SARS-CoV-2 viral dynamics in non-human primates

SARS-CoV-2 epidemiology, kinetics, and evolution: A narrative review

Since winter 2019, SARS-CoV-2 has emerged, spread, and evolved all around the globe. We explore 4 y of evolutionary epidemiology of this virus, ranging from the applied public health challenges to the more conceptual evolutionary biology perspectives. Through this review, we first present the spread and lethality of the infections it causes, starting from its emergence in Wuhan (China) from the initial epidemics all around the world, compare the virus to other betacoronaviruses, focus on its airborne transmission, compare containment strategies ("zero-COVID" vs. "herd immunity"), explain its phylogeographical tracking, underline the importance of natural selection on the epidemics, mention its within-host population dynamics. Finally, we discuss how the pandemic has transformed (or should transform) the surveillance and prevention of viral respiratory infections and identify perspectives for the research on epidemiology of COVID-19.

Translating animal models of SARS-CoV-2 infection to vascular, neurological and gastrointestinal manifestations of COVID-19

Since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) initiated a global pandemic resulting in an estimated 775 million infections with over 7 million deaths, it has become evident that COVID-19 is not solely a pulmonary disease. Emerging evidence has shown that, in a subset of patients, certain symptoms - including chest pain, stroke, anosmia, dysgeusia, diarrhea and abdominal pain - all indicate a role of vascular, neurological and gastrointestinal (GI) pathology in the disease process. Many of these disease processes persist long after the acute disease has been resolved, resulting in 'long COVID' or post-acute sequelae of COVID-19 (PASC). The molecular mechanisms underlying the acute and systemic conditions associated with COVID-19 remain incompletely defined. Appropriate animal models provide a method of understanding underlying disease mechanisms at the system level through the study of disease progression, tissue pathology, immune system response to the pathogen and behavioral responses. However, very few studies have addressed PASC and whether existing models hold promise for studying this challenging problem. Here, we review the current literature on cardiovascular, neurological and GI pathobiology caused by COVID-19 in patients, along with established animal models of the acute disease manifestations and their prospects for use in PASC studies. Our aim is to provide guidance for the selection of appropriate models in order to recapitulate certain aspects of the disease to enhance the translatability of mechanistic studies.

Neuroinvasion via Peripheral Nerves in Epidemic Viral Encephalitis Caused by Enterovirus, Orthoflavivirus and SARS-Coronavirus

Pathogens invade the central nervous system (CNS) and cause infections either through the haematogenous route or via peripheral nerves. Neuroinvasion via peripheral nerves, involving spinal or cranial somatic nerves, is well-established for certain viral encephalitides such as rabies, herpes simplex encephalitis, and poliomyelitis. Advances in understanding emerging and re-emerging viruses that cause epidemic CNS infections have highlighted the growing importance of peripheral nerve pathways in viral neuroinvasion. This review focuses on epidemic viral encephalitides caused by three groups of RNA viruses, viz., enteroviruses (enterovirus A71 and enterovirus D68), orthoflaviviruses (West Nile virus and Japanese encephalitis virus), and severe acute respiratory syndrome coronaviruses (mainly severe acute respiratory coronavirus-2). We examine evidence supporting the hypothesis that peripheral nerve viral transmission may play an increasingly significant if not more critical role than the haematogenous route in neuroinvasion.

Viral Rebound After Antiviral Treatment: A Mathematical Modeling Study of the Role of Antiviral Mechanism of Action

The development of antiviral treatments for SARS-CoV-2 was an important turning point for the pandemic. Availability of safe and effective antivirals has allowed people to return back to normal life. While SARS-CoV-2 antivirals are highly effective at preventing severe disease, there have been concerning reports of viral rebound in some patients after cessation of antiviral treatment. In this study, we use a mathematical model of viral infection to study the potential of different antivirals to prevent viral rebound. We find that antivirals that block production are most likely to result in viral rebound if the treatment time course is not sufficiently long. Since these antivirals do not prevent infection of cells, cells continue to be infected during treatment. When treatment is stopped, the infected cells will begin producing virus at the usual rate. Antivirals that prevent infection of cells are less likely to result in viral rebound since cells are not being infected during treatment. This study highlights the role of antiviral mechanism of action in increasing or reducing the probability of viral rebound.

The kinetics of SARS-CoV-2 infection based on a human challenge study

Studying the early events that occur after viral infection in humans is difficult unless one intentionally infects volunteers in a human challenge study. Here, we use data about severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in such a study in combination with mathematical modeling to gain insights into the relationship between the amount of virus in the upper respiratory tract and the immune response it generates. We propose a set of dynamic models of increasing complexity to dissect the roles of target cell limitation, innate immunity, and adaptive immunity in determining the observed viral kinetics. We introduce an approach for modeling the effect of humoral immunity that describes a decline in infectious virus after immune activation. We fit our models to viral load and infectious titer data from all the untreated infected participants in the study simultaneously. We found that a power-law with a power h < 1 describes the relationship between infectious virus and viral load. Viral replication at the early stage of infection is rapid, with a doubling time of ~2 h for viral RNA and ~3 h for infectious virus. We estimate that adaptive immunity is initiated ~7 to 10 d postinfection and appears to contribute to a multiphasic viral decline experienced by some participants; the viral rebound experienced by other participants is consistent with a decline in the interferon response. Altogether, we quantified the kinetics of SARS-CoV-2 infection, shedding light on the early dynamics of the virus and the potential role of innate and adaptive immunity in promoting viral decline during infection.

