From Infection to Immunity: Understanding the Response to SARS-CoV2 Through In-Silico Modeling

BackgroundImmune system conditions of the patient is a key factor in COVID-19 infection survival. A growing number of studies have focused on immunological determinants to develop better biomarkers for therapies.AimStudies of the insurgence of immunity is at the core of both SARS-CoV-2 vaccine development and therapies. This paper attempts to describe the insurgence (and the span) of immunity in COVID-19 at the population level by developing an in-silico model. We simulate the immune response to SARS-CoV-2 and analyze the impact of infecting viral load, affinity to the ACE2 receptor, and age in an artificially infected population on the course of the disease.MethodsWe use a stochastic agent-based immune simulation platform to construct a virtual cohort of infected individuals with age-dependent varying degrees of immune competence. We use a parameter set to reproduce known inter-patient variability and general epidemiological statistics.ResultsBy assuming the viremia at day 30 of the infection to be the proxy for lethality, we reproduce in-silico several clinical observations and identify critical factors in the statistical evolution of the infection. In particular, we evidence the importance of the humoral response over the cytotoxic response and find that the antibody titers measured after day 25 from the infection are a prognostic factor for determining the clinical outcome of the infection. Our modeling framework uses COVID-19 infection to demonstrate the actionable effectiveness of modeling the immune response at individual and population levels. The model developed can explain and interpret observed patterns of infection and makes verifiable temporal predictions. Within the limitations imposed by the simulated environment, this work proposes quantitatively that the great variability observed in the patient outcomes in real life can be the mere result of subtle variability in the infecting viral load and immune competence in the population. In this work, we exemplify how computational modeling of immune response provides an important view to discuss hypothesis and design new experiments, in particular paving the way to further investigations about the duration of vaccine-elicited immunity especially in the view of the blundering effect of immunosenescence.

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From infection to immunity: understanding the response to SARS-CoV2 through in-silico modeling

10.1101/2020.12.20.423670 ◽

2020 ◽

Author(s):

Filippo Castiglione ◽

Debashrito Deb ◽

Anurag P. Srivastava ◽

Pietro Liò ◽

Arcangelo Liso

Keyword(s):

Immune Response ◽

Viral Load ◽

Computational Modeling ◽

In Silico ◽

Vaccine Development ◽

Real Life ◽

Humoral Response ◽

Immune Competence ◽

Modeling Framework ◽

The Impact

AbstractBackgroundImmune system conditions of the patient is a key factor in COVID-19 infection survival. A growing number of studies have focused on immunological determinants to develop better biomarkers for therapies.AimThe dynamics of the insurgence of immunity is at the core of the both SARS-CoV-2 vaccine development and therapies. This paper addresses a fundamental question in the management of the infection: can we describe the insurgence (and the span) of immunity in COVID-19? The in-silico model developed here answers this question at individual (personalized) and population levels.We simulate the immune response to SARS-CoV-2 and analyze the impact of infecting viral load, affinity to the ACE2 receptor and age in the artificially infected population on the course of the disease.MethodsWe use a stochastic agent-based immune simulation platform to construct a virtual cohort of infected individuals with age-dependent varying degree of immune competence. We use a parameter setting to reproduce known inter-patient variability and general epidemiological statistics.ResultsWe reproduce in-silico a number of clinical observations and we identify critical factors in the statistical evolution of the infection. In particular we evidence the importance of the humoral response over the cytotoxic response and find that the antibody titers measured after day 25 from the infection is a prognostic factor for determining the clinical outcome of the infection.Our modeling framework uses COVID-19 infection to demonstrate the actionable effectiveness of simulating the immune response at individual and population levels. The model developed is able to explain and interpret observed patterns of infection and makes verifiable temporal predictions.Within the limitations imposed by the simulated environment, this work proposes in a quantitative way that the great variability observed in the patient outcomes in real life can be the mere result of subtle variability in the infecting viral load and immune competence in the population.In this work we i) show the power of model predictions, ii) identify the clinical end points that could be more suitable for computational modeling of COVID-19 immune response, iii) define the resolution and amount of data required to empower this class of models for translational medicine purposes and, iv) we exemplify how computational modeling of immune response provides an important light to discuss hypothesis and design new experiments.

