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A two-compartment mathematical model of endotoxin-induced inflammatory and physiologic alterations in swine*

Nieman, Gary BS; Brown, David BS; Sarkar, Joydeep PhD; Kubiak, Brian MD; Ziraldo, Cordelia BS; Dutta-Moscato, Joyeeta MS; Vieau, Christopher BS; Barclay, Derek BS; Gatto, Louis PhD; Maier, Kristopher PhD; Constantine, Gregory PhD; Billiar, Timothy R. MD; Zamora, Ruben PhD; Mi, Qi PhD; Chang, Steve MS; Vodovotz, Yoram PhD

doi: 10.1097/CCM.0b013e31823e986a
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Objective: To gain insights into individual variations in acute inflammation and physiology.

Design: Large-animal study combined with mathematical modeling.

Setting: Academic large-animal and computational laboratories.

Subjects: Outbred juvenile swine.

Interventions: Four swine were instrumented and subjected to endotoxemia (100 µg/kg), followed by serial plasma sampling.

Measurements and Main Results: Swine exhibited various degrees of inflammation and acute lung injury, including one death with severe acute lung injury (PaO2/FIO2 ratio μ200 and static compliance μ10 L/cm H2O). Plasma interleukin-1β, interleukin-4, interleukin-6, interleukin-8, interleukin-10, tumor necrosis factor-α, high mobility group box-1, and NO2 -/NO3 - were significantly (p μ .05) elevated over the course of the experiment. Principal component analysis was used to suggest principal drivers of inflammation. Based in part on principal component analysis, an ordinary differential equation model was constructed, consisting of the lung and the blood (as a surrogate for the rest of the body), in which endotoxin induces tumor necrosis factor-α in monocytes in the blood, followed by the trafficking of these cells into the lung leading to the release of high mobility group box-1, which in turn stimulates the release of interleukin-1β from resident macrophages. The ordinary differential equation model also included blood pressure, PaO2, and FIO2, and a damage variable that summarizes the health of the animal. This ordinary differential equation model could be fit to both inflammatory and physiologic data in the individual swine. The predicted time course of damage could be matched to the oxygen index in three of the four swine.

Conclusions: The approach described herein may aid in predicting inflammation and physiologic dysfunction in small cohorts of subjects with diverse phenotypes and outcomes.

Supplemental Digital Content is available in the text.

From the Department of Surgery (GN, BK, CV, KM), Upstate Medical University, Syracuse, NY; Immunetrics (DB, JS, SC), Pittsburgh, PA; Departments of Computational Biology (CZ), Surgery (CZ, JDM, DB, TRB, RZ, YV), Mathematics and Biostatistics (GC), and Sports Medicine and Nutrition (QM), Center for Inflammation and Regenerative Modeling (CZ, JDM, RZ, QM, YV), McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh; and the Department of Biology (LG), SUNY Cortland, Cortland, NY.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s Web site (

Supported, in part, by NIH grants R33-HL-089082, P50-GM-53789, and R01-HL080926.

Drs. Brown, Sarkar, and Chang are employees of Immunetrics. Dr. Vodovotz consulted for Immunetrics and holds equity interest and stock options in Immunetrics. The remaining authors have not disclosed any potential conflicts of interest.

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© 2012 by the Society of Critical Care Medicine and Lippincott Williams & Wilkins