Antiviral effect of Evusheld in COVID-19 hospitalized patients infected with pre-Omicron or Omicron variants: a modelling analysis of the randomized DisCoVeRy trial

BACKGROUND

The antiviral efficacy of Evusheld (AZD7442) in patients hospitalized for SARS-CoV-2 is unknown.

METHODS

We analysed the evolution of both the nasopharyngeal viral load and the serum neutralization activity against the variant of infection in 199 hospitalized patients (109 treated with Evusheld, 90 treated with placebo) infected with the SARS-CoV-2 virus and included in the randomized, double-blind, trial DisCoVeRy (NCT04315948). Using a mechanistic mathematical model, we reconstructed the trajectories of viral kinetics and how they are modulated by the increase in serum neutralization activity during Evusheld treatment.

RESULTS

Our model identified that the neutralization activity was associated with viral kinetics. Reflecting the variant-dependent neutralization activity of Evusheld, the antiviral activity of Evusheld was larger in patients infected with pre-Omicron or Omicron BA.2 variants than in patients infected with Omicron BA.1 variant. More specifically, the model predicted that Evusheld reduced the median time to viral clearance compared with placebo-treated patients by more than 5 days in patients infected by pre-Omicron (median: 5.9; 80% PI: 2.1-13.6) or Omicron BA.2 (median: 5.4; 80% PI: 2.0-12.4), respectively. The effect was more modest in patients infected by the Omicron BA.1 variant, reducing the median time to viral clearance by 2 days (median: 2.2; 80% PI: 0.4-8.9).

CONCLUSIONS

Hospitalized patients treated with Evusheld had a shorter median time to SARS-CoV-2 viral clearance. As Evusheld antiviral activity is mediated by the level of neutralization activity, its impact on viral clearance varies largely according to the variant of infection.

Heterogeneous SARS-CoV-2 kinetics due to variable timing and intensity of immune responses

The viral kinetics of documented SARS-CoV-2 infections exhibit a high degree of interindividual variability. We identified 6 distinct viral shedding patterns, which differed according to peak viral load, duration, expansion rate, and clearance rate, by clustering data from 768 infections in the National Basketball Association cohort. Omicron variant infections in previously vaccinated individuals generally led to lower cumulative shedding levels of SARS-CoV-2 than other scenarios. We then developed a mechanistic mathematical model that recapitulated 1,510 observed viral trajectories, including viral rebound and cases of reinfection. Lower peak viral loads were explained by a more rapid and sustained transition of susceptible cells to a refractory state during infection as well as by an earlier and more potent late, cytolytic immune response. Our results suggest that viral elimination occurs more rapidly during Omicron infection, following vaccination, and following reinfection due to enhanced innate and acquired immune responses. Because viral load has been linked with COVID-19 severity and transmission risk, our model provides a framework for understanding the wide range of observed SARS-CoV-2 infection outcomes.

How robust are estimates of key parameters in standard viral dynamic models?

Mathematical models of viral infection have been developed, fitted to data, and provide insight into disease pathogenesis for multiple agents that cause chronic infection, including HIV, hepatitis C, and B virus. However, for agents that cause acute infections or during the acute stage of agents that cause chronic infections, viral load data are often collected after symptoms develop, usually around or after the peak viral load. Consequently, we frequently lack data in the initial phase of viral growth, i.e., when pre-symptomatic transmission events occur. Missing data may make estimating the time of infection, the infectious period, and parameters in viral dynamic models, such as the cell infection rate, difficult. However, having extra information, such as the average time to peak viral load, may improve the robustness of the estimation. Here, we evaluated the robustness of estimates of key model parameters when viral load data prior to the viral load peak is missing, when we know the values of some parameters and/or the time from infection to peak viral load. Although estimates of the time of infection are sensitive to the quality and amount of available data, particularly pre-peak, other parameters important in understanding disease pathogenesis, such as the loss rate of infected cells, are less sensitive. Viral infectivity and the viral production rate are key parameters affecting the robustness of data fits. Fixing their values to literature values can help estimate the remaining model parameters when pre-peak data is missing or limited. We find a lack of data in the pre-peak growth phase underestimates the time to peak viral load by several days, leading to a shorter predicted growth phase. On the other hand, knowing the time of infection (e.g., from epidemiological data) and fixing it results in good estimates of dynamical parameters even in the absence of early data. While we provide ways to approximate model parameters in the absence of early viral load data, our results also suggest that these data, when available, are needed to estimate model parameters more precisely.

Modeling the emergence of viral resistance for SARS-CoV-2 during treatment with an anti-spike monoclonal antibody

To mitigate the loss of lives during the COVID-19 pandemic, emergency use authorization was given to several anti-SARS-CoV-2 monoclonal antibody (mAb) therapies for the treatment of mild-to-moderate COVID-19 in patients with a high risk of progressing to severe disease. Monoclonal antibodies used to treat SARS-CoV-2 target the spike protein of the virus and block its ability to enter and infect target cells. Monoclonal antibody therapy can thus accelerate the decline in viral load and lower hospitalization rates among high-risk patients with variants susceptible to mAb therapy. However, viral resistance has been observed, in some cases leading to a transient viral rebound that can be as large as 3-4 orders of magnitude. As mAbs represent a proven treatment choice for SARS-CoV-2 and other viral infections, evaluation of treatment-emergent mAb resistance can help uncover underlying pathobiology of SARS-CoV-2 infection and may also help in the development of the next generation of mAb therapies. Although resistance can be expected, the large rebounds observed are much more difficult to explain. We hypothesize replenishment of target cells is necessary to generate the high transient viral rebound. Thus, we formulated two models with different mechanisms for target cell replenishment (homeostatic proliferation and return from an innate immune response antiviral state) and fit them to data from persons with SARS-CoV-2 treated with a mAb. We showed that both models can explain the emergence of resistant virus associated with high transient viral rebounds. We found that variations in the target cell supply rate and adaptive immunity parameters have a strong impact on the magnitude or observability of the viral rebound associated with the emergence of resistant virus. Both variations in target cell supply rate and adaptive immunity parameters may explain why only some individuals develop observable transient resistant viral rebound. Our study highlights the conditions that can lead to resistance and subsequent viral rebound in mAb treatments during acute infection.