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Rapid Weight Loss, Central Obesity Improvement and Blood Glucose Reduction Are Associated with a Stronger Adaptive Immune Response Following COVID-19 mRNA Vaccine

Vaccines ◽

10.3390/vaccines10010079 ◽

2022 ◽

Vol 10 (1) ◽

pp. 79

Author(s):

Mikiko Watanabe ◽

Angela Balena ◽

Davide Masi ◽

Rossella Tozzi ◽

Renata Risi ◽

...

Keyword(s):

Immune Response ◽

Weight Loss ◽

Blood Glucose ◽

Adaptive Immune Response ◽

Humoral Response ◽

Rapid Weight Loss ◽

Low Carbohydrate Diet ◽

Adaptive Immune ◽

Glucose Reduction ◽

The Impact

Obesity is associated with a poor COVID-19 prognosis, and it seems associated with reduced humoral response to vaccination. Public health campaigns have advocated for weight loss in subjects with obesity, hoping to eliminate this risk. However, no evidence proves that weight loss leads to a better prognosis or a stronger immune response to vaccination. We aimed to investigate the impact of rapid weight loss on the adaptive immune response in subjects with morbid obesity. Twenty-one patients followed a hypocaloric, very-low-carbohydrate diet one week before to one week after the two mRNA vaccine doses. The diet’s safety and efficacy were assessed, and the adaptive humoral (anti-SARS CoV-2 S antibodies, Abs) and cell-mediated responses (IFNγ secretion on stimulation with two different SARS CoV-2 peptide mixes, IFNγ-1 and IFNγ-2) were evaluated. The patients lost ~10% of their body weight with metabolic improvement. A high baseline BMI correlated with a poor immune response (R −0.558, p = 0.013 for IFNγ-1; R −0.581, p = 0.009 for IFNγ-2; R −0.512, p = 0.018 for Abs). Furthermore, there was a correlation between weight loss and higher IFNγ-2 (R 0.471, p = 0.042), and between blood glucose reduction and higher IFNγ-1 (R 0.534, p = 0.019), maintained after weight loss and waist circumference reduction adjustment. Urate reduction correlated with higher Abs (R 0.552, p = 0.033). In conclusion, obesity is associated with a reduced adaptive response to a COVID-19 mRNA vaccine, and weight loss and metabolic improvement may reverse the effect.

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Impact of Binge Alcohol Intoxication on the Humoral Immune Response during Burkholderia spp. Infections

Microorganisms ◽

10.3390/microorganisms7050125 ◽

2019 ◽

Vol 7 (5) ◽

pp. 125

Author(s):

Ryan M. Moreno ◽

Victor Jimenez ◽

Fernando P. Monroy

Keyword(s):

Immune Response ◽

Humoral Immune Response ◽

Opportunistic Infections ◽

Alcohol Intoxication ◽

Experimental Studies ◽

Adaptive Immune System ◽

Humoral Response ◽

Humoral Immune ◽

Healthy Humans ◽

The Impact

Burkholderia pseudomallei, the causative agent of melioidosis can occur in healthy humans, yet binge alcohol use is progressively being recognized as a major risk factor. Currently, no experimental studies have investigated the effects of binge alcohol on the adaptive immune system during an active infection. In this study, we used B. thailandensis and B. vietnamiensis, to investigate the impact of a single binge alcohol episode on the humoral response during infection. Eight-week-old female C57BL/6 mice were administered alcohol comparable to human binge drinking (4.4 g/kg) or PBS intraperitoneally 30 min before intranasal infection. Mice infected with B. thailandensis had a 100% survival rate, while those infected with B. vietnamiensis had a 33% survivability rate when a binge alcohol dose was administered. B. thailandensis was detected in blood of mice administered alcohol at only 7 days post infection (PI), while those infected with B. vietnamiensis and receiving alcohol were found throughout the 28-day infection as well as in tissues at day 28 PI. Binge alcohol elevated IgM and delayed IgG specific to the whole cell lysate (WCL) of B. vietnamiensis but not B. thailandensis infections. Differences in immunogenicity of B. pseudomallei near-neighbors provide a framework for novel insights into the effects of binge alcohol’s suppression of the humoral immune response that can cause opportunistic infections in otherwise healthy hosts.

