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Modeling Inflammation and Wound Healing: Translational Insights into Diabetic Foot Ulcer Pathology

Computational Biology Seminar – October 30, 2006. Modeling Inflammation and Wound Healing: Translational Insights into Diabetic Foot Ulcer Pathology. Center for Inflammation and Regenerative Modeling Yoram Vodovotz, Depts. of Surgery and Immunology University of Pittsburgh.

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Modeling Inflammation and Wound Healing: Translational Insights into Diabetic Foot Ulcer Pathology

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  1. Computational Biology Seminar – October 30, 2006 Modeling Inflammation and Wound Healing: Translational Insights into Diabetic Foot Ulcer Pathology Center for Inflammation and Regenerative Modeling Yoram Vodovotz, Depts. of Surgery and Immunology University of Pittsburgh

  2. The important thing in science is not so much to obtain new facts as to discover new ways of thinking about them. Sir William Bragg (1862-1942)

  3. The Era of Interdisciplinary, Translational Research • The NIH Roadmap Initiative stresses the need to translate the wealth of reductionist data into viable clinical practice through interdisciplinary research and mathematical models • The FDA “Critical Path” document stresses the need to incorporate computational methods into drug and device design • Nowhere is this need greater than in diseases that involve inflammation and tissue healing

  4. Inflammation is a puzzle • Inflammation accompanies infection, trauma, ischemia, hemorrhagic shock, acute pancreatitis, and more chronic diseases (rheumatoid arthritis, ulcerative colitis, cardiovascular disease, diabetes, cancer) • The molecular events are qualitatively similar but quantitatively different, and occur with different time courses • Proper healing and regeneration require a well-regulated inflammatory response • Therapies can themselves cause or exacerbate inflammation, while drugs that modulate inflammation may have unforeseen effects on organ function. • Current status: • Dearth of effective therapies • Re-evaluation of existing anti-inflammatory drugs for other indications

  5. Diabetes and diabetic foot ulcers • Both inflammation and wound healing are deranged in chronic, non-healing foot ulcers, constituting a major complication of diabetes • Diabetic foot ulcers (DFU) are caused by both vascular and neurologic complications of diabetes, in combination with persistent opportunistic infections and deficient wound healing • Over 10 million Americans carry a diagnosis of diabetes, and an estimated 5 million more are undiagnosed diabetics. The incidence of foot ulcer in this population approaches 2% per year • Reported average treatment costs range from $2,500 to almost $14,000 per year • DFU are responsible for more than 50,000 major lower extremity amputations in the United States every year • Diabetics with foot ulcers have more than twice the mortality of diabetics with healthy feet

  6. Complement Endothelium Adaptive immunity Insult/therapy Core innate immunity Genetics Environment Tissue Healing Coagulation Pieces of the Inflammation Puzzle

  7. Statistical Models Association-based Limited use of prior knowledge Require large amounts of data Prediction within “training data” yet is often used incorrectly to predict outside such data Mechanistic Models Highly causal Extensive use of prior knowledge Allow explanation of emergent phenomena More difficult Predictions outside of “training data” Statistical vs. Mechanistic ModelsWhitcomb et al, Dig.Dis.Sci. 2005. 50:2195

  8. Translational Systems Biology • Current state • Extremely detailed information about highly simplified systems • Simulations designed for in vitro validation • “omics” information on clinically relevant situations, with no framework with which to interpret the data • Our approach • Models structured for immediate translational utility • Clinical trial simulations • Diagnostics • Rational drug design • Basic insights are important but secondary • Quantitative predictions

  9. A B C D Systems Biology at the Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine (www.mirm.pitt.edu/cirm) Develop Representative Models Research Biological Mechanisms Collect Biomarker Data Calibrate Models to Data Use Model for Predictions And Clinical Trial Simulations

