Trauma and systemic infection elicit an acute inflammatory response. Inflammation involves complex interactions among leukocytes, their products (cytokines, free radicals, and proteases), and the tissue damage/dysfunction that ensues. This multiple organ dysfunction often manifests as septic shock and severe lung dysfunction, referred to collectively as the acute respiratory distress syndrome (ARDS), and contributes to the 215,000 annual deaths in the U.S. from sepsis. The complexity of this process has stymied the progress towards immunomodulatory ARDS therapeutics. We have developed a mathematical model of these elements in order to unravel this complex interplay in various settings of acute inflammation, and have calibrated distinct variants of this model with data from mice, rats, swine, and humans (University of Pittsburgh Inflammatory Analyte/Modeling Component). Our modeling platform has been used to gain both basic and translational insights, the latter including simulated (in silico) clinical trials. In conjunction with these efforts, we developed a sepsis + gut ischemia/reperfusion (Sepsis+I/R) porcine model that mimics the pathogenesis of human septic shock and ARDS (Upstate Medical University ARDS Animal Model Component). We hypothesize that mathematical analysis of the complex biochemical and physiologic data generated in our Sepsis+I/R model will enable us to isolate key therapeutic targets and to test novel therapeutics;one such agent is the modified tetracycline COL-3.
Our Specific Aims are: 1) to develop a robust mathematical model describing Sepsis+I/R- induced shock and ARDS in swine, its pathologic consequences, and possible therapies, 2) to utilize COL-3 as a tool to further calibrate the mathematical model and 3) to demonstrate that NE, MMP-2 and MMP-9 are critical components in Sepsis+I/R-induced septic shock and ARDS pathogenesis. Our calibrated mathematical model will be used to conduct in silico clinical trials and establish a platform for the rational development of novel ARDS therapeutics. The in silico trials will be validated in animal experiments. The proposed translational studies will develop a robust mathematical model capable of describing the complex pathogenesis of sepsis-induced ARDS and identify target molecules whose modulation would significantly improve clinical outcome. Sepsis and septic shock are responsible for more that 215,000 deaths in the United States per year with an annual healthcare cost of over $16 billion dollars. Due to the complexity of sepsis pathogenesis, it has been exceedingly difficult to develop drugs that will reduce this high mortality. In the proposed study, we will analyze the mechanisms of sepsis mortality with a sophisticated mechanistic, partially-calibrated mathematical model that will be able to identify molecular """"""""choke points"""""""" that if blocked will arrest the progression of this disease and significantly reduce sepsis-induced morbidity and mortality.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Exploratory/Developmental Grants Phase II (R33)
Project #
5R33HL089082-03
Application #
7923836
Study Section
Special Emphasis Panel (ZHL1-CSR-K (M1))
Program Officer
Harabin, Andrea L
Project Start
2008-09-01
Project End
2011-08-31
Budget Start
2010-09-01
Budget End
2011-08-31
Support Year
3
Fiscal Year
2010
Total Cost
$439,484
Indirect Cost
Name
University of Pittsburgh
Department
Surgery
Type
Schools of Medicine
DUNS #
004514360
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213
Vodovotz, Yoram (2016) Reverse Engineering the Inflammatory ""Clock"": From Computational Modeling to Rational Resetting. Drug Discov Today Dis Models 22:57-63
Brown, David; Namas, Rami A; Almahmoud, Khalid et al. (2015) Trauma in silico: Individual-specific mathematical models and virtual clinical populations. Sci Transl Med 7:285ra61
Namas, Rami A; Mi, Qi; Namas, Rajaie et al. (2015) Insights into the Role of Chemokines, Damage-Associated Molecular Patterns, and Lymphocyte-Derived Mediators from Computational Models of Trauma-Induced Inflammation. Antioxid Redox Signal 23:1370-87
Emr, Bryanna; Sadowsky, David; Azhar, Nabil et al. (2014) Removal of inflammatory ascites is associated with dynamic modification of local and systemic inflammation along with prevention of acute lung injury: in vivo and in silico studies. Shock 41:317-23
Azhar, Nabil; Vodovotz, Yoram (2014) Innate immunity in disease: insights from mathematical modeling and analysis. Adv Exp Med Biol 844:227-43
Aerts, Jean-Marie; Haddad, Wassim M; An, Gary et al. (2014) From data patterns to mechanistic models in acute critical illness. J Crit Care 29:604-10
Vodovotz, Yoram; Billiar, Timothy R (2013) In silico modeling: methods and applications to trauma and sepsis. Crit Care Med 41:2008-14
Namas, Rami; Zamora, Ruben; Namas, Rajaie et al. (2012) Sepsis: Something old, something new, and a systems view. J Crit Care 27:314.e1-11
Dick, Thomas E; Molkov, Yaroslav I; Nieman, Gary et al. (2012) Linking Inflammation, Cardiorespiratory Variability, and Neural Control in Acute Inflammation via Computational Modeling. Front Physiol 3:222
Gomez, Hernando; Mesquida, Jaume; Hermus, Linda et al. (2012) Physiologic responses to severe hemorrhagic shock and the genesis of cardiovascular collapse: can irreversibility be anticipated? J Surg Res 178:358-69

Showing the most recent 10 out of 28 publications