(111d) Modeling Endotoxin-Induced Systemic Inflammation Using An Indirect Dynamic Response Approach

Endotoxin is a major component of the outer membrane of gram-negative bacteria, and the inflammation caused by the potent activation of the innate immune system by this moiety or other immunostimulants may be a complicating factor in many serious diseases including trauma, burns, invasive surgery and organ-specific illnesses (cystic fibrosis, inflammatory bowel disease, liver disease, kidney dialysis complications, asthma and autoimmune diseases). Sepsis, defined as systemic inflammation plus bacterial infection, kills between 30-50% of its victims in approximately 750,000 patients per year in the United States. Although an increasing number of therapeutics for treating the condition have entered clinical trials only a handful have advanced to late-stage testing and so far only one directed at coagulopathy has been efficacious enough for FDA approval. Efforts to design treatments for sepsis have targeted early events in the pathophysiological process such as endotoxin release but due to its complexity therapeutic strategies have been elusive [1].

Bacterial infection induces an acute inflammatory response (AIR), characterized by a cascade of events during which multiple cell types are deployed in order to locate pathogens, recruit other cells and eventually eliminate the offenders and restore homeostasis. Under normal circumstances, the inflammatory response is activated and once the pathogens are cleared, reparative processes begin and the response then abates. However, in some cases anti-inflammatory processes fail and an amplified, runaway inflammation turns what is normally a beneficial, reparative process into a detrimental physiological state characterized by systemic inflammation which can lead to multiple organ failure and sepsis with a significant rate of morbidity and mortality [2].

The complex and multiplex characteristics of AIR and its complications have been thought to be a leading potential reason for the inability to propose effective clinical intervention strategies [3]. The nature of the response has lead researchers to the realization that mathematical models of AIR [4, 5] might provide rational leads for the development of strategies that promote the resolution of the response and the eventual establishment of homeostasis. For obvious reasons ab initio development of predictive models of complex biological processes, such as AIR, is not possible. Therefore, model development needs to be based on relevant experimental measurements with appropriate injury models. The primary goal of this study is to unravel the complex nature of the host defense system by revealing the essential temporal transcriptional responses of a Human Endotoxin Model *. Gene expression in whole blood leukocytes was determined by microarray analyses immediately before and at 2, 4, 6, 9 and 24 hr after intravenous administration of bacterial endotoxin (2 ng/kg) to healthy human subjects. We test the hypothesis that there is an underlying set of distinct and coherent transcriptional profiles that are maximally affected by the endotoxin stimulus and therefore they can used to describe the dynamic progression of the perturbed biological system. One of the key aspects of our approach is the efficient decomposition of the entire dynamics of the system into its critical components (essential responses). We first apply a symbolic representation of time series data that allows for clustering probe sets that are highly similar in gene expression profile [6]. Based on the most statistically significant expression motifs we apply an optimization ? based algorithm that gives us three distinct temporal responses that are maximally affected by the stimulus ? henceforth termed essential responses. These characterize the critical components the inflammatory response and include: the pro-inflammatory response, comprised of pro-inflammatory mediators such cytokines, chemokines; the anti-inflammatory response, comprised of anti-inflammatory mediators such IL-10); and a final response characteristic of tissue damage leading to organ dysfunction, comprised of genes associated with the bioenergetics of the system.

These three essential responses, along with a standard pharmacodynamic model for simulating the clearance of endotoxin are combined in an integrative PK/PD model using the principles of Indirect Response (IDR) [7, 8]. The resulting model is described by a set of coupled ordinary differential equations containing the key aspects of inflammation such as pro-inflammation, anti-inflammation and organ dysfunction. Such quantifiable models are critical enablers towards understanding the connectivity of the critical components of the immune system, the relationships among various components and offers opportunities for unraveling the control mechanisms of the onset and resolution of systemic inflammation.

1. Martin GS, Mannino DM, Eaton S, Moss M: The epidemiology of sepsis in the United States from 1979 through 2000. N Engl J Med 2003, 348:1546-1554. 2. Laroux FS: Mechanisms of inflammation: the good, the bad and the ugly. Front Biosci 2004, 9:3156-3162. 3. Seely AJ, Christou NV: Multiple organ dysfunction syndrome: exploring the paradigm of complex nonlinear systems. Crit Care Med 2000, 28:2193-2200. 4. Day J, Rubin J, Vodovotz Y, Chow CC, Reynolds A, Clermont G: A reduced mathematical model of the acute inflammatory response II. Capturing scenarios of repeated endotoxin administration. J Theor Biol 2006, 242:237-256. 5. Reynolds A, Rubin J, Clermont G, Day J, Vodovotz Y, Bard Ermentrout G: A reduced mathematical model of the acute inflammatory response: I. Derivation of model and analysis of anti-inflammation. J Theor Biol 2006, 242:220-236. 6. Yang E, Maguire T, Yarmush ML, Berthiaume F, Androulakis IP: Bioinformatics analysis of the early inflammatory response in a rat thermal injury model. BMC Bioinformatics 2007, 8:10. 7. Krzyzanski W, Jusko WJ: Mathematical formalism for the properties of four basic models of indirect pharmacodynamic responses. J Pharmacokinet Biopharm 1997, 25:107-123. 8. Sharma A, Jusko WJ: Characteristics of indirect pharmacodynamic models and applications to clinical drug responses. Br J Clin Pharmacol 1998, 45:229-239. 9. Garrido MJ, Valle M, Campanero MA, Calvo R, Troconiz IF: Modeling of the in vivo antinociceptive interaction between an opioid agonist, (+)-O-desmethyltramadol, and a monoamine reuptake inhibitor, (-)-O-desmethyltramadol, in rats. J Pharmacol Exp Ther 2000, 295:352-359. 10. Josa M, Urizar JP, Rapado J, Dios-Vieitez C, Castaneda-Hernandez G, Flores-Murrieta F, Renedo MJ, Troconiz IF: Pharmacokinetic/pharmacodynamic modeling of antipyretic and anti-inflammatory effects of naproxen in the rat. J Pharmacol Exp Ther 2001, 297:198-205. 11. Mager DE, Neuteboom B, Efthymiopoulos C, Munafo A, Jusko WJ: Receptor-mediated pharmacokinetics and pharmacodynamics of interferon-beta1a in monkeys. J Pharmacol Exp Ther 2003, 306:262-270. 12. Seely AJ, Christou NV: Multiple organ dysfunction syndrome: exploring the paradigm of complex nonlinear systems. Crit Care Med 2000, 28:2193-2200.