur: Consultant and Grant Investigator , Research support

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