A qualitatively validated mathematical-computational model of the immune response to the yellow fever vaccine

Prediction of long-term humoral response induced by the two-dose heterologous Ad26.ZEBOV, MVA-BN-Filo vaccine against Ebola

The persistence of the long-term immune response induced by the heterologous Ad26.ZEBOV, MVA-BN-Filo two-dose vaccination regimen against Ebola has been investigated in several clinical trials. Longitudinal data on IgG-binding antibody concentrations were analyzed from 487 participants enrolled in six Phase I and Phase II clinical trials conducted by the EBOVAC1 and EBOVAC2 consortia. A model based on ordinary differential equations describing the dynamics of antibodies and short- and long-lived antibody-secreting cells (ASCs) was used to model the humoral response from 7 days after the second vaccination to a follow-up period of 2 years. Using a population-based approach, we first assessed the robustness of the model, which was originally estimated based on Phase I data, against all data. Then we assessed the longevity of the humoral response and identified factors that influence these dynamics. We estimated a half-life of the long-lived ASC of at least 15 years and found an influence of geographic region, sex, and age on the humoral response dynamics, with longer antibody persistence in Europeans and women and higher production of antibodies in younger participants.

Weekly Forecasting of Yellow Fever Occurrence and Incidence via Eco-Meteorological Dynamics

Yellow Fever (YF), a mosquito-borne disease, requires ongoing surveillance and prevention due to its persistence and ability to cause major epidemics, including one that began in Brazil in 2016. Forecasting based on factors influencing YF risk can improve efficiency in prevention. This study aimed to produce weekly forecasts of YF occurrence and incidence in Brazil using weekly meteorological and ecohydrological conditions. Occurrence was forecast as the probability of observing any cases, and incidence was forecast to represent morbidity if YF occurs. We fit gamma hurdle models, selecting predictors from several meteorological and ecohydrological factors, based on forecast accuracy defined by receiver operator characteristic curves and mean absolute error. We fit separate models for data before and after the start of the 2016 outbreak, forecasting occurrence and incidence for all municipalities of Brazil weekly. Different predictor sets were found to produce most accurate forecasts in each time period, and forecast accuracy was high for both time periods. Temperature, precipitation, and previous YF burden were most influential predictors among models. Minimum, maximum, mean, and range of weekly temperature, precipitation, and humidity contributed to forecasts, with optimal lag times of 2, 6, and 7 weeks depending on time period. Results from this study show the use of environmental predictors in providing regular forecasts of YF burden and producing nationwide forecasts. Weekly forecasts, which can be produced using the forecast model developed in this study, are beneficial for informing immediate preparedness measures.

Mechanistic Model Describing the Time Course of Humoral Immunity Following Ad26.COV2.S Vaccination in Non-Human Primates

Mechanistic modeling can be used to describe the time course of vaccine-induced humoral immunity and to identify key biologic drivers in antibody production. We used a six-compartment mechanistic model to describe a 20-week time course of humoral immune responses in 56 non-human primates (NHPs) elicited by vaccination with Ad26.COV2.S according to either a single-dose regimen (1 × 1011 or 5 × 1010 viral particles [vp]) or a two-dose homologous regimen (5 × 1010 vp) given in an interval of 4 or 8 weeks. Humoral immune responses were quantified by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike-specific binding antibody concentrations as determined by spike protein-enzyme-linked immunosorbent assay. The mechanistic model adequately described the central tendency and variability of binding antibody concentrations through 20 weeks in all vaccination arms. The estimation of mechanistic modeling parameters revealed greater contribution of the antibody production mediated by short-lived cells as compared with long-lived cells in driving the peak response, especially post second dose when a more rapid peak response was observed. The antibody production mediated by long-lived cells was identified as relevant for generating the first peak and for contributing to the long-term time course of sustained antibody concentrations in all vaccination arms. The findings contribute evidence on the key biologic components responsible for the observed time course of vaccine-induced humoral immunity in NHPs and constitute a step toward defining immune biomarkers of protection against SARS-CoV-2 that might translate across species. SIGNIFICANCE STATEMENT: We demonstrate the adequacy of a mechanistic modeling approach describing the time course of binding antibody concentrations in non-human primates (NHPs) elicited by different dose levels and regimens of Ad26.COV2.S. The findings are relevant for informing the mechanism-based accounts of vaccine-induced humoral immunity in NHPs and translational research efforts aimed at identifying immune biomarkers of protection against SARS-CoV-2 infection.