Within-host evolution of SARS-CoV-2: how often are de novo mutations transmitted from symptomatic infections?

Despite a relatively low mutation rate, the large number of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections has allowed for substantial genetic change, leading to a multitude of emerging variants. Using a recently determined mutation rate (per site replication), as well as within-host parameter estimates for symptomatic SARS-CoV-2 infection, we apply a stochastic transmission-bottleneck model to describe the survival probability of de novo SARS-CoV-2 mutations as a function of bottleneck size and selection coefficient. For narrow bottlenecks, we find that mutations affecting per-target-cell attachment rate (with phenotypes associated with fusogenicity and ACE2 binding) have similar transmission probabilities to mutations affecting viral load clearance (with phenotypes associated with humoral evasion). We further find that mutations affecting the eclipse rate (with phenotypes associated with reorganization of cellular metabolic processes and synthesis of viral budding precursor material) are highly favoured relative to all other traits examined. We find that mutations leading to reduced removal rates of infected cells (with phenotypes associated with innate immune evasion) have limited transmission advantage relative to mutations leading to humoral evasion. Predicted transmission probabilities, however, for mutations affecting innate immune evasion are more consistent with the range of clinically estimated household transmission probabilities for de novo mutations. This result suggests that although mutations affecting humoral evasion are more easily transmitted when they occur, mutations affecting innate immune evasion may occur more readily. We examine our predictions in the context of a number of previously characterized mutations in circulating strains of SARS-CoV-2. Our work offers both a null model for SARS-CoV-2 mutation rates and predicts which aspects of viral life history are most likely to successfully evolve, despite low mutation rates and repeated transmission bottlenecks.

Heterogeneous SARS-CoV-2 kinetics due to variable timing and intensity of immune responses

The viral kinetics of documented SARS-CoV-2 infections exhibit a high degree of inter-individual variability. We identified six distinct viral shedding patterns, which differed according to peak viral load, duration, expansion rate and clearance rate, by clustering data from 768 infections in the National Basketball Association cohort. Omicron variant infections in previously vaccinated individuals generally led to lower cumulative shedding levels of SARS-CoV-2 than other scenarios. We then developed a mechanistic mathematical model that recapitulated 1510 observed viral trajectories, including viral rebound and cases of reinfection. Lower peak viral loads were explained by a more rapid and sustained transition of susceptible cells to a refractory state during infection, as well as an earlier and more potent late, cytolytic immune response. Our results suggest that viral elimination occurs more rapidly during omicron infection, following vaccination, and following re-infection due to enhanced innate and acquired immune responses. Because viral load has been linked with COVID-19 severity and transmission risk, our model provides a framework for understanding the wide range of observed SARS-CoV-2 infection outcomes.

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.

Modeling the emergence of viral resistance for SARS-CoV-2 during treatment with an anti-spike monoclonal antibody

The COVID-19 pandemic has led to over 760 million cases and 6.9 million deaths worldwide. To mitigate the loss of lives, emergency use authorization was given to several anti-SARS-CoV-2 monoclonal antibody (mAb) therapies for the treatment of mild-to-moderate COVID-19 in patients with a high risk of progressing to severe disease. Monoclonal antibodies used to treat SARS-CoV-2 target the spike protein of the virus and block its ability to enter and infect target cells. Monoclonal antibody therapy can thus accelerate the decline in viral load and lower hospitalization rates among high-risk patients with susceptible variants. However, viral resistance has been observed, in some cases leading to a transient viral rebound that can be as large as 3-4 orders of magnitude. As mAbs represent a proven treatment choice for SARS-CoV-2 and other viral infections, evaluation of treatment-emergent mAb resistance can help uncover underlying pathobiology of SARS-CoV-2 infection and may also help in the development of the next generation of mAb therapies. Although resistance can be expected, the large rebounds observed are much more difficult to explain. We hypothesize replenishment of target cells is necessary to generate the high transient viral rebound. Thus, we formulated two models with different mechanisms for target cell replenishment (homeostatic proliferation and return from an innate immune response anti-viral state) and fit them to data from persons with SARS-CoV-2 treated with a mAb. We showed that both models can explain the emergence of resistant virus associated with high transient viral rebounds. We found that variations in the target cell supply rate and adaptive immunity parameters have a strong impact on the magnitude or observability of the viral rebound associated with the emergence of resistant virus. Both variations in target cell supply rate and adaptive immunity parameters may explain why only some individuals develop observable transient resistant viral rebound. Our study highlights the conditions that can lead to resistance and subsequent viral rebound in mAb treatments during acute infection.

