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Mechanistic Models in Viral Infection: Comparison
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Please note this is a comparison between Version 2 by Camila Xu and Version 1 by Bárbara Costa.

Mechanistic models are useful to untangle the complex system of virus, cytokines, and immune cells by considering several factors, such as viral entry, replication in target cells, viral spread in the body, immune response, and other complex factors involved in tissue/organ damage and recovery.

 

  • mechanistic models
  • viral infection
  • immune system

1. Introduction

Humans can contract a variety of viruses that have serious negative effects on their health and economy. Acute infections are caused by viruses, such as the rhinovirus, and influenza A or B viruses [1], whereas chronic infections are caused by others, such as the human immunodeficiency virus (HIV), Epstein-Barr virus (EBV), and hepatitis C virus (HCV) [2]. Various disease etiologists and levels of severity of viral infections exist, ranging from asymptomatic to fatal. Additionally, several viruses, including those that cause cancer, autoimmune diseases, and Alzheimer’s disease, such as EBV, human papillomavirus (HPV), or herpes simplex virus (HSV), may put a host at risk for coinfection with other pathogens [3].
In comparison with other pathogens, it is difficult to control viral-associated disorders because there is no one, all-encompassing method to control viruses due to the tremendous diversity in viruses’ epidemiology and pathogenicity. Even when prophylactic or therapeutic alternatives are available, inducing protective immunity may not always be successful and there may be decreased, time-dependent efficacy in single- or multi-pathogen infections. A lack of understanding of how host defense mechanisms restrict viral spread, how different viral components counteract these defense mechanisms, and how these relate to illness outcomes has impeded the development of new preventative and therapeutic measures. Numerous quantitative data have been generated recently because of improvements in multiparameter flow cytometry, high-throughput technologies and the SARS-CoV-2 pandemic, which pushed forward a lot of knowledge. These advancements have also brought attention to the need for new theoretical models that can explain complex biological interactions. Mathematical models have been developed to assess the several aspects of viral infection, since infection kinetics, to virus replication, mechanisms of viral persistence and control by host immune responses, as well as evaluate the clinical potential of various antiviral therapies [4,5][4][5]. These models have been curated and applied to in silico experiments to develop the creation of new hypotheses. Additionally, clinical dose-efficacy response and pharmacokinetic/pharmacodynamic (PK/PD) studies can be assisted using modelling and simulation platforms, which provide simulations for both viral dynamics and treatment efficacy [6].

2. Mechanistic Models

Mechanistic models are useful to untangle the complex system of virus, cytokines, and immune cells by considering several factors, such as viral entry, replication in target cells, viral spread in the body, immune response, and other complex factors involved in tissue/organ damage and recovery. Temporal interactions coexist with time-dependent risk factors and patients multimorbidity, which must also be accounted for as infection progresses. Since all this needs to be modelled, it is necessary to separate each model and then integrate everything into a multiscale model [13][7].
Clinical studies of viral load performed on individual patients’ blood samples, immune cell or cytokine profile all provide snapshots of the viral infection [14][8], and weit can be recognized typical patterns of infection progression when weresearchers combine these data. The depth and frequency of these measures, however, are frequently insufficient to accurately predict everyone’s immune response and thus maximize the effectiveness of individualized treatments. WeResearchers still have a limited knowledge of the mechanisms underlying why some people react to a virus with minor symptoms while others experience severe sickness, or why some people recover fully while others experience long-term effects like post-polio syndrome or long-COVID [15][9]. This is because wresearchers are unable to foresee how an individual’s immune response and immunological-viral interactions would develop. As a result, weresearchers cannot currently reliably forecast how a given patient will react to therapy with immune modulators or antiviral medications, whether for endemic circulating viruses like seasonal flu or novel viruses brought on by pandemics. However, it is nearly impossible to anticipate immune system behavior using qualitative models because parameters such as: cytokine levels and immune cell profiles can change in complex ways over time and geographically [16][10].
The complexity of viral infection and immune response has led to the development of mechanistic computational models, which differ in the mathematical and computational representation of components, interactions, levels of spatial detail, and the time scales they consider [17][11]. The model of infection is based on scientifically driven theories that identify the essential physical elements of immune response and viral infection and how these elements interact to produce infection (considering essential measurable and quantifiable variables to best capture these elements and interactions). Moreover, it is important to know how to translate the dynamic mechanistic model into a computer simulation to construct a mechanistic computational model [4,18][4][12]. For instance, we must decide whether to characterize individual virions or viral concentrations, or whether changes occur continuously over time or as stochastic events. Finally, we must consider that there are time limits on the windows for successful therapies, and some treatments might only be effective when used as prophylactic measures or in the early stages of infection [19][13].