Within-host modeling to measure dynamics of antibody responses after natural infection or vaccination: A systematic review

BACKGROUND

Within-host models describe the dynamics of immune cells when encountering a pathogen, and how these dynamics can lead to an individual-specific immune response. This systematic review aims to summarize which within-host methodology has been used to study and quantify antibody kinetics after infection or vaccination. In particular, we focus on data-driven and theory-driven mechanistic models.

MATERIALS

PubMed and Web of Science databases were used to identify eligible papers published until May 2022. Eligible publications included those studying mathematical models that measure antibody kinetics as the primary outcome (ranging from phenomenological to mechanistic models).

RESULTS

We identified 78 eligible publications, of which 8 relied on an Ordinary Differential Equations (ODEs)-based modelling approach to describe antibody kinetics after vaccination, and 12 studies used such models in the context of humoral immunity induced by natural infection. Mechanistic modeling studies were summarized in terms of type of study, sample size, measurements collected, antibody half-life, compartments and parameters included, inferential or analytical method, and model selection.

CONCLUSIONS

Despite the importance of investigating antibody kinetics and underlying mechanisms of (waning of) the humoral immunity, few publications explicitly account for this in a mathematical model. In particular, most research focuses on phenomenological rather than mechanistic models. The limited information on the age groups or other risk factors that might impact antibody kinetics, as well as a lack of experimental or observational data remain important concerns regarding the interpretation of mathematical modeling results. We reviewed the similarities between the kinetics following vaccination and infection, emphasising that it may be worth translating some features from one setting to another. However, we also stress that some biological mechanisms need to be distinguished. We found that data-driven mechanistic models tend to be more simplistic, and theory-driven approaches lack representative data to validate model results.

Quantifying Antibody Persistence After a Single Dose of COVID-19 Vaccine Ad26.COV2.S in Humans Using a Mechanistic Modeling and Simulation Approach

Understanding persistence of humoral immune responses elicited by vaccination against coronavirus disease 2019 (COVID-19) is critical for informing the duration of protection and appropriate booster timing. We developed a mechanistic model to characterize the time course of humoral immune responses in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)-seronegative adults after primary vaccination with the Janssen COVID-19 vaccine, Ad26.COV2.S. The persistence of antibody responses was quantified through mechanistic modeling-based simulations. Two biomarkers of humoral immune responses were examined: SARS-CoV-2 neutralizing antibodies determined by wild-type virus neutralization assay (wtVNA) and spike protein-binding antibodies determined by indirect spike protein enzyme-linked immunosorbent assay (S-ELISA). The persistence of antibody responses was defined as the period of time during which wtVNA and S-ELISA titers remained above the lower limit of quantification. A total of 442 wtVNA and 1,185 S-ELISA titers from 82 and 220 participants, respectively, were analyzed following administration of a single dose of Ad26.COV2.S (5 × 10¹⁰ viral particles). The mechanistic model adequately described the time course of observed wtVNA and S-ELISA serum titers and its associated variability up to 8 months following vaccination. Mechanistic model-based simulations show that single-dose Ad26.COV2.S elicits durable but waning antibody responses up to 24 months following immunization. Of the estimated model parameters, the production rate of memory B cells was decreased in older adults relative to younger adults, and the antibody production rate mediated by long-lived plasma cells was increased in women relative to men. A steeper waning of antibody responses was predicted in men and in older adults.

Theoretical Explanation for the Rarity of Antibody-Dependent Enhancement of Infection (ADE) in COVID-19

Global vaccination against the SARS-CoV-2 virus has proved to be highly effective. However, the possibility of antibody-dependent enhancement of infection (ADE) upon vaccination remains underinvestigated. Here, we aimed to theoretically determine conditions for the occurrence of ADE in COVID-19. We developed a series of mathematical models of antibody response: model Ab-a model of antibody formation; model Cv-a model of infection spread in the body; and a complete model, which combines the two others. The models describe experimental data on SARS-CoV and SARS-CoV-2 infections in humans and cell cultures, including viral load dynamics, seroconversion times and antibody concentration kinetics. The modelling revealed that a significant proportion of macrophages can become infected only if they bind antibodies with high probability. Thus, a high probability of macrophage infection and a sufficient amount of pre-existing antibodies are necessary for the development of ADE in SARS-CoV-2 infection. However, from the point of view of the dynamics of pneumocyte infection, the two cases where the body has a high concentration of preexisting antibodies and a high probability of macrophage infection and where there is a low concentration of antibodies in the body and no macrophage infection are indistinguishable. This conclusion could explain the lack of confirmed ADE cases for COVID-19.