Animal models for COVID-19 and tuberculosis

Respiratory infections cause tremendous morbidity and mortality worldwide. Amongst these diseases, tuberculosis (TB), a bacterial illness caused by Mycobacterium tuberculosis which often affects the lung, and coronavirus disease 2019 (COVID-19) caused by the Severe Acute Respiratory Syndrome Coronavirus type 2 (SARS-CoV-2), stand out as major drivers of epidemics of global concern. Despite their unrelated etiology and distinct pathology, these infections affect the same vital organ and share immunopathogenesis traits and an imperative demand to model the diseases at their various progression stages and localizations. Due to the clinical spectrum and heterogeneity of both diseases experimental infections were pursued in a variety of animal models. We summarize mammalian models employed in TB and COVID-19 experimental investigations, highlighting the diversity of rodent models and species peculiarities for each infection. We discuss the utility of non-human primates for translational research and emphasize on the benefits of non-conventional experimental models such as livestock. We epitomize advances facilitated by animal models with regard to understanding disease pathophysiology and immune responses. Finally, we highlight research areas necessitating optimized models and advocate that research of pulmonary infectious diseases could benefit from cross-fertilization between studies of apparently unrelated diseases, such as TB and COVID-19.

Modelling the viral dynamics of the SARS-CoV-2 Delta and Omicron variants in different cell types

We use viral kinetic models fitted to viral load data from in vitro studies to explain why the SARS-CoV-2 Omicron variant replicates faster than the Delta variant in nasal cells, but slower than Delta in lung cells, which could explain Omicron's higher transmission potential and lower severity. We find that in both nasal and lung cells, viral infectivity is higher for Omicron but the virus production rate is higher for Delta, with an estimated approximately 200-fold increase in infectivity and 100-fold decrease in virus production when comparing Omicron with Delta in nasal cells. However, the differences are unequal between cell types, and ultimately lead to the basic reproduction number and growth rate being higher for Omicron in nasal cells, and higher for Delta in lung cells. In nasal cells, Omicron alone can enter via a TMPRSS2-independent pathway, but it is primarily increased efficiency of TMPRSS2-dependent entry which accounts for Omicron's increased activity. This work paves the way for using within-host mathematical models to understand the transmission potential and severity of future variants.

Monkeypox: a review of epidemiological modelling studies and how modelling has led to mechanistic insight

Human monkeypox (mpox) virus is a viral zoonosis that belongs to the Orthopoxvirus genus of the Poxviridae family, which presents with similar symptoms as those seen in human smallpox patients. Mpox is an increasing concern globally, with over 80,000 cases in non-endemic countries as of December 2022. In this review, we provide a brief history and ecology of mpox, its basic virology, and the key differences in mpox viral fitness traits before and after 2022. We summarize and critique current knowledge from epidemiological mathematical models, within-host models, and between-host transmission models using the One Health approach, where we distinguish between models that focus on immunity from vaccination, geography, climatic variables, as well as animal models. We report various epidemiological parameters, such as the reproduction number, R0, in a condensed format to facilitate comparison between studies. We focus on how mathematical modelling studies have led to novel mechanistic insight into mpox transmission and pathogenesis. As mpox is predicted to lead to further infection peaks in many historically non-endemic countries, mathematical modelling studies of mpox can provide rapid actionable insights into viral dynamics to guide public health measures and mitigation strategies.

Multiple cohort study of hospitalized SARS-CoV-2 in-host infection dynamics: Parameter estimates, identifiability, sensitivity and the eclipse phase profile

Within-host SARS-CoV-2 modelling studies have been published throughout the COVID-19 pandemic. These studies contain highly variable numbers of individuals and capture varying timescales of pathogen dynamics; some studies capture the time of disease onset, the peak viral load and subsequent heterogeneity in clearance dynamics across individuals, while others capture late-time post-peak dynamics. In this study, we curate multiple previously published SARS-CoV-2 viral load data sets, fit these data with a consistent modelling approach, and estimate the variability of in-host parameters including the basic reproduction number, R0, as well as the best-fit eclipse phase profile. We find that fitted dynamics can be highly variable across data sets, and highly variable within data sets, particularly when key components of the dynamic trajectories (e.g. peak viral load) are not represented in the data. Further, we investigated the role of the eclipse phase time distribution in fitting SARS-CoV-2 viral load data. By varying the shape parameter of an Erlang distribution, we demonstrate that models with either no eclipse phase, or with an exponentially-distributed eclipse phase, offer significantly worse fits to these data, whereas models with less dispersion around the mean eclipse time (shape parameter two or more) offered the best fits to the available data across all data sets used in this work. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Simplified within-host and Dose-response Models of SARS-CoV-2

Understanding the mechanistic dynamics of transmission is key to designing more targeted and effective interventions to limit the spread of infectious diseases. A well-described within-host model allows explicit simulation of how infectiousness changes over time at an individual level. This can then be coupled with dose-response models to investigate the impact of timing on transmission. We collected and compared a range of within-host models used in previous studies and identified a minimally-complex model that provides suitable within-host dynamics while keeping a reduced number of parameters to allow inference and limit unidentifiability issues. Furthermore, non-dimensionalised models were developed to further overcome the uncertainty in estimates of the size of the susceptible cell population, a common problem in many of these approaches. We will discuss these models, and their fit to data from the human challenge study (see Killingley et al. (2022)) for SARS-CoV-2 and the model selection results, which has been performed using ABC-SMC. The parameter posteriors have then used to simulate viral-load based infectiousness profiles via a range of dose-response models, which illustrate the large variability of the periods of infection window observed for COVID-19.

