Fold Rise in Antibody Titers by Measured by Glycoprotein-Based Enzyme-Linked Immunosorbent Assay Is an Excellent Correlate of Protection for a Herpes Zoster Vaccine, Demonstrated via the Vaccine Efficacy Curve

(See the editorial commentary by Wittes on pages 1523–5.) Herpes zoster (HZ), caused by reactivation of latent varicella zoster virus (VZV) [1], entails rash and pain that decreases daily functioning and health-related quality of life, more so with increasing age [1–10]. The phase III Shingles Prevention Study (SPS) showed that the live, attenuated zoster vaccine (ZV; Zostavax, Merck, Whitehouse Station, NJ) had an estimated 51% vaccine efficacy (VE; 95% confidence interval [CI], 44%–58%) to reduce the incidence of HZ in persons aged ≥60 years [11]. Subsequently, another phase III Zostavax Efficacy and Safety Trial (ZEST) was conducted to assess the VE of ZV in persons aged 50–59 years and showed an estimated 70% VE (95% CI, 54%–81%) [12]. Secondary analyses of the SPS suggested that 3 different measures of VZV-specific immunity were correlated with protection from HZ: responder cell frequency–determined cell-mediated immunity (CMI), interferon γ (IFN-γ) enzyme-linked immunosorbent spot–determined CMI, and glycoprotein-based enzyme-linked immunosorbent assay (gpELISA)–determined antibody level [13]. Because the gpELISA is easier to use and more validated as a reproducible quantitative assay and because the combination of titers and fold rise appeared to best correlate with protection in the SPS [14], the ZEST included an objective to assess VZV antibody titers by gpELISA but not by the CMI measures as a correlate of protection (CoP). Both week 6 and fold rise in VZV antibody titers determined by gpELISA were strong inverse correlates of risk (CoRs) of HZ in vaccine recipients [15], where a CoR is defined as an immune biomarker that is associated with the rate of a study end point used to measure VE in a defined population [16]. Here we directly assess whether the identified CoRs are also good CoPs [16]. A CoP may be defined as an immune biomarker measuring a response to vaccination that reliably predicts the level of VE to prevent a clinically meaningful end point. The literature on approaches for assessing CoPs has used different meanings for the term “CoP,” with no consensus [16–20]. Plotkin and Gilbert [21] advocated for the common use of the definition specified above, which is solely about reliable statistical prediction and is silent about whether the biomarker is also a mechanism of protection. These authors also advocated for annotating CoPs as mCoPs (mechanistic CoPs) or nCoPs (nonmechanistic CoPs) for scenarios in which experiments have determined whether the biomarker is a true mechanism of protection. Validated CoPs benefit vaccine development by serving as reliable surrogate end points for true clinical end points of interest, thereby allowing using surrogate end points in phase I and II trials to provide reliable answers about clinical VE without requiring phase III field trials. Validated CoPs confer many other benefits, as extensively discussed elsewhere [16–20, 22]. Given that reliable prediction of VE is very useful in itself, even as a so-called black box predictor with no associated mechanism, nCoPs and mCoPs are highly beneficial for vaccine development. The concept of a CoP is the same as a valid surrogate end point in the clinical trials statistical literature. Two major statistical frameworks have been developed for assessing the validity of a surrogate end point based on an efficacy trial, the Prentice framework [16, 23–25] and the principal stratification or VE curve framework [26, 27]. While both are useful for assessing the validity of the candidate gpELISA CoPs, this article focuses on the VE curve framework. This approach directly estimates the VE curve, defined as the VE against the clinical end point for each of a spectrum of participant subgroups defined by their biomarker readout measuring vaccine-induced immune response. The major challenge posed to using this approach is that the vaccine-induced immune response is only measured in subjects assigned to receive the vaccine (it is unknown in subjects assigned to receive placebo). Consequently, its effective application requires availability of a subject characteristic before immunization that predicts the candidate CoP [28]; such a baseline immunogenicity predictor (BIP) allows inclusion of an estimate of the immune response to the vaccine that placebo recipients would have had had they been vaccinated, thereby enabling estimation of the VE curve. This approach can be used for estimating the VE curve for subgroups defined either by levels of the vaccine-induced immune response or by both the levels of this response and the response under assignment to placebo. Several statistical methods have been developed for estimating the VE curve based on a BIP [27–32]. However, because adequately predictive BIPs have not been available, the methods have not yet been effectively applied. Fortunately, a high-quality BIP is available in the ZEST, and here we report, for the first time, an application of this framework to produce clear results that validate one biomarker as an excellent CoP (fold rise in gpELISA titers) and another biomarker as a poor CoP (week 6 gpELISA titers). By directly assessing how VE varies with participant subgroups defined by gpELISA readouts, this framework provides clearly interpretable results that lend themselves to central applications, such as comparing the utility of different immune response biomarkers as CoPs and estimating seroprotection levels.