Cross-Species Translation of Biophase Half-Life and Potency of GalNAc-Conjugated siRNAs.

Small interfering RNAs (siRNAs) with N-acetylgalactosamine (GalNAc) conjugation for improved liver uptake represent an emerging class of drugs to treat liver diseases. Understanding how pharmacokinetics and pharmacodynamics translate is pivotal for in vivo study design and human dose prediction. However, the literature is sparse on translational data for this modality, and pharmacokinetics in the liver is seldom measured. To overcome these difficulties, we collected time-course biomarker data for 11 GalNAc-siRNAs in various species and applied the kinetic-pharmacodynamic modeling approach to estimate the biophase (liver) half-life and the potency. Our analysis indicates that the biophase half-life is 0.6-3 weeks in mouse, 1-8 weeks in monkey, and 1.5-14 weeks in human. For individual siRNAs, the biophase half-life is 1-8 times longer in human than in mouse, and generally 1-3 times longer in human than in monkey. The analysis indicates that the siRNAs are more potent in human than in mouse and monkey.

PK/PD-based adaptive tailoring of oseltamivir doses to treat within-host influenza viral infections.

Influenza A virus (IAV) is a latent global threat to human health. In view of the risk of pandemics, prophylactic and curative treatments are essential. Oseltamivir is a neuraminidase inhibitor efficiently supporting recovery from influenza infections. Current common clinical practice is a constant drug dose (75 or 150 mg) administered at regular time intervals twice a day. We aim to use quantitative systems pharmacology to propose an efficient adaptive drug scheduling. We combined the mathematical model for IAV infections validated by murine data, which captures the viral dynamics and the dynamics of the immune host response, with a pharmacokinetic (PK)/pharmacodynamic (PD) model of oseltamivir. Next, we applied an adaptive impulsive feedback control method to systematically calculate the adaptive dose of oseltamivir in dependence on the viral load and the number of immune effectors at the time of drug administration. Our in silico results revealed that the treatment with adaptive control-based drug scheduling is able to either increase the drug virological efficacy or reduce the drug dose while keeping the same virological efficacy. Thus, adaptive adjustment of the drug dose would reduce not only the potential side effects but also the amount of stored oseltamivir required for the prevention of outbreaks.

Hierarchical effects of pro-inflammatory cytokines on the post-influenza susceptibility to pneumococcal coinfection.

In the course of influenza A virus (IAV) infections, a secondary bacterial infection frequently leads to serious respiratory conditions provoking high hospitalization and death tolls. Although abundant pro-inflammatory responses have been reported as key contributing factors for these severe dual infections, the relative contributions of cytokines remain largely unclear. In the current study, mathematical modelling based on murine experimental data dissects IFN-γ as a cytokine candidate responsible for impaired bacterial clearance, thereby promoting bacterial growth and systemic dissemination during acute IAV infection. We also found a time-dependent detrimental role of IL-6 in curtailing bacterial outgrowth which was not as distinct as for IFN-γ. Our numerical simulations suggested a detrimental effect of IFN-γ alone and in synergism with IL-6 but no conclusive pathogenic effect of IL-6 and TNF-α alone. This work provides a rationale to understand the potential impact of how to manipulate temporal immune components, facilitating the formulation of hypotheses about potential therapeutic strategies to treat coinfections.

Oseltamivir PK/PD Modeling and Simulation to Evaluate Treatment Strategies against Influenza-Pneumococcus Coinfection.

Influenza pandemics and seasonal outbreaks have shown the potential of Influenza A virus (IAV) to enhance susceptibility to a secondary infection with the bacterial pathogen Streptococcus pneumoniae (Sp). The high morbidity and mortality rate revealed the poor efficacy of antiviral drugs and vaccines to fight IAV infections. Currently, the most effective treatment for IAV is by antiviral neuraminidase inhibitors. Among them, the most frequently stockpiled is Oseltamivir which reduces viral release and transmission. However, effectiveness of Oseltamivir is compromised by the emergence of resistant IAV strains and secondary bacterial infections. To date, little attention has been given to evaluate how Oseltamivir treatment strategies alter Influenza viral infection in presence of Sp coinfection and a resistant IAV strain emergence. In this paper we investigate the efficacy of current approved Oseltamivir treatment regimens using a computational approach. Our numerical results suggest that the curative regimen (75 mg) may yield 47% of antiviral efficacy and 9% of antibacterial efficacy. An increment in dose to 150 mg (pandemic regimen) may increase the antiviral efficacy to 49% and the antibacterial efficacy to 16%. The choice to decrease the intake frequency to once per day is not recommended due to a significant reduction in both antiviral and antibacterial efficacy. We also observe that the treatment duration of 10 days may not provide a clear improvement on the antiviral and antibacterial efficacy compared to 5 days. All together, our in silico study reveals the success and pitfalls of Oseltamivir treatment strategies within IAV-Sp coinfection and calls for testing the validity in clinical trials.

Modeling Influenza Virus Infection: A Roadmap for Influenza Research.

Influenza A virus (IAV) infection represents a global threat causing seasonal outbreaks and pandemics. Additionally, secondary bacterial infections, caused mainly by Streptococcus pneumoniae, are one of the main complications and responsible for the enhanced morbidity and mortality associated with IAV infections. In spite of the significant advances in our knowledge of IAV infections, holistic comprehension of the interplay between IAV and the host immune response (IR) remains largely fragmented. During the last decade, mathematical modeling has been instrumental to explain and quantify IAV dynamics. In this paper, we review not only the state of the art of mathematical models of IAV infection but also the methodologies exploited for parameter estimation. We focus on the adaptive IR control of IAV infection and the possible mechanisms that could promote a secondary bacterial coinfection. To exemplify IAV dynamics and identifiability issues, a mathematical model to explain the interactions between adaptive IR and IAV infection is considered. Furthermore, in this paper we propose a roadmap for future influenza research. The development of a mathematical modeling framework with a secondary bacterial coinfection, immunosenescence, host genetic factors and responsiveness to vaccination will be pivotal to advance IAV infection understanding and treatment optimization.