Animal Models for the Study of SARS-CoV-2-Induced Respiratory Disease and Pathology

Emergence of the betacoronavirus SARS-CoV-2 has resulted in a historic pandemic, with millions of deaths worldwide. An unprecedented effort has been made by the medical, scientific, and public health communities to rapidly develop and implement vaccines and therapeutics to prevent and reduce hospitalizations and deaths. Although SARS-CoV-2 infection can lead to disease in many organ systems, the respiratory system is its main target, with pneumonia and acute respiratory distress syndrome as the hallmark features of severe disease. The large number of patients who have contracted COVID-19 infections since 2019 has permitted a detailed characterization of the clinical and pathologic features of the disease in humans. However, continued progress in the development of effective preventatives and therapies requires a deeper understanding of the pathogenesis of infection. Studies using animal models are necessary to complement in vitro findings and human clinical data. Multiple animal species have been evaluated as potential models for studying the respiratory disease caused by SARSCoV-2 infection. Knowing the similarities and differences between animal and human responses to infection is critical for effective translation of animal data into human medicine. This review provides a detailed summary of the respiratory disease and associated pathology induced by SARS-CoV-2 infection in humans and compares them with the disease that develops in 3 commonly used models: NHP, hamsters, and mice. The effective use of animals to study SARS-CoV-2-induced respiratory disease will enhance our understanding of SARS-CoV-2 pathogenesis, allow the development of novel preventatives and therapeutics, and aid in the preparation for the next emerging virus with pandemic potential.

Long-term durability of immune responses to the BNT162b2 and mRNA-1273 vaccines based on dosage, age and sex

The lipid nanoparticle (LNP)-formulated mRNA vaccines BNT162b2 and mRNA-1273 are a widely adopted multi vaccination public health strategy to manage the COVID-19 pandemic. Clinical trial data has described the immunogenicity of the vaccine, albeit within a limited study time frame. Here, we use a within-host mathematical model for LNP-formulated mRNA vaccines, informed by available clinical trial data from 2020 to September 2021, to project a longer term understanding of immunity as a function of vaccine type, dosage amount, age, and sex. We estimate that two standard doses of either mRNA-1273 or BNT162b2, with dosage times separated by the company-mandated intervals, results in individuals losing more than 99% humoral immunity relative to peak immunity by 8 months following the second dose. We predict that within an 8 month period following dose two (corresponding to the original CDC time-frame for administration of a third dose), there exists a period of time longer than 1 month where an individual has lost more than 99% humoral immunity relative to peak immunity, regardless of which vaccine was administered. We further find that age has a strong influence in maintaining humoral immunity; by 8 months following dose two we predict that individuals aged 18-55 have a four-fold humoral advantage compared to aged 56-70 and 70+ individuals. We find that sex has little effect on the immune response and long-term IgG counts. Finally, we find that humoral immunity generated from two low doses of mRNA-1273 decays at a substantially slower rate relative to peak immunity gained compared to two standard doses of either mRNA-1273 or BNT162b2. Our predictions highlight the importance of the recommended third booster dose in order to maintain elevated levels of antibodies.

BioSimia, France CNRS network for nonhuman primate biomedical research in infectiology, immunology, and neuroscience

Research and developments based on nonhuman primate models have a specific place in biomedical sciences, and nonhuman primate species also have a specific place in the public opinion on the use of animal in research. While nonhuman primates are used in very limited number compared to other animal models, they are rightly the focus of deep ethical concerns. The importance of nonhuman primates in neuroscientific fundamental and preclinical discoveries together with the targeting of such research by activist groups well illustrate this fact. Nonhuman primates also highly contribute to other biomedical fields including immunology, virology, or metabolic and respiratory physiology. In all these fields, researchers, engineers and technicians face similar matters and share the same needs for optimal animal welfare, handling, and veterinary care, the same quest for first-rate research infrastructure and funding, and the same yearning for more public understanding and support. In this article, we give an overview of the evolution of human-animal relationships and public attitudes to animal research in France, and we recount the creation of BioSimia, France network for nonhuman primate biomedical research which now links all academic laboratories nationwide in all the domains for which nonhuman primates remain essential. We explain the principles as well as the outcomes of networking across disciplines. As a perspective, we outline the potential benefits of extending such network to a European scale.

Identifiability of parameters in mathematical models of SARS-CoV-2 infections in humans

Determining accurate estimates for the characteristics of the severe acute respiratory syndrome coronavirus 2 in the upper and lower respiratory tracts, by fitting mathematical models to data, is made difficult by the lack of measurements early in the infection. To determine the sensitivity of the parameter estimates to the noise in the data, we developed a novel two-patch within-host mathematical model that considered the infection of both respiratory tracts and assumed that the viral load in the lower respiratory tract decays in a density dependent manner and investigated its ability to match population level data. We proposed several approaches that can improve practical identifiability of parameters, including an optimal experimental approach, and found that availability of viral data early in the infection is of essence for improving the accuracy of the estimates. Our findings can be useful for designing interventions.

Modeling within-host and aerosol dynamics of SARS-CoV-2: The relationship with infectiousness

The relationship between transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the amount of virus present in the proximity of a susceptible host is not understood. Here, we developed a within-host and aerosol mathematical model and used it to determine the relationship between viral kinetics in the upper respiratory track, viral kinetics in the aerosols, and new transmissions in golden hamsters challenged with SARS-CoV-2. We determined that infectious virus shedding early in infection correlates with transmission events, shedding of infectious virus diminishes late in the infection, and high viral RNA levels late in the infection are a poor indicator of transmission. We further showed that viral infectiousness increases in a density dependent manner with viral RNA and that their relative ratio is time-dependent. Such information is useful for designing interventions.