A Validated Mathematical Model of the Cytokine Release Syndrome in Severe COVID-19

By June 2021, a new contagious disease, the Coronavirus disease 2019 (COVID-19), has infected more than 172 million people worldwide, causing more than 3.7 million deaths. Many aspects related to the interactions of the disease's causative agent, SAR2-CoV-2, and the immune response are not well understood: the multiscale interactions among the various components of the human immune system and the pathogen are very complex. Mathematical and computational tools can help researchers to answer these open questions about the disease. In this work, we present a system of fifteen ordinary differential equations that models the immune response to SARS-CoV-2. The model is used to investigate the hypothesis that the SARS-CoV-2 infects immune cells and, for this reason, induces high-level productions of inflammatory cytokines. Simulation results support this hypothesis and further explain why survivors have lower levels of cytokines levels than non-survivors.

Current and prospective computational approaches and challenges for developing COVID-19 vaccines

SARS-CoV-2, which causes COVID-19, was first identified in humans in late 2019 and is a coronavirus which is zoonotic in origin. As it spread around the world there has been an unprecedented effort in developing effective vaccines. Computational methods can be used to speed up the long and costly process of vaccine development. Antigen selection, epitope prediction, and toxicity and allergenicity prediction are areas in which computational tools have already been applied as part of reverse vaccinology for SARS-CoV-2 vaccine development. However, there is potential for computational methods to assist further. We review approaches which have been used and highlight additional bioinformatic approaches and PK modelling as in silico methods which may be useful for SARS-CoV-2 vaccine design but remain currently unexplored. As more novel viruses with pandemic potential are expected to arise in future, these techniques are not limited to application to SARS-CoV-2 but also useful to rapidly respond to novel emerging viruses.

Validation of a yellow fever vaccine model using data from primary vaccination in children and adults, re-vaccination and dose-response in adults and studies with immunocompromised individuals

BACKGROUND

An effective yellow fever (YF) vaccine has been available since 1937. Nevertheless, questions regarding its use remain poorly understood, such as the ideal dose to confer immunity against the disease, the need for a booster dose, the optimal immunisation schedule for immunocompetent, immunosuppressed, and pediatric populations, among other issues. This work aims to demonstrate that computational tools can be used to simulate different scenarios regarding YF vaccination and the immune response of individuals to this vaccine, thus assisting the response of some of these open questions.

RESULTS

This work presents the computational results obtained by a mathematical model of the human immune response to vaccination against YF. Five scenarios were simulated: primovaccination in adults and children, booster dose in adult individuals, vaccination of individuals with autoimmune diseases under immunomodulatory therapy, and the immune response to different vaccine doses. Where data were available, the model was able to quantitatively replicate the levels of antibodies obtained experimentally. In addition, for those scenarios where data were not available, it was possible to qualitatively reproduce the immune response behaviours described in the literature.

CONCLUSIONS

Our simulations show that the minimum dose to confer immunity against YF is half of the reference dose. The results also suggest that immunological immaturity in children limits the induction and persistence of long-lived plasma cells are related to the antibody decay observed experimentally. Finally, the decay observed in the antibody level after ten years suggests that a booster dose is necessary to keep immunity against YF.

A personalized computational model of edema formation in myocarditis based on long-axis biventricular MRI images

Background

Myocarditis is defined as the inflammation of the myocardium, i.e. the cardiac muscle. Among the reasons that lead to this disease, we may include infections caused by a virus, bacteria, protozoa, fungus, and others. One of the signs of the inflammation is the formation of edema, which may be a consequence of the interaction between interstitial fluid dynamics and immune response. This complex physiological process was mathematically modeled using a nonlinear system of partial differential equations (PDE) based on porous media approach. By combing a model based on Biot’s poroelasticity theory with a model for the immune response we developed a new hydro-mechanical model for inflammatory edema. To verify this new computational model, T2 parametric mapping obtained by Magnetic Resonance (MR) imaging was used to identify the region of edema in a patient diagnosed with unspecific myocarditis.

Results

A patient-specific geometrical model was created using MRI images from the patient with myocarditis. With this model, edema formation was simulated using the proposed hydro-mechanical mathematical model in a two-dimensional domain. The computer simulations allowed us to correlate spatiotemporal dynamics of representative cells of the immune systems, such as leucocytes and the pathogen, with fluid accumulation and cardiac tissue deformation.

Conclusions

This study demonstrates that the proposed mathematical model is a very promising tool to better understand edema formation in myocarditis. Simulations obtained from a patient-specific model reproduced important aspects related to the formation of cardiac edema, its area, position, and shape, and how these features are related to immune response.