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An overt pro-inflammatory immune response is a key factor contributing to lethal pneumococcal infection in an influenza pre-infected host and represents a potential target for therapeutic intervention. However, there is a paucity of knowledge about the level of contribution of individual cytokines. Based on the predictions of our previous mathematical modeling approach, the potential benefit of IFN-γ- and/or IL-6-specific antibody-mediated cytokine neutralization was explored in C57BL/6 mice infected with the influenza A/PR/8/34 strain, which were subsequently infected with the Streptococcus pneumoniae strain TIGR4 on day 7 post influenza. While single IL-6 neutralization had no effect on respiratory bacterial clearance, single IFN-γ neutralization enhanced local bacterial clearance in the lungs. Concomitant neutralization of IFN-γ and IL-6 significantly reduced the degree of pneumonia as well as bacteremia compared to the control group, indicating a positive effect for the host during secondary bacterial infection. The results of our model-driven experimental study reveal that the predicted therapeutic value of IFN-γ and IL-6 neutralization in secondary pneumococcal infection following influenza infection is tightly dependent on the experimental protocol while at the same time paving the way toward the development of effective immune therapies.
The thymus hosts the development of a specific type of adaptive immune cells called T cells. T cells orchestrate the adaptive immune response through recognition of antigen by the highly variable T-cell receptor (TCR). T-cell development is a tightly coordinated process comprising lineage commitment, somatic recombination of Tcr gene loci and selection for functional, but non-self-reactive TCRs, all interspersed with massive proliferation and cell death. Thus, the thymus produces a pool of T cells throughout life capable of responding to virtually any exogenous attack while preserving the body through self-tolerance. The thymus has been of considerable interest to both immunologists and theoretical biologists due to its multi-scale quantitative properties, bridging molecular binding, population dynamics and polyclonal repertoire specificity. Here, we review experimental strategies aimed at revealing quantitative and dynamic properties of T-cell development and how they have been implemented in mathematical modeling strategies that were reported to help understand the flexible dynamics of the highly dividing and dying thymic cell populations. Furthermore, we summarize the current challenges to estimating in vivo cellular dynamics and to reaching a next- generation multi-scale picture of T-cell development.
Neuraminidase inhibitors in influenza treatment and prevention – is it time to call it a day?
(2018)
Stockpiling neuraminidase inhibitors (NAIs) such as oseltamivir and zanamivir is part of a global effort to be prepared for an influenza pandemic. However, the contribution of NAIs for the treatment and prevention of influenza and its complications is largely debatable due to constraints in the ability to control for confounders and to explore unobserved areas of the drug effects. For this study, we used a mathematical model of influenza infection which allowed transparent analyses. The model recreated the oseltamivir effects and indicated that: (i) the efficacy was limited by design, (ii) a 99% efficacy could be achieved by using high drug doses (however, taking high doses of drug 48 h post-infection could only yield a maximum of 1.6-day reduction in the time to symptom alleviation), and (iii) contributions of oseltamivir to epidemic control could be high, but were observed only in fragile settings. In a typical influenza infection, NAIs’ efficacy is inherently not high, and even if their efficacy is improved, the effect can be negligible in practice.
The specific temporal evolution of bacterial and phage population sizes, in particular bacterial depletion and the emergence of a resistant bacterial population, can be seen as a kinetic fingerprint that depends on the manifold interactions of the specific phage–host pair during the course of infection. We have elaborated such a kinetic fingerprint for a human urinary tract Klebsiella pneumoniae isolate and its phage vB_KpnP_Lessing by a modeling approach based on data from in vitro co-culture. We found a faster depletion of the initially sensitive bacterial population than expected from simple mass action kinetics. A possible explanation for the rapid decline of the bacterial population is a synergistic interaction of phages which can be a favorable feature for phage therapies. In addition to this interaction characteristic, analysis of the kinetic fingerprint of this bacteria and phage combination revealed several relevant aspects of their population dynamics: A reduction of the bacterial concentration can be achieved only at high multiplicity of infection whereas bacterial extinction is hardly accomplished. Furthermore the binding affinity of the phage to bacteria is identified as one of the most crucial parameters for the reduction of the bacterial population size. Thus, kinetic fingerprinting can be used to infer phage–host interactions and to explore emergent dynamics which facilitates a rational design of phage therapies.
Influenza is a contagious respiratory disease caused by influenza A and influenza B viruses. The World Health Organisation (WHO) reports that annual influenza epidemics result in approximately 1 billion infections, 3 to 5 million severe cases, and 300 to 650 thousand deaths. Understanding hidden mechanisms that lead to optimal vaccine efficacy and improvement antiviral treatment strategies remain continuous and central tasks. First, regarding the immune response to vaccines and natural infections, the antibody response echoes the dynamics of diverse immune elements such as B-cells, and plasma cells. Also, responses reflect the processes for B-cells to gain and adapt affinity for the virus. Antibodies (Abs) that respond to the virus surface proteins, particularly to the hemagglutinin (HA), have been identified to protect against infection. The Abs responses binding to HA can be broadly protective as this protein is considerably accessible on the virion. When following sequential infections with similar influenza strains, i.e. two infections with different strains of a subtype, an enhanced breadth and magnitude of Abs response is developed, mainly after the second infection. The effect of being effective to new strains is called Abs cross-reaction.
