Refined efficacy estimates of the Sanofi Pasteur dengue vaccine CYD-TDV using machine learning

Abstract

CYD-TDV is the first licensed dengue vaccine for individuals 9–45 (or 60) years of age. Using 12% of the subjects enroled in phase-2b and phase-3 trials for which baseline serostatus was measured, the vaccine-induced protection against virologically confirmed dengue during active surveillance (0–25 months) was found to vary with prior exposure to dengue. Because age and dengue exposure are highly correlated in endemic settings, refined insight into how efficacy varies by serostatus and age is essential to understand the increased risk of hospitalisation observed among vaccinated individuals during the long-term follow-up and to develop safe and effective vaccination strategies. Here we apply machine learning to impute the baseline serostatus for subjects with post-dose 3 titres but missing baseline serostatus. We find evidence for age dependence in efficacy independent of serostatus and estimate that among 9–16 year olds, CYD-TDV is protective against serotypes 1, 3 and 4 regardless of baseline serostatus.

Efficacy profile of the CYD-TDV dengue vaccine revealed by Bayesian survival analysis of individual-level phase III data

“…Blood samples of trial participants who contracted virologically confirmed dengue during follow-up were analysed using the NS1 assay in the CYD14 and CYD15 trials. The results provided clear evidence of the enhanced risk of hospitalised or severe dengue disease in baseline seronegative vaccinees ( Sridhar et al, 2018 ) and these were in line with refined estimates of vaccine efficacy obtained with machine learning ( Dorigatti et al, 2018 ). Subsequently, in November 2017, Dengvaxia was recommended only in persons with a confirmed prior dengue infection ( WHO, 2018 ).…”

Cross-serotype interactions and disease outcome prediction of dengue infections in Vietnam

“…Whilst we appreciate that the implementation of serological screening cannot be performed routinely in large populations can be challenging, our results offer some guidance as to how severe future epidemics could potentially be, given the serotype replacement history in an epidemiological setting. Furthermore, our results suggest that vaccines that confer broadly neutralizing antibody responses could alleviate enhancement related concerns, something that became clear from the CYD-TDV vaccine efficacy analyses 46 .…”

“…We used machine learning techniques to analyse the occurrence of asymptomatic dengue infections. Similar techniques have been used recently to impute immunological status 46 and identifying subclinical infections 3 . Using a subset of our longitudinal data limited to individuals that had antibody titre measurements taken in two consecutive years, we trained a random forest algorithm (RFA) on individuals known to have been cases and those displaying no change or a decrease in antibody titres from one year to the next.…”

Functional Profiling of Antibody Immune Repertoires in Convalescent Zika Virus Disease Patients

“…Flavivirus-reactive antibodies have the potential to enhance infection through Fc-gamma receptor interactions when bound to the virion with a stoichiometry that does not support neutralization (26)(27)(28)(29), a phenomenon known as antibodydependent enhancement (ADE). The recent failure of a dengue virus vaccine (CYD-TDV) to provide significant protection, and the increased risk of dengue virus disease during subsequent infection in DENV-naïve vaccinees, illustrate the challenges inherent in safe and effective flavivirus vaccine development (28,30). Prior studies also suggest that individuals with preexisting YFV and JEV immunity generate stronger and broader immune responses to DENV vaccination, demonstrating the potential importance of flavivirus cross-reactive immune responses in human patients (31).…”