Interplay of Viral Infection Dynamics and the Immune System on Modelling

The development of mathematical immunology modelling has been influenced by the complexity of understanding the immune response to HIV infection. The target-cell-limited model, initially, proposed to understand the dynamics of HIV infection provided a foundation for within-host modelling [20][14]. This framework was afterwards expanded in numerous ways and applied to other viral illnesses. Three factors make up the straightforward target-cell-limited model: the viral load, infected virus-producing cells, and cells that are sensitive yet not infected. The lifespans of infected target cells and virus particles could be estimated by fitting this model to the viral-load data [21][15]. It also permitted assessment of the pace at which infected cells produce virions. These early models laid the foundation for the subsequent creation of far better HIV medicines.
For example, the innate immune system’s first line of defense is the interferon (IFN) family of proteins, which block viral replication inside infected cells and stop host cells from becoming infected. The importance of innate immune response feedback loops in influencing how diseases develop in people has been discussed in recent models [22][16]. Moreover, the simple target-cell-limited model could not explain observed primary HIV dynamics in an infected host after the initial acute viral-load peak, raising the possibility that cytotoxic T lymphocytes (CTLs) and cytokine-suppressive viral replication may be involved in regulating viral load [20][14].
Insights with significant therapeutic utility have been revealed by models that incorporate the adaptive immune response. For instance, the “post-treatment control” of HIV viral load observed in some HIV patients was explained by an effector-cell response and exhaustion model [23][17], and it was suggested that therapeutic vaccination to boost effector-cell response before stopping antiretroviral therapy might increase the likelihood of post-treatment viral load control. Models were developed to optimize the duration of the therapy and minimize cost and toxicity of direct-acting antiviral (DAA) medication. Viral-kinetic models can forecast the length of DAA therapy required to reach a cure in patients infected with HCV and thus tailor the treatment [23][17]. These models can be applied to early viral-kinetic data under drug treatment. Besides the model could suggest the one patient who relapsed would have benefited from taking sofosbuvir + ledipasvir for an additional week [24][18]. By assuming that HCV RNA in serum contains both infectious and non-infectious viruses, Goyal et al. expanded these models [25][19], which helps to explain why some people can be cured by ultrashort DAA therapy.
According to Baral et al., the viral reduction brought on by DAAs during chronic HCV treatment may have prevented CTL depletion, allowing the virus to be eliminated after treatment. The model was able to estimate the required length of DAA therapy for each patient and, as a result, tailor the treatment by defining a good response for specific patients [26][20].
The length of infection Influences both the rate at which viral particles are produced and the mortality rate of infected cells and age-structured models with detailed submodels of the viral life cycle can be used for the systematic exploration of new drug targets [14,27][8][21]. All viruses replicate in the same way: they establish contact with a target cell in the host, release genetic material into the cell, use the machinery of the cell to replicate, assemble new viral particles, and release those particles from the infected cell [28][22]. Which of these steps should be blocked for the quickest and most efficient therapies can be determined by mechanistic models? Models, for instance, can investigate how the number of infections impacts the viral replication rate. Model simulations can also predict the impact of drug-based perturbations when viral replication pathways involve both positive and negative feedback [29][23].
We may anticipate that the best course of action in situations when we have a highly powerful antiviral would be to seek treatment as soon as possible following either infection or diagnosis. Delaying antiviral medication increases the danger of immunological reactions and virus-induced tissue damage. Early antiviral therapy, however, may prevent the adaptive immune response from being activated and, as a result, the development of long-term protective immunity [30][24]. This adjustment was studied in a model by Stromberg et al., who suggested a short window following infection during which antiviral therapy could lessen disease symptoms without obstructing the development of long-term immunity, ensuring that those infected receive the benefits of vaccination with a lower risk of the disease [31][25].
The immune system’s cells react to a wide range of signals, many of which arrive at the same time. WResearchers can investigate the biochemical mechanisms underlying such reactions with the aid of computational models using signaling pathways and cellular behavior, particularly when those models strive to include molecular specifics of intracellular reaction networks [15,32][9][26]. Such detailed models may focus on the interactions between molecule binding domains and how these interactions are influenced by elements such as post-translational modifications or steric restraints in multi-molecular complexes. The mechanistic immunological hypotheses can then be formalized and tested through quantitative simulations [33][27]. However, weit is needed to be aware that comprehension of multi-signal cellular responses and their interaction at the tissue level is the most difficult challenge we face in our quest to understand immune cell function because it can be too reductionist [34][28]. Learning how to employ drugs to understand, strengthen and replicate immune system function may be preferable to trying to simulate all the intricacies mentioned above.
Table 21 shows that there are a number of mathematical models that have been developed to understand the dynamics of HIV, Influenz and SARS-CoV-2 infection and the effects of antiretroviral therapy (ART) on the different diseases. These models typically involve the use of differential equations to describe the interations between the virus and the immune system, as well as the effects of ART on the virus. These types of models can be used to simulate the course of infection and the effects of different ART regimens on the virus, and can provide insights into how to optimize treatment strategies. However, it’s important to note that these models are simplified representations of complex biological systems, and their predictions may not always be accurate in real-world situations.
Table 21. Types of the most severe and prevalent diseases caused by virus infections that can be cured by modulating the immune response.
Disease How It Can Be Modulated?
Acquired immunodeficiency syndrome (AIDS), which is caused HIV Example 1: HIV-1 immune dynamics model, which was developed by Nowak and May in 1996 [35][29]. This model describes the interactions between HIV, CD4+ T cells, and antiviral drugs. The model includes equations that describe the rate of HIV replication, the rate of CD4+ T cell production and loss, and the rate at which antiviral drugs inhibit HIV replication.