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Prediction and evolution of the molecular fitness of SARS-CoV-2 variants: Introducing SpikePro

10.1101/2021.04.11.439322 ◽

2021 ◽

Author(s):

Fabrizio Pucci ◽

Marianne Rooman

Keyword(s):

Immune Response ◽

Neutralizing Antibodies ◽

Molecular Mechanisms ◽

Population Level ◽

Critical Issue ◽

Spike Protein ◽

Human Immune Response ◽

Human Immune ◽

The Stability ◽

The Impact

The understanding of the molecular mechanisms driving the fitness of the SARS-CoV-2 virus and its mutational evolution is still a critical issue. We built a simplified computational model, called SpikePro, to predict the SARS-CoV-2 fitness from the amino acid sequence and structure of the spike protein. It contains three contributions: the viral transmissibility predicted from the stability of the spike protein, the infectivity computed in terms of the affinity of the spike protein for the ACE2 receptor, and the ability of the virus to escape from the human immune response based on the binding affinity of the spike protein for a set of neutralizing antibodies. Our model reproduces well the available experimental, epidemiological and clinical data on the impact of variants on the biophysical characteristics of the virus. For example, it is able to identify circulating viral strains that, by increasing their fitness, recently became dominant at the population level. SpikePro is a useful instrument for the genomic surveillance of the SARS-CoV-2 virus, since it predicts in a fast and accurate way the emergence of new viral strains and their dangerousness. It is freely available in the GitHub repository github.com/3BioCompBio/SpikeProSARS-CoV-2.

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A multi-scale epidemic model of Salmonella infection with heterogeneous shedding

ESAIM Proceedings and Surveys ◽

10.1051/proc/202067015 ◽

2020 ◽

Vol 67 ◽

pp. 261-284

Author(s):

Simon Labarthe ◽

Béatrice Laroche ◽

Thi Nhu Tao Nguyen ◽

Bastien Polizzi ◽

Florian Patout ◽

...

Keyword(s):

Control Strategies ◽

Food Poisoning ◽

Population Level ◽

Pathogen Transmission ◽

Modeling Framework ◽

Multi Scale ◽

Multiscale Modeling Framework ◽

The Individual ◽

Random Fluctuations ◽

The Impact

Salmonella strains colonize the digestive tract of farm livestock, such as chickens or pigs, without affecting them, and potentially infect food products, representing a threat for human health ranging from food poisoning to typhoid fever. It has been shown that the ability to excrete the pathogen in the environment and contaminate other animals is variable. This heterogeneity in pathogen carriage and shedding results from interactions between the host’s immune response, the pathogen and the commensal intestinal microbiota. In this paper we propose a novel generic multiscale modeling framework of heterogeneous pathogen transmission in an animal population. At the intra-host level, the model describes the interaction between the commensal microbiota, the pathogen and the inflammatory response. Random fluctuations in the ecological dynamics of the individual microbiota and transmission at between-host scale are added to obtain a drift-diffusion PDE model of the pathogen distribution at the population level. The model is further extended to represent transmission between several populations. The asymptotic behavior as well as the impact of control strategies including cleaning and antimicrobial administration are investigated through numerical simulation.

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A Quantitative Systems Pharmacology Model of the Pathophysiology and Treatment of COVID-19 Predicts Optimal Timing of Pharmacological Interventions

10.1101/2021.12.07.21267277 ◽

2021 ◽

Author(s):

Rohit Rao ◽

Cynthia J. Musante ◽

Richard Allen

Keyword(s):

