Forward and reverse translational approaches to predict efficacy of neutralizing respiratory syncytial virus (RSV) antibody prophylaxis

Abstract

Background: Neutralizing mAbs can prevent communicable viral diseases. MK-1654 is a respiratory syncytial virus (RSV) F glycoprotein neutralizing monoclonal antibody (mAb) under development to prevent RSV infection in infants. Development and validation of methods to predict efficacious doses of neutralizing antibodies across patient populations exposed to a time-varying force of infection (i.e., seasonal variation) are necessary.

Methods: Five decades of clinical trial literature were leveraged to build a model-based meta-analysis (MBMA) describing the relationship between RSV serum neutralizing activity (SNA) and clinical endpoints. The MBMA was validated by backward translation to animal challenge experiments and forward translation to predict results of a recent RSV mAb trial. MBMA predictions were evaluated against a human trial of 70 participants who received either placebo or one of four dose-levels of MK-1654 and were challenged with RSV [NCT04086472]. The MBMA was used to perform clinical trial simulations and predict efficacy of MK-1654 in the infant target population.

Findings: The MBMA established a quantitative relationship between RSV SNA and clinical endpoints. This relationship was quantitatively consistent with animal model challenge experiments and results of a recently published clinical trial. Additionally, SNA elicited by increasing doses of MK-1654 in humans reduced RSV symptomatic infection rates with a quantitative relationship that approximated the MBMA. The MBMA indicated a high probability that a single dose of ≥ 75 mg of MK-1654 will result in prophylactic efficacy (> 75% for 5 months) in infants.

Interpretation: An MBMA approach can predict efficacy of neutralizing antibodies against RSV and potentially other respiratory pathogens.

Keywords: Human Challenge Study; Modelling and Simulation; Monoclonal Antibody; RSV, Meta-analysis; Respiratory Syncytial Virus.

Fig. 1

Published field and challenge study data indicate higher SNA results in lower incidence rates of RSV. (a, b) Curves for incidence rate versus FOI-weighted mean SNA titre. The mean of the SNA titre time-course (weighted by the FOI for field studies) was plotted as the independent variable for visualization. Each point represents a paired reported incidence rate and weighted mean SNA titre for a study arm in the literature. Error bars indicate the 95% confidence interval (CI) for the reported incidence rate. The size of a data point corresponds to its relative contribution to the model. Solid lines represent the model-fitted relationship between incidence rate and SNA titre for a typical trial. The shaded purple area represents the 95% CI of the model-fitted relationship. Higher mean SNA titre results in (a) lower seasonal RSV incidence rates across populations and disease severity in clinical field trials and (b) lower RSV incidence rates for adults in human challenge trials. In panels a and b, 40 and 1 data points, respectively, with a reported incidence rate of zero were included in the model fit but are not shown to increase visibility of key features. Similarly, the y-axis has been scaled to enable the visibility of salient data properties and results in some vertical bars representing CI to be truncated. The y-axis of panel b has been truncated at an incidence rate of 100%. Disease severity level definitions are provided (Supplementary Fig. S2). FOI, force-of-infection; SNA, serum neutralizing antibody; LRTI, lower respiratory tract infection; ICU, intensive care unit.

Fig. 2

Integrated MBMA scaled by V²ACHER reveals a strong relationship between increasing SNA titre and decreasing incidence of RSV LRTI in infants. All data points and model predictions have been scaled to RSV LRTI in infants using the V²ACHER method. The shaded purple area represents the 95% CI of the model-fitted relationship. Nine data points with an incidence rate of zero were included in the model but are not shown to improve visibility of key features. Similarly, the y-axis has been scaled to enable the visibility of salient data properties and some vertical bars representing CI are truncated. The points with crosses represent study arms with reported zero incidence rates; these points may appear as non-zero incidence rates after V²ACHER scaling. In one study (Supplementary Table S1, PMID: 29373476) the reported incidence rates were zero for all study arms but the model fit this finding well; the V²ACHER visualization method represents this by showing as points (shown as crosses) very close to the curve.

Fig. 3

Overlay of cotton rat challenge model with RSV incidence rate model in infants shows consistency. Solid points represent the reported incidence rate and weighted mean SNA titre for a stratified study arm in infants from the literature. Error bars indicate the 95% CI for the reported incidence rate. The size of each point corresponds to its relative model weight. Lines represent the model-fitted relationship between incidence rate and SNA titre for Tier-1 (i.e., rigorous, randomised controlled trials) infant studies lasting 151 days (solid purple) and the scaled viral load vs SNA titre relationship from cotton rat (dashed green). The shaded purple area represents the 95% CI of the model-fitted relationship. Seventeen data points with an incidence rate of zero were included in the model but are not shown to improve visibility of key features.

Fig. 4

Viral load as determined by RT-qPCR and quantitative culture following intranasal challenge with RSVA after single intravenous doses of MK-1654 or placebo (a to d). Mean nasal RSV viral load titres with corresponding standard are shown as measured by RT-qPCR at study Days 27 to 40 (a) and by quantitative culture at study Days 31 to 40 (c). The analysis of the VL-AUC is displayed for RT-qPCR (b) and quantitative culture (d). In (b), the least square mean is based on an analysis of variance (ANOVA) model with fixed effects for treatment. P values [2-sided t-test] and the CIs were constructed assuming a normal distribution for the VL-AUC. In each of the tables, n denotes the number of participants who were randomised, dosed with MK-1654 or placebo, received RSVA inoculation and contributed to the analysis. SD = standard deviation. RT-qPCR = reverse transcription quantitative real-time PCR.

Fig. 5

MK-1654 human challenge trial efficacy results are consistent with MBMA predictions. Each point represents a paired reported incidence rate of either asymptomatic or symptomatic infection and mean SNA titre at the time of challenge from the MK-1654 challenge trial (blue triangles) or the literature challenge trials (red circles). Error bars indicate the 95% CI for the incidence rate. Purple lines represent the model-fitted relationship between incidence rate and weighted mean SNA titre based on all literature data (both field and challenge data but excluding data from this challenge trial). The shaded purple area represents the 95% CI of the model fitted relationship. One datapoint with an incidence rate of zero was included in the model but is not shown for readability. The y-axis has been truncated at an incidence rate of 100%.

Fig. 6

In silico clinical trial simulations predict high efficacy against RSV LRTI for preterm and full-term infants. Simulation-based median predicted incidence rates and mean (95% CIs) for efficacy shown across dose levels (a) and distributions of predicted efficacy (b). Efficacy refers to the relative risk reduction of RSV LRTI between treatment and placebo groups following observation for 150 days. Each “violin” in b represents the distribution of predicted efficacy following simulation of 1000 clinical trials (3300 virtual infants enrolled to MK-1654 or placebo in a 2:1 ratio). Solid points represent the mean predicted efficacy for that dose level. Solid, vertical lines represent the upper and lower quartiles for the predicted distribution. LRTI, lower respiratory tract infection; CI, confidence interval.