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Prediction of the Acute Inflammatory Response From A Mathematical Model FREE TO VIEW

Claudio E. Lagoa, DVM, MS; Gilles Clermont, MD; Rukmini Kumar, BS; John Bartels, PhD; Carson C. Chow, PhD; Yoram Vodovotz, PhD
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University of Pittsburgh Medical Center, Pittsburgh, PA


Chest


Chest. 2003;124(4_MeetingAbstracts):121S. doi:10.1378/chest.124.4_MeetingAbstracts.121S-a
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Abstract

PURPOSE:  Endotoxemia, trauma, hemorrhage, and infection all elicit an acute inflammatory response mediated by cells, cytokines, and other mediators that interact in a complex way. Attempts to modulate specific components of this cascade have largely failed as therapies, possibly because the of the complexity and redundancy of these interactions. We hypothesized that a mathematical simulation could accurately describe the time course of cell populations, cytokines, and key physiologic parameters of the acute inflammatory response.

METHODS:  We constructed a mathematical simulation of acute inflammation containing several of the key cellular and molecular components of this response. We calibrated this simulation across four experimental scenarios of acute inflammation in C57Bl/6 mice (3, 6 mg/kg LPS, trauma, and reversible hemorrhagic shock) by minimizing the error between data generated in vivo and predicted values of four analytes across all scenarios using a customized genetic algorithm. We thus produced a single simulation that could describe time course of analytes for all experimental scenarios. We validated this calibrated simulation in mice receiving 12mg/kg of endotoxin intraperitoneally by comparing experimental values of serum TNF, IL-6, IL-10, and NO2-/NO3- to theoretical predictions.

RESULTS:  A sample of results is depicted for TNF (figure). The mathematical simulation predicts (solid line) the time course of measured analytes (mean±SEM) accurately over the 24 hours of the experiments and provides specific time courses of analytes not measured.CONCLUSIONS: The magnitude and dynamics of the inflammatory response to endotoxin in vivo was accurately predicted by mathematical simulation.

CLINICAL IMPLICATIONS:  In silico modeling of complex systems provides a powerful method of integrating the concerted response of multiple interacting components of the acute inflammatory response. Such simulations could yield useful individual predictions and help individualize therapeutic interventions.

DISCLOSURE:  C.E. Lagoa, None.

Tuesday, October 28, 2003

2:30 PM - 4:00 PM


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