Quantifying the relationship between SARS-CoV-2 dynamics in upper respiratory tract and in aerosols is key to understanding SARS-CoV-2 transmission and evaluating intervention strategies. Of particular interest is the link between the viral RNA measured by PCR and a subject’s infectiousness. Here, we developed a mechanistic model of viral transmission in golden hamsters and used data in upper respiratory tract and aerosols to evaluate key within-host and environment based viral parameters. The significance of our research is in identifying the timing and duration of viral shedding, how long it stays infectious, and the link between infectious virus and total viral RNA. Such knowledge enhances our understanding of the SARS-CoV-2 transmission window.

The area under the effect curve as an efficacy determinant for anti‐infectives

Pharmacokinetic/pharmacodynamic (PK/PD) indices making use of area under the curve, maximum concentration, and the duration that in vivo drug concentration is maintained above a critical level are commonly applied to clinical dose prediction from animal efficacy experiments in the infectious disease arena. These indices make suboptimal use of the nonclinical data, and the prediction depends on the shape of the PK profiles in the animals, determined by the species‐specific absorption, distribution, metabolism, and elimination properties, and the dosing regimen used in the efficacy experiments. Motivated by the principle that efficacy is driven by pharmacology, we conducted simulations using a generalized pathogen dynamic model, to assess the properties of an alternative efficacy predictor: the area under the effect curve (AUEC), computed using in vitro PD and in vivo PK. Across a wide range of hypothetical scenarios, the AUEC consistently showed regimen‐independent strong correlation (R² 0.76–0.98) with in vivo efficacy, superior to all other indices. These findings serve as proof of concept that AUEC should be considered in practice as a translation tool for cross‐species dose prediction. Using AUEC for clinical dose prediction could also potentially cut down animal use by reducing or avoiding dose fractionation experiments.

Modelling the response to vaccine in non-human primates to define SARS-CoV-2 mechanistic correlates of protection

The definition of correlates of protection is critical for the development of next-generation SARS-CoV-2 vaccine platforms. Here, we propose a model-based approach for identifying mechanistic correlates of protection based on mathematical modelling of viral dynamics and data mining of immunological markers. The application to three different studies in non-human primates evaluating SARS-CoV-2 vaccines based on CD40-targeting, two-component spike nanoparticle and mRNA 1273 identifies and quantifies two main mechanisms that are a decrease of rate of cell infection and an increase in clearance of infected cells. Inhibition of RBD binding to ACE2 appears to be a robust mechanistic correlate of protection across the three vaccine platforms although not capturing the whole biological vaccine effect. The model shows that RBD/ACE2 binding inhibition represents a strong mechanism of protection which required significant reduction in blocking potency to effectively compromise the control of viral replication.

The Basic Reproduction Number and Delayed Action of T Cells for Patients Infected with SARS-CoV-2

COVID-19 has been prevalent for the last two years. The transmission capacity of SARS-CoV-2 differs under the influence of different epidemic prevention policies, making it difficult to measure the infectivity of the virus itself. In order to evaluate the infectivity of SARS-CoV-2 in patients with different diseases, we constructed a viral kinetic model by adding the effects of T cells and antibodies. To analyze and compare the delay time of T cell action in patients with different symptoms, we constructed a delay differential equation model. Through the first model, we found that the basic reproduction number of severe patients is greater than that of mild patients, and accordingly, we constructed classification criteria for severe and mild patients. Through the second model, we found that the delay time of T cell action in severe patients is much longer than that in mild patients, and accordingly, we present suggestions for the prevention, diagnosis, and treatment of different patients.

Optimal control design incorporating vaccination and treatment on six compartment pandemic dynamical system

In this paper, a mathematical model of the COVID-19 pandemic with lockdown that provides a more accurate representation of the infection rate has been analyzed. In this model, the total population is divided into six compartments: the susceptible class, lockdown class, exposed class, asymptomatic infected class, symptomatic infected class, and recovered class. The basic reproduction number (R0) is calculated using the next-generation matrix method and presented graphically based on different progression rates and effective contact rates of infective individuals. The COVID-19 epidemic model exhibits the disease-free equilibrium and endemic equilibrium. The local and global stability analysis has been done at the disease-free and endemic equilibrium based on R0. The stability analysis of the model shows that the disease-free equilibrium is both locally and globally stable when R0<1, and the endemic equilibrium is locally and globally stable when R0>1 under some conditions. A control strategy including vaccination and treatment has been studied on this pandemic model with an objective functional to minimize. Finally, numerical simulation of the COVID-19 outbreak in India is carried out using MATLAB, highlighting the usefulness of the COVID-19 pandemic model and its mathematical analysis.

Natural SARS-CoV-2 Infection in a Free-Ranging Black-Tailed Marmoset (Mico melanurus) from an Urban Area in Mid-West Brazil

The emergence of spillover pathogens such as SARS-CoV-2 is a risk to vulnerable human populations. We report natural SARS-CoV-2 infection in a free-ranging adult female black-tailed marmoset (Mico melanurus) from an urban area of Cuiabá, Mato Grosso State, Brazil. The animal was found after a motor vehicle collision without previous clinical history. Necropsy confirmed polytrauma. Severe multifocal to coalescent haemorrhage and mild multifocal peribronchial lymphocytic hyperplasia were seen in lung sections. The alveolar septa were multifocally expanded by a few lymphocytes. Mild lymphocytic periportal hepatitis and interstitial nephritis were found. The lymphoid nodules of the large intestine showed marked lymphocytic hyperplasia. Infection by SARS-CoV-2 was established by viral RNA detection in a pool of nasopharyngeal and oropharyngeal swabs and liver samples. Immunohistochemistry detected the viral nucleocapsid protein in sections of lung, liver, spleen, lymph nodes and large intestine, and spike protein antigen in lung tissue. This is the first report of naturally occurring SARS-CoV-2 infection in a New World monkey. Platyrrhine species should be included as potential hosts of natural infection of SARS-CoV-2.