A Stochastic Model for CD4+ T Cell Proliferation and Dissemination Network in Primary Immune Response.

The study of the initial phase of the adaptive immune response after first antigen encounter provides essential information on the magnitude and quality of the immune response. This phase is characterized by proliferation and dissemination of T cells in the lymphoid organs. Modeling and identifying the key features of this phenomenon may provide a useful tool for the analysis and prediction of the effects of immunization. This knowledge can be effectively exploited in vaccinology, where it is of interest to evaluate and compare the responses to different vaccine formulations. The objective of this paper is to construct a stochastic model based on branching process theory, for the dissemination network of antigen-specific CD4+ T cells. The devised model is validated on in vivo animal experimental data. The model presented has been applied to the vaccine immunization context making references to simple proliferation laws that take into account division, death and quiescence, but it can also be applied to any context where it is of interest to study the dynamic evolution of a population.

Ebola virus infection modeling and identifiability problems.

The recent outbreaks of Ebola virus (EBOV) infections have underlined the impact of the virus as a major threat for human health. Due to the high biosafety classification of EBOV (level 4), basic research is very limited. Therefore, the development of new avenues of thinking to advance quantitative comprehension of the virus and its interaction with the host cells is urgently needed to tackle this lethal disease. Mathematical modeling of the EBOV dynamics can be instrumental to interpret Ebola infection kinetics on quantitative grounds. To the best of our knowledge, a mathematical modeling approach to unravel the interaction between EBOV and the host cells is still missing. In this paper, a mathematical model based on differential equations is used to represent the basic interactions between EBOV and wild-type Vero cells in vitro. Parameter sets that represent infectivity of pathogens are estimated for EBOV infection and compared with influenza virus infection kinetics. The average infecting time of wild-type Vero cells by EBOV is slower than in influenza infection. Simulation results suggest that the slow infecting time of EBOV could be compensated by its efficient replication. This study reveals several identifiability problems and what kind of experiments are necessary to advance the quantification of EBOV infection. A first mathematical approach of EBOV dynamics and the estimation of standard parameters in viral infections kinetics is the key contribution of this work, paving the way for future modeling works on EBOV infection.

Vaginal immunization to elicit primary T-cell activation and dissemination.

Primary T-cell activation at mucosal sites is of utmost importance for the development of vaccination strategies. T-cell priming after vaginal immunization, with ovalbumin and CpG oligodeoxynucleotide adjuvant as model vaccine formulation, was studied in vivo in hormone-synchronized mice and compared to the one induced by the nasal route. Twenty-four hours after both vaginal or nasal immunization, antigen-loaded dendritic cells were detected within the respective draining lymph nodes. Vaginal immunization elicited a strong recruitment of antigen-specific CD4(+) T cells into draining lymph nodes that was more rapid than the one observed following nasal immunization. T-cell clonal expansion was first detected in iliac lymph nodes, draining the genital tract, and proliferated T cells disseminated towards distal lymph nodes and spleen similarly to what observed following nasal immunization. T cells were indeed activated by the antigen encounter and acquired homing molecules essential to disseminate towards distal lymphoid organs as confirmed by the modulation of CD45RB, CD69, CD44 and CD62L marker expression. A multi-type Galton Watson branching process, previously used for in vitro analysis of T-cell proliferation, was applied to model in vivo CFSE proliferation data in draining lymph nodes 57 hours following immunization, in order to calculate the probabilistic decision of a cell to enter in division, rest in quiescence or migrate/die. The modelling analysis indicated that the probability of a cell to proliferate was higher following vaginal than nasal immunization. All together these data show that vaginal immunization, despite the absence of an organized mucosal associated inductive site in the genital tract, is very efficient in priming antigen-specific CD4(+) T cells and inducing their dissemination from draining lymph nodes towards distal lymphoid organs.

A non-linear deterministic model for regulation of diauxic lag on cellobiose by the pneumococcal multidomain transcriptional regulator CelR.

When grown on glucose and beta-glucosides, S. pneumoniae shows sequential use of sugars resulting in diauxic growth with variable time extent of the lag phase separating the biphasic growth curve. The pneumococcal beta-glucoside uptake locus containing the PTS transporter spr0276-82, is regulated by a multi-domain transcriptional regulator CelR. In this work, we address the contribution of phosphorylation of the phosphorylable cysteine in the EIIB domain of CelR to diauxic lag. Utilising site-directed mutagenesis of the phosphorylable amino acids in the EIIB and EIIA domains of CelR, we show that the EIIB domain activation is linked to the duration of the lag phase. Analysis of mutants for other PTS systems indicates that a second beta-glucoside PTS (spr0505), not able to support growth on cellobiose, is responsible for the lag during diauxic growth. A mathematical model of the process is devised together with a nonlinear identification procedure which provides model parameter estimates characterizing the single phases of bacterial growth. Parameter identification performed on data recorded in appropriate experiments on mutants allows for establishing a relationship between a specific model parameter, the EIIB domain and the time extent of the diauxic lag. The experimental results and the related insights provided by the mathematical model provide evidence that the conflicting activation of the CelR regulator is at the origin of the lag phase during sequential growth on glucose and cellobiose. This data is the first description of diauxic lag regulation involving two PTS and a multidomain regulator and could serve as a promising approach for studying the S. pneumoniae growth process on complex carbon sources as possibly encountered in the human host.