A poorly controlled acute inflammatory response can lead to organ dysfunction and death. Severe systemic inflammation can be induced and perpetuated by diverse insults such as the administration of toxic bacterial products (e.g., endotoxin), traumatic injury, and hemorrhage. Here, we probe whether these varied shock states can be explained by a universal inflammatory system that is initiated through different means and, once initiated, follows a course specified by the cellular and molecular mechanisms of the immune and endocrine systems. To examine this question, we developed a mathematical model incorporating major elements of the acute inflammatory response in C57Bl/6 mice, using input from experimental data. We found that a single model with different initiators including the autonomic system could describe the response to various insults. This model was able to predict a dose range of endotoxin at which mice would die despite having been calibrated only in nonlethal inflammatory paradigms. These results show that the complex biology of inflammation can be modeled and supports the hypothesis that shock states induced by a range of physiologic challenges could arise from a universal response that is differently initiated and modulated.
When the body is infected, it mounts an acute inflammatory response to rid itself of the pathogens and restore health. Uncontrolled acute inflammation due to infection is defined clinically as sepsis and can culminate in organ failure and death.We consider a three dimensional ordinary differential equation model of inflammation consisting of a pathogen, and two inflammatory mediators. The model reproduces the healthy outcome and diverse negative outcomes, depending on initial conditions and parameters. We analyze the various bifurcations between the different outcomes when key parameters are changed and suggest various therapeutic strategies. We suggest that the clinical condition of sepsis can arise from several distinct physiological states, each of which requires a different treatment approach.
A mathematical simulation of anti-TNF therapy identified clear windows of opportunity for this intervention as well as populations that can be harmed by anti-TNF therapy. The construction of an in silico clinical trial could provide profound insight into the design of clinical trials of immunomodulatory therapies, ranging from optimal patient selection to individualized dosage and duration of proposed therapeutic interventions.
Drug-induced liver injury (DILI) is not only a major concern for all patients requiring drug therapy, but also for the pharmaceutical industry. Many new in vitro assays and pre-clinical animal models are being developed to help screen compounds for the potential to cause DILI. This study demonstrates that mechanistic, mathematical modeling offers a method for interpreting and extrapolating results. The DILIsym™ model (version 1A), a mathematical representation of DILI, was combined with in vitro data for the model hepatotoxicant methapyrilene (MP) to carry out an in vitro to in vivo extrapolation. In addition, simulations comparing DILI responses across species illustrated how modeling can aid in selecting the most appropriate pre-clinical species for safety testing results relevant to humans. The parameter inputs used to predict DILI for MP were restricted to in vitro inputs solely related to ADME (absorption, distribution, metabolism, elimination) processes. MP toxicity was correctly predicted to occur in rats, but was not apparent in the simulations for humans and mice (consistent with literature). When the hepatotoxicity of MP and acetaminophen (APAP) was compared across rats, mice, and humans at an equivalent dose, the species most susceptible to APAP was not susceptible to MP, and vice versa. Furthermore, consideration of variability in simulated population samples (SimPops™) provided confidence in the predictions and allowed examination of the biological parameters most predictive of outcome. Differences in model sensitivity to the parameters were related to species differences, but the severity of DILI for each drug/species combination was also an important factor.
Bacillus anthracis (anthrax) can trigger an acute inflammatory response that results in multisystem organ failure and death. Previously, we developed a mathematical model of acute inflammation after gram-negative infection that had been matched qualitatively to literature data. We modified the properties of the invading bacteria in that model to those specific to B. anthracis and simulated the host response to anthrax infection. We simulated treatment strategies against anthrax in a genetically diverse population including the following: (1) antibiotic treatment initiated at various time points, (2) antiprotective antigen vaccine, and (3) a combination of antibiotics and vaccine. In agreement with studies in mice, our simulations showed that antibiotics only improve survival if administered early in the course of anthrax infection. Vaccination that leads to the formation of antibodies to protective antigen is anti-inflammatory and beneficial in averting shock and improving survival. However, antibodies to protective antigen alone are predicted not to be universally protective against anthrax infection. Rather, our simulations suggest that an optimal strategy would require both vaccination and antibiotic administration.
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