Immune Response ◽

Viral Load ◽

Symptom Onset ◽

In Silico ◽

Clinical Trial Design ◽

Baseline Viral Load ◽

Optimal Timing ◽

Systems Pharmacology ◽

Quantitative Systems Pharmacology ◽

Wide Range

AbstractA quantitative systems pharmacology (QSP) model of the pathogenesis and treatment of SARS-CoV-2 infection can streamline and accelerate the development of novel medicines to treat COVID-19. Simulation of clinical trials allows in silico exploration of the uncertainties of clinical trial design and can rapidly inform their protocols. We previously published a preliminary model of the immune response to SARS-CoV-2 infection. To further our understanding of COVID-19 and treatment we significantly updated the model by matching a curated dataset spanning viral load and immune responses in plasma and lung. We identified a population of parameter sets to generate heterogeneity in pathophysiology and treatment and tested this model against published reports from interventional SARS-CoV-2 targeting Ab and anti-viral trials. Upon generation and selection of a virtual population, we match both the placebo and treated responses in viral load in these trials. We extended the model to predict the rate of hospitalization or death within a population. Via comparison of the in silico predictions with clinical data, we hypothesize that the immune response to virus is log-linear over a wide range of viral load. To validate this approach, we show the model matches a published subgroup analysis, sorted by baseline viral load, of patients treated with neutralizing Abs. By simulating intervention at different timepoints post infection, the model predicts efficacy is not sensitive to interventions within five days of symptom onset, but efficacy is dramatically reduced if more than five days pass post-symptom onset prior to treatment.

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Slight reduction in SARS-CoV-2 exposure viral load due to masking results in a significant reduction in transmission with widespread implementation

Scientific Reports ◽

10.1038/s41598-021-91338-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ashish Goyal ◽

Daniel B. Reeves ◽

Niket Thakkar ◽

Mike Famulare ◽

E. Fabián Cardozo-Ojeda ◽

...

Keyword(s):

Viral Load ◽

Antiviral Therapy ◽

Population Level ◽

Transmission Risk ◽

Reproductive Number ◽

Post Exposure Prophylaxis ◽

Slight Reduction ◽

Infected People ◽

Widespread Implementation ◽

The Impact

AbstractMasks are a vital tool for limiting SARS-CoV-2 spread in the population. Here we utilize a mathematical model to assess the impact of masking on transmission within individual transmission pairs and at the population level. Our model quantitatively links mask efficacy to reductions in viral load and subsequent transmission risk. Our results reinforce that the use of masks by both a potential transmitter and exposed person substantially reduces the probability of successful transmission, even if masks only lower exposure viral load by ~ 50%. Slight increases in mask adherence and/or efficacy above current levels would reduce the effective reproductive number (Re) substantially below 1, particularly if implemented comprehensively in potential super-spreader environments. Our model predicts that moderately efficacious masks will also lower exposure viral load tenfold among people who get infected despite masking, potentially limiting infection severity. Because peak viral load tends to occur pre-symptomatically, we also identify that antiviral therapy targeting symptomatic individuals is unlikely to impact transmission risk. Instead, antiviral therapy would only lower Re if dosed as post-exposure prophylaxis and if given to ~ 50% of newly infected people within 3 days of an exposure. These results highlight the primacy of masking relative to other biomedical interventions under consideration for limiting the extent of the COVID-19 pandemic prior to widespread implementation of a vaccine. To confirm this prediction, we used a regression model of King County, Washington data and simulated the counterfactual scenario without mask wearing to estimate that in the absence of additional interventions, mask wearing decreased Re from 1.3–1.5 to ~ 1.0 between June and September 2020.

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Endogenous Antibody Responses to SARS-CoV-2 in Patients With Mild or Moderate COVID-19 Who Received Bamlanivimab Alone or Bamlanivimab and Etesevimab Together

Frontiers in Immunology ◽

10.3389/fimmu.2021.790469 ◽

2021 ◽

Vol 12 ◽

Author(s):

Lin Zhang ◽

Josh Poorbaugh ◽

Michael Dougan ◽

Peter Chen ◽

Robert L. Gottlieb ◽

...

Keyword(s):

