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Challenging the Gold Standard for Evaluating Influenza Vaccines

The level of hemagglutination inhibition (HAI) used as the traditional gold standard for evaluating influenza vaccines in the United States and European Union may not actually be the best predictor of protection from influenza virus-induced disease.

The level of hemagglutination inhibition (HAI) used as the traditional gold standard for evaluating influenza vaccines in the United States and European Union may not actually be the best predictor of protection from influenza virus-induced disease. The underwhelming performance of influenza vaccines in recent years1 may be attributable, at least in part, to the use of this standard. If the HAI titers used to predict a protective effect of vaccination are ineffective, then new correlates must be sought.

HAI titers of ≥1:40 are currently considered to be protective.2 This cutoff was established almost a half-century ago,3 and evidence suggesting that this old benchmark may not be as useful as once believed is mounting.4 Despite the emerging unease with the nature of the correlation between vaccine effectiveness and the HAI titer standard, other possible correlates of immune protection including neuraminidase inhibition (NAI) titers have been either understudied or considered as a secondary assessment. Additionally, the current standard has not been assessed in a healthy volunteer challenge study since the early 1970s.

According to the lead author, Matthew J. Memoli, MD, from the Viral Pathogenesis and Evolution Section of the Laboratory of Infectious Diseases in the Division of Intramural Research at the National Institute of Allergy and Infectious Diseases in Bethesda, Maryland, "The variability of results in past studies and variations in recent influenza vaccine efficacy are likely multifactorial and suggest that more work is needed to better understand the correlates of protection in influenza infection."

In order to test the current standard and assess new correlates, Memoli et al conducted a healthy volunteer challenge study to determine whether participants with a high prechallenge serum HAI titer (≥1:40) were less likely to develop mild to moderate influenza disease (MMID) after intranasal inoculation with a wild-type influenza A/H1N1pdm virus compared to participants with a low prechallenge HAI titer (<1:40). The correlation of antineuraminidase antibodies to protection as measured by baseline neuraminidase inhibition (NAI) titers was investigated as well.5

Memoli et al confirmed past findings supporting a correlation between the standard HAI titer cutoff for protection and a protective effect; however, they also found that the protection predicted was incomplete. To highlight the novelty and importance of these results, Dr. Memoli and colleagues stated that, "This is the first healthy volunteer challenge study to specifically evaluate this cutoff since it was established."

Additional study results suggested that the use of both HAI and NAI titers together could be a better predictor of MMID and disease severity than either alone. However, the study data indicated that the NAI titer alone was a stronger correlate of disease severity than the HAI titer alone. Furthermore, greater levels of variability in response were detected after infection in HAI titers as compared with NAI titers during the two months following the viral challenge. More specifically, 100% of the participants with low NAI titers at baseline showed a significant increase in postchallenge titers, while roughly half of those with low baseline HAI titers showed no significant rise in postchallenge titers. The study results prompted Dr. Memoli et al to state that, "To the degree that a challenge model mimics natural infection, this high variability in detected antihemagglutinin antibody responses suggests that estimates of influenza infection rates based primarily on serosurveys may significantly underestimate the true infection rate and also raises concern over whether convalescent-phase HAI titers should be regarded as optimal markers of recent infection."

In describing the broader implications of their findings, Memoli and colleagues stated, "These data will also likely lead to future clinical studies that must be done to identify and confirm other correlates of protection beside anti-surface glycoprotein antibody titers and to develop a more refined understanding of the correlates of protection from influenza infection." Therefore, the results presented in this publication may represent a pivotal step towards addressing the larger issue of ineffective influenza vaccines and subsequent development of vaccines that afford better protection based on new standards used to define protection from influenza virus-induced disease.

William Perlman, PhD, CMPP is a former research scientist currently working as a medical/scientific content development specialist. He earned his BA in Psychology from Johns Hopkins University, his PhD in Neuroscience at UCLA, and completed three years of postdoctoral fellowship in the Neuropathology Section of the Clinical Brain Disorders Branch of the National Institute of Mental Health.

References

  1. Centers for Disease Control and Prevention. 24 June 2015. Seasonal influenza vaccine effectiveness, 2005-2015. Accessed 11 May 2016. http://www.cdc.gov/flu/professionals/vaccination/effectivenessstudies.htm.
  2. Cox RJ. Correlates of protection to influenza virus, where do we go from here? Hum Vaccin Immunother 2013;9:405—8.
  3. Hobson D, Curry RL, Beare AS, Ward-Gardner A. The role of serum haemagglutination-inhibiting antibody in protection against challenge infection with influenza A2 and B viruses. J Hyg (Lond) 1972;70:767—77.
  4. Black S, Nicolay U, Vesikari T, et al. 2011. Hemagglutination inhibition antibody titers as a correlate of protection for inactivated influenza vaccines in children. Pediatr Infect Dis J 2011;30:1081—5.
  5. Memoli MJ, Shaw PA, Han A, et al. Evaluation of antihemagglutinin and antineuraminidase antibodies as correlates of protection in an influenza A/H1N1 virus healthy human challenge model. mBio 2016;7:e00417-16.