Mechanistic model for booster doses effectiveness in healthy, cancer and immunosuppressed patients infected with SARS-CoV-2

Voutouria, Chrysovalantis; Hardin, C. Corey; Naranbhai, Vivek; Nikmaneshia, Mohammad R; Khandekar, Melin J.; Gainor, Justin F.; Stylianopoulosb, Triantafyllos; Munn, Lance L.; Jain, Rakesh K.

doi:10.1101/2022.06.30.22277076

Cited by 3 publications

(5 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We model an individual who has been vaccinated against the ancestral strain of SARS-CoV-2 with the BNT-162b2a mRNA vaccine, including a first booster dose. Viral infection is assumed to take place 6 months following the booster dose (the model allows for the variation of antibody level over time [12].…”

Section: Modeling Strategy and Model Formulationmentioning

confidence: 99%

“…related to vaccination-induced immunity, including the affinity for and uptake rate of vaccine particles by cells, the rate of DNA transcription to mRNA, the production rate of viral antigens and the degradation rates of the vaccine and the viral antigen have been reported previously [12]. The values of model parameters are summarized in S1 Table in S1 Appendix.…”

Section: Plos Onementioning

confidence: 99%

“…We have developed a mechanistic model of COVID-19 that incorporates the major aspects of pathogenesis [12,13]. In this study, we deploy our model to study how the possible trajectories of viral load and arterial oxygen saturation following infection are affected by changes in infectious load, changes in the potency of antiviral agents, changes in the magnitude of the innate immune response and coagulation cascade and by changes to the adaptive immune response, including immunization and the presence of viral variants with varying degrees of immune evasion.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic heterogeneity in COVID-19: Insights from a mathematical model

Voutouri,

Hardin,

Naranbhai

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Critical illness, such as severe COVID-19, is heterogenous in presentation and treatment response. However, it remains possible that clinical course may be influenced by dynamic and/or random events such that similar patients subject to similar injuries may yet follow different trajectories. We deployed a mechanistic mathematical model of COVID-19 to determine the range of possible clinical courses after SARS-CoV-2 infection, which may follow from specific changes in viral properties, immune properties, treatment modality and random external factors such as initial viral load. We find that treatment efficacy and baseline patient or viral features are not the sole determinant of outcome. We found patients with enhanced innate or adaptive immune responses can experience poor viral control, resolution of infection or non-infectious inflammatory injury depending on treatment efficacy and initial viral load. Hypoxemia may result from poor viral control or ongoing inflammation despite effective viral control. Adaptive immune responses may be inhibited by very early effective therapy, resulting in viral load rebound after cessation of therapy. Our model suggests individual disease course may be influenced by the interaction between external and patient-intrinsic factors. These data have implications for the reproducibility of clinical trial cohorts and timing of optimal treatment.

show abstract

Section: Modeling Strategy and Model Formulationmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic heterogeneity in COVID-19: Insights from a mathematical model

Voutouri,

Hardin,

Naranbhai

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…We proposed that it was possible to model waning in anti-RBD Ab titer levels over time through simple mathematical models. We used exponential and power law models, which accurately describe non-linear waning of antibody titers for both natural infection and vaccination ( 11 , 17 – 20 ). Our aim was to develop a simple way to describe evolution of the antibody titer over time.…”

Section: Introductionmentioning

confidence: 99%

Mathematical modelling of the waning of anti-RBD IgG SARS-CoV-2 antibody titers after a two-dose BNT162b2 mRNA vaccination