Example 2: HIV-1 treatment intensification model, which was developed by Guedj et al. in 2007 [36][30]. This model is based on the HIV-1 immune dynamics model and incorporates additional equations to describe the effects of different ART regimens on the virus.
Influenza caused by the influenza virus Example 1: influenza transmission model, which was developed by Ferguson et al. in 2005 [37][31]. This model describes the transmission of influenza from person to person, as well as the effect of antiviral drugs on the virus. The model includes equations that describe the rate of influenza transmission, the rate of antiviral drug-induced reduction in the number of infectious individuals, and the rate at which individuals become immune to the virus.

Example 2: influenza vaccine effectiveness model, which was developed by Thompson et al. in 2006 [38][32]. This model is based on the influenza transmission model and incorporates additional equations to describe the effects of vaccination on the virus and the immune system. The model is based on the influenza transmission model, which was developed by Ferguson et al. in 2005, and incorporates additional equations to describe the effects of vaccination on the virus and the immune system. It’s important to note that the model is based on a specific set of assumptions and may not be applicable to all situations
Severe acute respiratory syndrome (SARS), which is caused by the SARS-CoV-2 virus Example 1: SEIR (susceptible-exposed-infectious-recovered) model, which is a type of compartmental model that describes the transmission of infectious diseases. The SEIR model includes equations that describe the rate of transmission of SARS-CoV-2, the rate of recovery, and the rate at which individuals become immune to the virus [39][33].

Example 2: SIR (susceptible-infectious-recovered) model, which is a variant of the SEIR model that does not include an exposed compartment. The SIR model has been used to study the effectiveness of various interventions, such as antiviral drugs and vaccines, on the transmission of SARS-CoV-2 [40][34].
Ideally, if patients were treated based on the knowledge obtained from their immune status, drug response, and predictive models, therapy failure would be minimized, and severe diseases could be better controlled. This would also improve our ability to forecast how a given patient would react to the therapy. It is indeed true that virus infections can be complex and can affect different tissues or organs, and that the choice of treatment can depend on a number of factors, including the specific virus, the patient’s immune status, and the severity of the infection.
One approach to developing a patient-specific antiviral therapy strategy is to use a mathematical model that takes into account a panel of patient-specific parameters that may be related to the virus or the host immune response. This type of model could be used to forecast drug efficacy or to guide the selection of drugs for a particular patient.
There are a number of different approaches that could be used to build such a model, depending on the specific goals of the treatment and the data available. For example, the model could be based on data from clinical trials or observational studies, or it could incorporate data on the patient’s specific genetic and immunologic characteristics.
It’s important to note that developing a patient-specific antiviral therapy strategy based on a mathematical model is still a complex and challenging task, and more research is needed to fully understand the factors that influence the effectiveness of different antiviral therapies. However, by using quantitative approaches like this, it may be possible to optimize treatment choices and improve outcomes for patients with virus infections.

 

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