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

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.

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

2021 ◽  
Vol 12 ◽  
Author(s):  
Filippo Castiglione ◽  
Debashrito Deb ◽  
Anurag P. Srivastava ◽  
Pietro Liò ◽  
Arcangelo Liso
Keyword(s):  
Immune Response ◽  
Viral Load ◽  
In Silico ◽  
Vaccine Development ◽  
Real Life ◽  
Humoral Response ◽  
Population Level ◽  
Immune Competence ◽  
Modeling Framework ◽  
The Impact

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.

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

Vaccines ◽  
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.

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

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.

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

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.

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

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.

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

2020 ◽  
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

<p>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 <i>in silico </i>screening for drugs and compounds that has the potential to disrupt viral protein functions. By evaluating the dataset from the “Shennong project”, an <i>in silico</i> 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.</p>

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

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.

scholarly journals Effect of Different Disease-Modifying Therapies on Humoral Response to BNT162b2 Vaccine in Sardinian Multiple Sclerosis Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Maristella Pitzalis ◽  
Maria Laura Idda ◽  
Valeria Lodde ◽  
Annalisa Loizedda ◽  
Monia Lobina ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Immune Response ◽  
Smoking Status ◽  
Humoral Response ◽  
Disease Modifying Therapies ◽  
Significant Difference ◽  
Disease Modifying ◽  
Multiple Sclerosis Patients ◽  
Humoral Responses ◽  
The Impact

ObjectivesVaccination against COVID-19 is highly recommended to patients affected by multiple sclerosis (MS); however, the impact of MS disease-modifying therapies (DMTs) on the immune response following vaccination has been only partially investigated. Here, we aimed to elucidate the effect of DMTs on the humoral immune response to mRNA-based anti-SARS-CoV-2 vaccines in MS patients.MethodsWe obtained sera from 912 Sardinian MS patients and 63 healthy controls 30 days after the second dose of BNT162b2 vaccine and tested them for SARS-CoV-2 response using anti-Spike (S) protein-based serology. Previous SARS-CoV-2 infection was assessed by anti-Nucleocapsid (N) serology. Patients were either untreated or undergoing treatment with a total of 13 different DMTs. Differences between treatment groups comprised of at least 10 patients were assessed by generalized linear mixed-effects model. Demographic and clinical data and smoking status were analyzed as additional factors potentially influencing humoral immunity from COVID-19 vaccine.ResultsMS patients treated with natalizumab, teriflunomide, azathioprine, fingolimod, ocrelizumab, and rituximab showed significantly lower humoral responses compared to untreated patients. We did not observe a statistically significant difference in response between patients treated with the other drugs (dimethyl fumarate, interferon, alemtuzumab and glatiramer acetate) and untreated patients. In addition, older age, male sex and active smoking were significantly associated with lower antibody titers against SARS-CoV-2. MS patients previously infected with SARS-CoV-2 had significantly higher humoral responses to vaccine than uninfected patients.ConclusionHumoral response to BNT162b2 is significantly influenced by the specific DMTs followed by patients, as well as by other factors such as previous SARS-CoV-2 infection, age, sex, and smoking status. These results are important to inform targeted strategies to prevent clinically relevant COVID-19 in MS patients.

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

2020 ◽  
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

<p>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 <i>in silico </i>screening for drugs and compounds that has the potential to disrupt viral protein functions. By evaluating the dataset from the “Shennong project”, an <i>in silico</i> 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.</p>

scholarly journals Effect of different disease-modifying therapies on humoral response to BNT162b2 vaccine in Sardinian multiple sclerosis patients

2021 ◽  
Author(s):  
Maristella Pitzalis ◽  
Maria Laura Idda ◽  
Valeria Lodde ◽  
Annalisa Loizedda ◽  
Monia Lobina ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Immune Response ◽  
Smoking Status ◽  
Humoral Response ◽  
Disease Modifying Therapies ◽  
Significant Difference ◽  
Disease Modifying ◽  
Multiple Sclerosis Patients ◽  
Humoral Responses ◽  
The Impact

Objectives: Vaccination against COVID-19 is highly recommended to patients affected by multiple sclerosis (MS); however, the impact of MS disease-modifying therapies (DMTs) on the immune response following vaccination has been only partially investigated. Here, we aimed to elucidate the effect of DMTs on the humoral immune response to mRNA-based anti-SARS-CoV-2 vaccines in MS patients. Methods: We obtained sera from 912 Sardinian MS patients and 63 healthy controls 30 days after the second dose of BNT162b2 vaccine and tested them for SARS-CoV-2 response using anti-Spike (S) protein-based serology. Previous SARS-CoV-2 infection was assessed by anti-Nucleocapsid (N) serology. Patients were either untreated or undergoing treatment with a total of 13 different DMTs. Differences between treatment groups comprised of at least 10 patients were assessed by generalized linear mixed-effects model. Demographic and clinical data and smoking status were analyzed as additional factors potentially influencing humoral immunity from COVID-19 vaccine. Results: MS patients treated with natalizumab, teriflunomide, azathioprine, fingolimod, ocrelizumab, and rituximab showed significantly lower humoral responses compared to untreated patients. We did not observe a statistically significant difference in response between patients treated with the other drugs (dimethyl fumarate, interferon, alemtuzumab and glatiramer acetate) and untreated patients. In addition, older age, male sex and active smoking were significantly associated with lower antibody titers against SARS-CoV-2. MS patients previously infected with SARS-CoV-2 had significantly higher humoral responses to vaccine than uninfected patients. Conclusion: Humoral response to BNT162b2 is significantly influenced by the specific DMTs followed by patients, as well as by other factors such as previous SARS-CoV-2 infection, age, sex, and smoking status. These results are important to inform targeted strategies to prevent clinically relevant COVID-19 in MS patients.

Sign in / Sign up

Export Citation Format

Share Document