Modeling explains prolonged SARS-CoV-2 nasal shedding relative to lung shedding in remdesivir treated rhesus macaques

In clinical trials, remdesivir decreased recovery time in hospitalized patients with SARS- CoV-2 and prevented hospitalization when given early during infection, despite not reducing nasal viral loads. In rhesus macaques, early remdesivir prevented pneumonia and lowered lung viral loads, but viral loads increased in nasal passages after five days. We developed mathematical models to explain these results. Our model raises the hypotheses that: 1) in contrast to nasal passages viral load monotonically decreases in lungs during therapy because of infection-dependent generation of refractory cells, 2) slight reduction in lung viral loads with an imperfect agent may result in a substantial decrease in lung damage, and 3) increases in nasal viral load may occur due to a blunting of peak viral load which decreases the intensity of the innate immune response. We demonstrate that a higher potency drug could lower viral loads in nasal passages and lung.

Variant‐specific SARS‐CoV‐2 within‐host kinetics

Since early 2021, SARS‐CoV‐2 variants of concern (VOCs) have been causing epidemic rebounds in many countries. Their properties are well characterized at the epidemiological level but the potential underlying within‐host determinants remain poorly understood. We analyze a longitudinal cohort of 6944 individuals with 14 304 cycle threshold (Ct) values of reverse‐transcription quantitative polymerase chain reaction (RT‐qPCR) VOC screening tests performed in the general population and hospitals in France between February 6 and August 21, 2021. To convert Ct values into numbers of virus copies, we performed an additional analysis using droplet digital PCR (ddPCR). We find that the number of viral genome copies reaches a higher peak value and has a slower decay rate in infections caused by Alpha variant compared to that caused by historical lineages. Following the evidence that viral genome copies in upper respiratory tract swabs are informative on contagiousness, we show that the kinetics of the Alpha variant translate into significantly higher transmission potentials, especially in older populations. Finally, comparing infections caused by the Alpha and Delta variants, we find no significant difference in the peak viral copy number. These results highlight that some of the differences between variants may be detected in virus load variations.

Validation of the Performance of A1HPV6, a Triage Blood Test for the Early Diagnosis and Prognosis of SARS-CoV-2 Infection

Background and Aims

Apolipoprotein A1 (A1) and haptoglobin (HP) serum levels are associated with the spread and severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We have constructed and validated a multivariable risk calculator (A1HPV6) integrating A1, HP, alpha2-macroglobulin, and gamma glutamyl transferase to improve the performances of virological biomarkers.

Methods

In a prospective observational study of hospitalized patients with nonsevere SARS-CoV-2 infection, A1HPV6 was constructed in 127 patients and validated in 116. The specificity was assessed in 7482 controls representing the general population. The primary diagnostic endpoint was the area under the receiver operating characteristic curve in patients with positive SARS-CoV-2 PCR. The primary prognostic endpoint was the age-and-sex adjusted risk of A1HPV6 to predict patients with WHO-stage > 4 (W > 4) severity. We assessed the kinetics of the A1HPV6 components in a nonhuman primate model (NHP), from baseline to 7 days (D7) after SARS-CoV-2 infection.

Results

The area under the receiver operating characteristic curve for A1HPV6 was 0.99 (95% CI 0.97–0.99) in the validation subset, which was not significantly different from that in the construction subset, 0.99 (0.99–0.99; P = .80), like for sensitivity 92% (85–96) vs 94% (88–97; P = .29). A1HPV6 was associated with W > 4, with a significant odds ratio of 1.3 (1.1–1.5; 0.002). In NHP, A1 levels decreased (P < .01) at D2 and normalized at D4; HP levels increased at D2 and peaked at D4. In patients, A1 concentration was very low at D2 vs controls (P < .01) and increased at D14 (P < .01) but was still lower than controls; HP increased at D2 and remained elevated at D14.

Conclusion

These results validate the diagnostic and prognostic performances of A1HPV6. Similar kinetics of apolipoprotein A1, HP, and alpha-2-macroglobulin were observed in the NHP model. ClinicalTrials.gov number, NCT01927133.

Impairment of T cells' antiviral and anti-inflammation immunities may be critical to death from COVID-19

Clarifying dominant factors determining the immune heterogeneity from non-survivors to survivors is crucial for developing therapeutics and vaccines against COVID-19. The main difficulty is quantitatively analysing the multi-level clinical data, including viral dynamics, immune response and tissue damages. Here, we adopt a top-down modelling approach to quantify key functional aspects and their dynamical interplay in the battle between the virus and the immune system, yielding an accurate description of real-time clinical data involving hundreds of patients for the first time. The quantification of antiviral responses gives that, compared to antibodies, T cells play a more dominant role in virus clearance, especially for mild patients (96.5%). Moreover, the anti-inflammatory responses, namely the cytokine inhibition and tissue repair rates, also positively correlate with T cell number and are significantly suppressed in non-survivors. Simulations show that the lack of T cells can lead to more significant inflammation, proposing an explanation for the monotonic increase of COVID-19 mortality with age and higher mortality for males. We propose that T cells play a crucial role in the immunity against COVID-19, which provides a new direction-improvement of T cell number for advancing current prevention and treatment.