  10. International Conference on Complexity in Acute Illness Tysons Corner, VA, October 19-21, 2006 • Surgery (Pitt) • Tim Billiar • Ruben Zamora • Rosie Hoffman • David Hackam • Robert Kormos • David Steed • Edith Tzeng • Juan Ochoa • Claudio Lagoa • Andres Torres • Binnie Bitten • Derek Barclay • Thierry Clermont • Critical Care Medicine (Pitt) • Gilles Clermont • Mitchell Fink • John Kellum • Russ Delude • Juan Carlos Puyana • McGowan Institute (Pitt) • Alan Russell • John Murphy • William Federspiel • William Wagner • SHRS (Pitt) • Cliff Brubaker • Kittie Verdolini • Nicole Li • Medicine (Pitt) • David Whitcomb • Marc Roberts • Children’s Hospital of Pittsburgh • David Hackam • Jeffrey Upperman • Pat Hebda • Raphael Hirsch • Mathematics (Pitt) • Carson Chow • Bard Ermentrout • Jonathan Rubin • Beatrice Riviere • Ivan Yotov • David Swigon • Judy Day • Qi Mi • Mathematics (CMU) • Shlomo Ta’asan • Rima Gandlin • Statistics (Pitt) • Greg Constantine • Immunetrics, Inc. • John Bartels • Steve Chang • Arie Baratt • Joyce Wei • IBM • Fred Busche • Cook County Hospital • Gary An • University of Cologne • Eddy Neugebauer • Rolf Lefering • Ludwig Boltzmann Institute • Heinz Redl • SUNY-Upstate • Gary Nieman • David Carney SCAI http://www.scai-med.org Challenges and Rewards on the Road to Translational Systems Biology in Acute Illness: Four Case Reports from Interdisciplinary Teams Gary An MD (1), C. Antony Hunt PhD (2), Gilles Clermont MD (3, 6), Edmund Neugebauer, PhD (4) and Yoram Vodovotz PhD (5, 6) 1) Department of Surgery, Northwestern University Feinberg School of Medicine 2) Biosystems Research Group, University of California, San Francisco 3) Department of Critical Care Medicine, University of Pittsburgh 4) Institute for Research in Operative Medicine, University of Witten/Herdecke 5) Departments of Surgery and Immunology, University of Pittsburgh 6) Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA J. Crit. Care, submitted for publication Inflammation Modeling is a Team Sport

  11. Damage/dysfunction: A Central Feature of Mathematical Models of Inflammation • Global models of acute inflammation • 3-variable model of sepsis (Kumar et al, J. Theoretical. Biol. 2004. 230:145): analysis of overall dynamics of acute inflammation • 4-variable model of sepsis (Reynolds et al, J.Theor.Biol. 2006. 242:220) • 4-variable model of endotoxemia, showing preconditioning (Day et al, J.Theor.Biol. 2006. 242:237) • Larger, qualitative model of sepsis: more detailed simulation of sepsis, along with simulation of anti-TNF clinical trial (Clermont et al, Crit.Care Med.2004. 32:2061) • Modification of this model to account for the known features of anthrax infection (Kumar et al, in preparation) • Yet larger model of acute inflammation calibrated with data in mice (Chow et al, Shock 2005.24:74). Used to examine role of trauma in hemorrhagic shock-induced inflammation (Lagoa et al, Shock. 2006, In Press) and to study features of gene knockout mice (Prince et al, Mol. Med. 2006, 12:88) • Yet larger model that includes DC, TH cells (Day et al, in preparation). Recalibrated for other species (rat, pig). Used to study cardiopulmonary bypass-induced inflammation, irreversible hemorrhagic shock, & novel therapeutics (in preparation) • Agent-based model of inflammation and wound healing (Li et al, submitted; Mi et al, submitted) • Model of gut epithelial barrier failure and bacterial translocation (Upperman etal, J.Pediatr.Res., 2006. In Press; Sullivan et al, in preparation) • Model of cellular apoptosis and the role of NO reaction products (Vodovotz et al, Curr.Mol.Med. 2004. 4:753; Bagci et al, Biophys.J. 2006. 90:1546; Bagci et al, In Preparation).

  12. Alarm/Danger Signals Inflammation Trauma/blood loss Infection/LPS Damage/Dysfunction X Anti-inflammation Central Role of Damage/Dysfunction in Models of Inflammation and Tissue Healing

  13. Agent-based Modeling • A model occurs in a particular “World” • Models are based on individual actors of a biological process (“Agents”) • Sub-divided into classes • Subject to rules (as simple as possible) • Agents move and interact with other agents • Emergent behaviors / patterns • References • G. An. Agent-based computer simulation and SIRS: building a bridge between basic science and clinical trials. Shock 16 (4):266-273, 2001. • G. An. In-silico experiments of existing and hypothetical cytokine-directed clinical trials using agent based modeling. Crit Care Med. 32:2050-2060, 2004. • Y. Vodovotz, G. Clermont, C. Chow, and G. An. Mathematical models of the acute inflammatory response. Curr.Opin.Crit Care 10:383-390, 2004.

