Elsevier

Vaccine

Volume 31, Issue 36, 12 August 2013, Pages 3732-3738
Vaccine

A novel method for evaluating natural and vaccine induced serological responses to Bordetella pertussis antigens

https://doi.org/10.1016/j.vaccine.2013.05.073Get rights and content

Highlights

  • Long term follow up of time course of serum antibody responses to natural infection and vaccination.

  • Described by means of nonlinear regression models in a mixed model framework.

  • Characterize serum antibody responses by peak level and decay rate.

  • Quantitative comparison of vaccine responses and natural infection.

Abstract

We studied the time course of serum IgG antibodies against 3 different pertussis vaccine antigens: PT (pertussis toxin), FHA (filamentous hemagglutinin), Prn (pertactin) in sera from individuals vaccinated with four different pertussis vaccines at 4 years of age: (N = 44, 44, 23 and 23, respectively,) and compared the responses to/after natural infection with Bordetella pertussis (N = 44, age 1–8 years).

These longitudinal data were analyzed with a novel method, using a mathematical model to describe the observed responses, and their variation among subjects. This allowed us to estimate biologically meaningful characteristics of the serum antibody response, like peak level and decay rate, and to compare these among natural infections and vaccine responses.

Compared to natural infection, responses to PT after vaccination with the tested vaccines are smaller in magnitude and tend to decay slightly faster. When present in vaccines, FHA and Prn tend to produce high peak levels, higher than those in naturally infected patients, but these decay faster. As expected, the Dutch whole cell vaccine produced lower antibody responses than the acellular vaccines. This model allows a better comparison of the kinetics of vaccine induced antibody responses and after natural infection over a long follow up period.

Introduction

The introduction of mass vaccination against pertussis or whooping cough in the 1940–1950s caused a drastic decrease in infant mortality and may have greatly reduced the amount of notified cases of morbidity and mortality of Bordetella pertussis. However, since 1993 (1996 in the Netherlands) an increase in symptomatic cases has been observed and pertussis has been recognized as a re-emerging infection [1], [2], [3].

Although the effects of improved diagnostic methods and increased knowledge among practitioners have contributed, waning immunity in combination with pathogen adaptation have been implied as possible main causes for the re-emergence of pertussis [4], [5], [6], [7], [8], [9]. The re-emergence of pertussis has led to a renewed interest in pertussis vaccines, and their protection against symptomatic disease [10]. Pertussis vaccines contain whole, killed bacteria (whole cell vaccines, mostly used in third world countries), or specific antigenic components (acellular vaccines). An important issue is comparison of the serological response among different vaccines, and between vaccinated and naturally infected subjects.

Studies of the immune response to vaccination usually report antibody levels before and after administration of the vaccine, recording (group) geometric mean levels [11], [12], [13], [14], or seroconversion above a specified threshold [15]. Antibody persistence is usually characterized as the decrease in (group) geometric mean antibody levels with time after vaccination [16], [17] or even frequency of detection (above an arbitrary threshold) [18].

In all these classical approaches the data are summarized prior to analysis or testing, neglecting the heterogeneity predominant in serological data [19], [20] and concentrating on group metrics. Between subject variation in the dynamics of antibody responses is fundamentally neglected [19], [21].

If the serological antibody response to infection or vaccination can be characterized by biologically meaningful variables like peak antibody level and antibody decay rate, instead of observing group level statistics of antibody levels at a range of times post infection or vaccination, comparison between vaccines is less hampered by design issues like times of sampling or numbers of samples per subject. As follow up is usually feasible only for few samples per individual subject, temporal information is sparse and little is observed about the decay rate or peak antibody levels in any individual subject. For that reason, a mixed model approach is appropriate [21], [22], explicitly modelling within and between subject variation.

We have previously studied IgG antibody responses to pertussis toxin (IgG-PT) in subjects naturally infected with B. pertussis across a wide range of ages using a dynamic model of the interaction between antigen and antibodies [21]. Here we use the same approach to model both natural and vaccine induced responses to various antigens, to derive model based characteristics of the serum antibody response, allowing comparison between the different antibodies in terms of peak antibody levels and antibody decay rates.

Section snippets

Data

Longitudinal serum samples of natural infections were obtained from a patient cohort from a paediatric practice, as described previously [21]. From this cohort, patients of ages >1 and ≤8 years were selected, to agree with the ages in the vaccinated cohorts while keeping the numbers included not too small (N = 44). The average number of sera per patient was 3.9 (minimum 1, maximum 9 blood samples per patient). The whole cohort included also an elderly group of pertussis patients and consisted in

Results

Fig. 1 shows observed IgG concentrations against 3 components of B. pertussis: pertussis toxin (PT), filamentous hemagglutinin (FHA) and pertactin (Prn) for natural infection, booster vaccination with the Dutch whole cell vaccine, and the acellular vaccines (ACV-SKB, ACV-WL and ACV-PM). Both concentrations and time (from symptom onset in natural infection or from vaccination) are transformed to a log-scale, to facilitate identification of a response pattern. The considerable amount of

Discussion

Our approach of comparing vaccines comprises two stages: (1) fitting a mathematical model for the kinetics of the antibody response (including rising and decaying phase of the response) to serological data from subjects naturally infected and subjects vaccinated with different vaccines, and (2) comparing key variables of the model for natural infection and different vaccines. We use predicted values of peak antibody concentrations and predicted halftime values (of antibody decay) to

Acknowledgement

The contribution of one of the authors (PT) to this study was funded by POLYMOD, a European Commission project funded within the Sixth Framework Programme, Contract Number: SSP22-CT-2004-502084.

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