On the other hand, as for antiviral treatment, the WHO currently approves the use of neuraminidase inhibitors (NIs) such as zanamivir and oseltamivir. Diverse research areas such as system biology, learning-based methods, control theory, and systems pharmacology have guided the development of modern treatment schemes. To do so, mathematical models are used to describe a wide range of phenomena such as viral pathogenesis, immune responses, and the drug's dynamics in the body. Drug dynamics are usually expressed in two phases, pharmacokinetics (PK) and pharmacodynamics (PD) - the PK/PD approach. These schemes leverage pre-clinical and clinical data through modeling and simulation of infection and drug effects at diverse levels. Under such a framework, control-based scheduling systems seek to tailor optimal antiviral treatment for infectious diseases. Thus, influenza treatment can be theoretically studied as a control-based optimization duty (about systems stability, bounded inputs, and optimality). Finally, towards real-world implementation, learning-based methods such as neural networks (NNs) can guide solving issues on the control-based performance. Using NNs as identifiers provide a setting to deal with infrequent measures and uncertain parameters for the control systems.
This thesis theoretically explores central mechanisms in influenza infection via modeling and control approaches. In the first project, we explore how and to what extent antibody-antigen affinity flexibility could guide the Abs cross-reaction in two sequential infections using a hypothetical family of antigens. The set of antigens generally represent strains of influenza, such as those of a subtype. Each antigen is composed of a variable and a conserved area, generically representing the structures of the HA, head, and stalk, respectively. We test diverse scenarios of affinity thresholds in the conserved and variable areas of the antigens. The Abs response reaches a high magnitude when using equivalent affinity thresholds in the conserved and variable areas during the first infection. However, improved cross-reaction is developed when slightly increasing the affinity threshold of the variable area for the second infection. Key mutations via affinity maturation is a feature that, together with affinity flexibility between infections, guides Abs cross-reaction in the model outcome. These results could correlate with studies pointing out that broad responses might be dependent on reaching specific mutations for getting affinity to a newly presented antigen while broadly reaching related antigens. The general platform may serve as a proof-of-concept for exploring fundamental mechanisms that favor the Abs cross-reaction.
In a second project, theoretical schemes are developed to combine impulsive and inverse optimal control strategies to address antiviral treatment scheduling. We present results regarding stability, passivity, bounded inputs, and optimality using impulsive action. The study is founded on mathematical models of the influenza virus (target-cell limited model) adjusted to data from clinical trials. In these studies, participants were experimentally infected with influenza H1N1 and treated with NIs. Results show that control-based strategies could tailor dosage and reduce the amount of medication by up to 44%. Also, control-based treatment reaches the efficacy (98%) of the current treatment recommendations by the WHO. Monte Carlo simulations (MCS) disclose the robustness of the proposed control-based techniques. Using MCS, we also explore the applicability to the individualized treatment of infectious diseases through virtual clinical trials. Furthermore, bounded control strategies are applied directly in drug dose estimation accounting for overdose prevention. Finally, due to the limitations of the available technology intended for clinical practice, we emphasize the necessity of developing system identifiers and observers for real-world applications.
In the third project, the problem of data scarcity and infrequent measures in the real world is handled by means of learning-based methods. System identification is derived using a Recurrent High Order Neural Network (RHONN) trained with the Extended Kalman filter (EKF). Lessons learned from impulsive control frameworks are taken to develop a neural inverse optimal impulsive control --neurocontrol. The treatment efficacy is tested for early (one day post-infection) and late (2 to 3 days post-infection) treatment initiation. The neurocontrol reaches an efficacy of up to 95% while saving almost 40% of the total drug in the early treatment. Robustness is tested via virtual clinical trials using MCS.
Lastly, taking all together, the schemes developed in this thesis for modeling the Abs cross-reaction and control-based treatment tailoring can be extended and adapted to explore similar phenomena in different respiratory pathogens, such as SARS-CoV-2.
The true revolution in the age of digital neuroanatomy is the ability to extensively quantify anatomical structures and thus investigate structure-function relationships in great detail. Large-scale projects were recently launched with the aim of providing infrastructure for brain simulations. These projects will increase the need for a precise understanding of brain structure, e.g., through statistical analysis and models.
From articles in this Research Topic, we identify three main themes that clearly illustrate how new quantitative approaches are helping advance our understanding of neural structure and function. First, new approaches to reconstruct neurons and circuits from empirical data are aiding neuroanatomical mapping. Second, methods are introduced to improve understanding of the underlying principles of organization. Third, by combining existing knowledge from lower levels of organization, models can be used to make testable predictions about a higher-level organization where knowledge is absent or poor. This latter approach is useful for examining statistical properties of specific network connectivity when current experimental methods have not yet been able to fully reconstruct whole circuits of more than a few hundred neurons.