Immune Response ◽

Viral Load ◽

Humoral Response ◽

Serum Samples ◽

Minimal Impact ◽

Neutralizing Activity ◽

Load Following ◽

Endogenous Immune Response ◽

Antibody Titers ◽

Protein Variants

BackgroundNeutralizing monoclonal antibodies (mAbs) to SARS-CoV-2 are clinically efficacious when administered early, decreasing hospitalization and mortality in patients with mild or moderate COVID-19. We investigated the effects of receiving mAbs (bamlanivimab alone and bamlanivimab and etesevimab together) after SARS-CoV-2 infection on the endogenous immune response.MethodsLongitudinal serum samples were collected from patients with mild or moderate COVID-19 in the BLAZE-1 trial who received placebo (n=153), bamlanivimab alone [700 mg (n=100), 2800 mg (n=106), or 7000 mg (n=98)], or bamlanivimab (2800 mg) and etesevimab (2800 mg) together (n=111). A multiplex Luminex serology assay measured antibody titers against SARS-CoV-2 antigens, including SARS-CoV-2 protein variants that evade bamlanivimab or etesevimab binding, and SARS-CoV-2 pseudovirus neutralization assays were performed.ResultsThe antibody response in patients who received placebo or mAbs had a broad specificity. Titer change from baseline against a receptor-binding domain mutant (Spike-RBD E484Q), as well as N-terminal domain (Spike-NTD) and nucleocapsid protein (NCP) epitopes were 1.4 to 4.1 fold lower at day 15-85 in mAb recipients compared with placebo. Neutralizing activity of day 29 sera from bamlanivimab monotherapy cohorts against both spike E484Q and beta variant (B.1.351) were slightly reduced compared with placebo (by a factor of 3.1, p=0.001, and 2.9, p=0.002, respectively). Early viral load correlated with the subsequent antibody titers of the native, unmodified humoral response (p<0.0001 at Day 15, 29, 60 and 85 for full-length spike).ConclusionsPatients with mild or moderate COVID-19 treated with mAbs develop a wide breadth of antigenic responses to SARS-CoV-2. Small reductions in titers and neutralizing activity, potentially due to a decrease in viral load following mAb treatment, suggest minimal impact of mAb treatment on the endogenous immune response.

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Emergency Antiviral Drug Discovery During a Pandemic - a Case Study on the Application of Natural Compounds to Treat COVID-19

10.26434/chemrxiv.12307592.v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jianfeng Yu ◽

Shengxi Shao ◽

Bin Liu ◽

Zhihao Wang ◽

Yi-Zhou Jiang ◽

...

Keyword(s):

Chlorogenic Acid ◽

In Silico ◽

Vaccine Development ◽

Antiviral Drug ◽

Viral Proteins ◽

Knowledge Based ◽

Protein Functions ◽

The Impact ◽

Nutraceutical Products

The spreading COVID-19 pandemic has brought the world to a halt in 2020. One of the major challenges is the lack of effective antiviral drugs. Drug and vaccine development typically takes years; a practical approach to formulate knowledge-based prescriptions is to conduct in silico screening for drugs and compounds that has the potential to disrupt viral protein functions. By evaluating the dataset from the “Shennong project”, an in silico screening of the DrugBank library against SARS-CoV-2 proteins, we identified chlorogenic acid and rutin displayed a strong affinity with diverse viral proteins. Chlorogenic acid is naturally present in coffee in large quantity, and rutin is available as nutraceutical products, both compounds are considered safe to consume, hence could potentially aid the recovery or treatment for COVID-19 patients at low health risk. We emphasise that the results require further clinical clarification, the impact of this work shall be examined by professionals carefully.

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Divergent COVID-19 Disease Trajectories Predicted by a DAMP-Centered Immune Network Model

Frontiers in Immunology ◽

10.3389/fimmu.2021.754127 ◽

2021 ◽

Vol 12 ◽

Author(s):

Judy D. Day ◽

Soojin Park ◽

Benjamin L. Ranard ◽

Harinder Singh ◽

Carson C. Chow ◽

...

Keyword(s):

Immune Response ◽

Viral Load ◽

Neutralizing Antibodies ◽

Viral Infections ◽

Temporal Dynamics ◽

Severe Disease ◽

Type I ◽

Modeling Framework ◽

Molecular Pattern ◽

Immune Network Model

COVID-19 presentations range from mild to moderate through severe disease but also manifest with persistent illness or viral recrudescence. We hypothesized that the spectrum of COVID-19 disease manifestations was a consequence of SARS-CoV-2-mediated delay in the pathogen-associated molecular pattern (PAMP) response, including dampened type I interferon signaling, thereby shifting the balance of the immune response to be dominated by damage-associated molecular pattern (DAMP) signaling. To test the hypothesis, we constructed a parsimonious mechanistic mathematical model. After calibration of the model for initial viral load and then by varying a few key parameters, we show that the core model generates four distinct viral load, immune response and associated disease trajectories termed “patient archetypes”, whose temporal dynamics are reflected in clinical data from hospitalized COVID-19 patients. The model also accounts for responses to corticosteroid therapy and predicts that vaccine-induced neutralizing antibodies and cellular memory will be protective, including from severe COVID-19 disease. This generalizable modeling framework could be used to analyze protective and pathogenic immune responses to diverse viral infections.

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