From Genetics to Epigenetics: Top 4 Aspects for Improved SARS-CoV-2 Vaccine Designs as Paradigmatic Examples

This literature review described the genetic and biochemical factors that may have been overlooked in the formulation of vaccines and that most likely underlie possible issues with mass vaccination.

Estimation of viral kinetics model parameters in young and aged SARS-CoV-2 infected macaques

The SARS-CoV-2 virus disproportionately causes serious illness and death in older individuals. In order to have the greatest impact in decreasing the human toll caused by the virus, antiviral treatment should be targeted to older patients. For this, we need a better understanding of the differences in viral dynamics between SARS-CoV-2 infection in younger and older adults. In this study, we use previously published averaged viral titre measurements from the nose and throat of SARS-CoV-2 infection in young and aged cynomolgus macaques to parametrize a viral kinetics model. We find that all viral kinetics parameters differ between young and aged macaques in the nasal passages, but that there are fewer differences in parameter estimates from the throat. We further use our parametrized model to study the antiviral treatment of young and aged animals, finding that early antiviral treatment is more likely to lead to a lengthening of the infection in aged animals, but not in young animals.

COVA1-18 neutralizing antibody protects against SARS-CoV-2 in three preclinical models

Effective treatments against Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) are urgently needed. Monoclonal antibodies have shown promising results in patients. Here, we evaluate the in vivo prophylactic and therapeutic effect of COVA1-18, a neutralizing antibody highly potent against the B.1.1.7 isolate. In both prophylactic and therapeutic settings, SARS-CoV-2 remains undetectable in the lungs of treated hACE2 mice. Therapeutic treatment also causes a reduction in viral loads in the lungs of Syrian hamsters. When administered at 10 mg kg-1 one day prior to a high dose SARS-CoV-2 challenge in cynomolgus macaques, COVA1-18 shows very strong antiviral activity in the upper respiratory compartments. Using a mathematical model, we estimate that COVA1-18 reduces viral infectivity by more than 95% in these compartments, preventing lymphopenia and extensive lung lesions. Our findings demonstrate that COVA1-18 has a strong antiviral activity in three preclinical models and could be a valuable candidate for further clinical evaluation.

Quantifying the relationship between SARS-CoV-2 viral load and infectiousness

The relationship between SARS-CoV-2 viral load and infectiousness is poorly known. Using data from a cohort of cases and high-risk contacts, we reconstructed viral load at the time of contact and inferred the probability of infection. The effect of viral load was larger in household contacts than in non-household contacts, with a transmission probability as large as 48% when the viral load was greater than 1010 copies per mL. The transmission probability peaked at symptom onset, with a mean probability of transmission of 29%, with large individual variations. The model also projects the effects of variants on disease transmission. Based on the current knowledge that viral load is increased by two- to eightfold with variants of concern and assuming no changes in the pattern of contacts across variants, the model predicts that larger viral load levels could lead to a relative increase in the probability of transmission of 24% to 58% in household contacts, and of 15% to 39% in non-household contacts.

Prevention of host-to-host transmission by SARS-CoV-2 vaccines

As the number of individuals vaccinated against SARS-CoV-2 rises worldwide, population-level data regarding the vaccines' ability to reduce infection are being generated. Randomised trials have shown that these vaccines dramatically reduce symptomatic COVID-19; however, less is known about their effects on transmission between individuals. The natural course of infection with SARS-CoV-2 involves infection of the respiratory epithelia and replication within the mucosa to sufficient viral titres for transmission via aerosol particles and droplets. Here we discuss the available data on the existing, approved SARS-CoV-2 vaccines' capacity to reduce transmissibility by reducing primary infection, viral replication, capacity for transmission, and symptomaticity. The potential for mucosal-targeted SARS-CoV-2 vaccine strategies to more effectively limit transmission than intramuscular vaccines is considered with regard to known immunological mechanisms. Finally, we enumerate the population-level effects of approved vaccines on transmission through observational studies following clinical trials and vaccine distribution in real-world settings.

Modeling Within-Host Dynamics of SARS-CoV-2 Infection: A Case Study in Ferrets

The pre-clinical development of antiviral agents involves experimental trials in animals and ferrets as an animal model for the study of SARS-CoV-2. Here, we used mathematical models and experimental data to characterize the within-host infection dynamics of SARS-CoV-2 in ferrets. We also performed a global sensitivity analysis of model parameters impacting the characteristics of the viral infection. We provide estimates of the viral dynamic parameters in ferrets, such as the infection rate, the virus production rate, the infectious virus proportion, the infected cell death rate, the virus clearance rate, as well as other related characteristics, including the basic reproduction number, pre-peak infectious viral growth rate, post-peak infectious viral decay rate, pre-peak infectious viral doubling time, post-peak infectious virus half-life, and the target cell loss in the respiratory tract. These parameters and indices are not significantly different between animals infected with viral strains isolated from the environment and isolated from human hosts, indicating a potential for transmission from fomites. While the infection period in ferrets is relatively short, the similarity observed between our results and previous results in humans supports that ferrets can be an appropriate animal model for SARS-CoV-2 dynamics-related studies, and our estimates provide helpful information for such studies.