et al. 2023

View full text Add to dashboard Cite

BackgroundAfter exposure to SARS-CoV-2 and/or vaccination there is an increase in serum antibody titers followed by a non-linear waning. Our aim was to find out if this waning of antibody titers would fit to a mathematical model.MethodsWe analyzed anti-RBD (receptor binding domain) IgG antibody titers and the breakthrough infections over a ten-month period following the second dose of the mRNA BNT162b2 (Pfizer-BioNtech.) vaccine, in a cohort of 54 health-care workers (HCWs) who were either never infected with SARS-CoV-2 (naïve, nHCW group, n=27) or previously infected with the virus (experienced, eHCW group, n=27). Two mathematical models, exponential and power law, were used to quantify antibody waning kinetics, and we compared the relative quality of the goodness of fit to the data between both models was compared using the Akaik Information Criterion.ResultsWe found that the waning slopes were significantly more pronounced for the naïve when compared to the experienced HCWs in exponential (p-value: 1.801E-9) and power law (p-value: 9.399E-13) models. The waning of anti-RBD IgG antibody levels fitted significantly to both exponential (average-R2: 0.957 for nHCW and 0.954 for eHCW) and power law (average-R2: 0.991 for nHCW and 0.988 for eHCW) models, with a better fit to the power law model. In the nHCW group, titers would descend below an arbitrary 1000-units threshold at a median of 210.6 days (IQ range: 74.2). For the eHCW group, the same risk threshold would be reached at 440.0 days (IQ range: 135.2) post-vaccination.ConclusionTwo parsimonious models can explain the anti-RBD IgG antibody titer waning after vaccination. Regardless of the model used, eHCWs have lower waning slopes and longer persistence of antibody titers than nHCWs. Consequently, personalized vaccination booster schedules should be implemented according to the individual persistence of antibody levels.

show abstract

“…We propose that it is possible to model the waning in the level of anti-RBD Ab titer over time through simple mathematical models. We have used the exponential and potential models, which are accurate models to analyze the non-linear waning of antibody titers for both natural infection and vaccination (11,(17)(18)(19)(20). Our aim is to develop a simple way to describe the evolution of the antibody titer over time.…”

Section: Introductionmentioning

confidence: 99%

Mathematical modeling of the waning of anti-RBD IgG SARS-CoV-2 antibody titers after a two-dose BNT162b2 mRNA vaccination

Torres

Ontañón

Cabo

et al. 2022

Preprint

View full text Add to dashboard Cite

Objective. Serum antibody levels have been linked to immune protection in SARS-CoV-2 infection. After exposure to the virus and/or vaccination there is an increase in serum antibody titers followed by progressive non-linear waning of antibody levels. Our aim was to find out if this waning of antibody titers would adjust to a mathematical model. Methods. We studied serum anti-RBD (receptor binding domain) IgG antibody titers over a ten month period in a cohort of 54 health-care workers who were either never infected with SARS-CoV-2 (naive, nHCWs group, n = 27) or previously infected (experienced, eHCWs group, n = 27) with the virus after the second dose of the BNT162b2 mRNA vaccine. We have selected a risk threshold of 1000 UA/ml anti-RBD Ab titer for symptomatic infection based on the upper titer threshold for those volunteers who suffered infection prior to the omicron outbreak. Two mathematical models, exponential and potential, were used to quantify antibody waning kinetics and the relative quality of the goodness of fit to the data between both models was compared using the Akaike Information Criterion. Results. We found that the waning of anti-RBD IgG antibody levels adjusted significantly to both exponential and potential models in all participants from both the naive and experienced groups. Moreover, the waning slopes were significantly more pronounced for the naive when compared to the experienced health-care workers. In the nHCWs group, titers would descend below this 1000-units threshold at a median of 210.6 days (IQ range: 74,2). However, for the eHCWs group, the risk threshold would be reached at 440.0 days (IQ range: 135,2) post-vaccination. Conclusions. The goodness of the fit of the anti-RBD IgG antibody waning would allow us to predict when the antibody titers would fall below an established threshold in both naive and previously infected subjects.

show abstract

Mechanistic model for booster doses effectiveness in healthy, cancer and immunosuppressed patients infected with SARS-CoV-2

Cited by 3 publications

References 47 publications

Dynamic heterogeneity in COVID-19: Insights from a mathematical model

Dynamic heterogeneity in COVID-19: Insights from a mathematical model

Mathematical modelling of the waning of anti-RBD IgG SARS-CoV-2 antibody titers after a two-dose BNT162b2 mRNA vaccination

Mathematical modeling of the waning of anti-RBD IgG SARS-CoV-2 antibody titers after a two-dose BNT162b2 mRNA vaccination

Contact Info

Product

Resources

About