  14. In Silico Biology? Courtesy of Dr. A. DeMaio Johns Hopkins University Agent-based Model of Inflammation and Tissue Healing

  15. Reproduced from Current Problems in Surgery, 38, 72-141, 2001 Reproduced from Surgical Clinics of North America, 83, 483-507, 2003

  16. Agent-based model reproduces the dynamics of normal skin wound healing A B Fibroblasts Neutrophils Macrophages C

  17. Known inflammatory derangements in diabetes / diabetic foot ulcers • Elevated TNF production • Hussain MJ, et al. Elevated serum levels of macrophage-derived cytokines precede and accompany the onset of IDDM. Diabetologia 1996;39(1):60-9. • Harsch IA, et al. Impaired gastric ulcer healing in diabetic rats: role of heat shock protein, growth factors, prostaglandins and proinflammatory cytokines. Eur J Pharmacol 2003;481(2-3):249-60. • Reduced expression of active TGF-b1 (Jude EB, et al. Transforming growth factor-beta 1, 2, 3 and receptor type I and II in diabetic foot ulcers. Diabet Med 2002;19(6):440-7)

  18. Questions • Will changing the agent-based model to account for either elevated TNF alone or reduced TGF-b1 alone yield a phenotype reminiscent of DFU? • Increased inflammation • Delayed healing • Will further superimposing known therapies for DFU yield predictions of improved healing? • Will the agent-based model be capable of suggesting novel therapies that manipulate TNF and TGF-b1?

  19. TNF-high TGF-low Normal Delayed healing with either TNF elevation or TGF-b1 reduction: single cases

  20. Delayed healing with either TNF elevation or TGF-b1 reduction: Population variability A B C A = Normal healing B = TNF-high C = TGF-b1-low

  21. Characteristics of simulated DFU *† A B * * * *: P< 0.05 vs. Normal; †: P< 0.05 vs. TGF-b1-low (all by Kruskall-Wallis ANOVA on ranks followed by Tukey post-hoc test). C D * * † * *† E *† F *

  22. Translational Utility of Models: In Silico Clinical Trials

  23. Simulation of existing DFU therapy:Debridement A B * * †

  24. TNF-high TGF-low TNF-high + platelets TGF-low + platelets Normal Simulation of existing DFU therapy:Platelet releasate / PDGF

  25. Simulations of novel therapies for DFU • Anti-TNF antibodies • Failed in sepsis (except very recent trial) • Used clinically for rheumatoid arthritis • TGF-b1 modulation • Latent TGF-b1 • TGF-b1 activator

  26. TGF-b Precursor Monomer (361 aa) TGF-b Precursor Dimer Latency-associated Peptide (LAP; 249 aa x 2) Active TGF-b (112 aa x 2) Latent TGF-b

  27. Simulation of an anti-TNF trial for sepsis Population • 1000 simulated organisms where we varied: • Pathogen load and virulence (mean mortality of 30%) • Time to intervention • Genetic predisposition Intervention • Three durations (6h, 24h, 48h) • Three levels of potency (2,10,20) • Removal of 18% to 55% of circulating TNF (AUC) • Absorption of circulating TNF proportional to circulating amounts

  28. Damage as a surrogate for survival: a simulated clinical trialof a-TNF in sepsis (Clermont et al, Crit.Care Med.2004 32:2061)

  29. Modeling the effects of age(Immunetrics, IBM) • Killing capacity of macrophages and neutrophils decrease with age and respiratory burst also decreases • The death rates of the resting and active macrophages increase with age to reflect decrease in the macrophage precursors and impaired macrophage chemotactic response • Production of reactive nitrogen intermediates decreases with age • Superoxide production by neutrophils and macrophages decreases with age • TNF and IL-6 production by activated macrophages decreases with age References: 1) Timothy P. Plackett, Eric D. Boehmer, Douglas E. Faunce, and Elizabeth J. Kovacs, Aging and innate immune cells, J. Leukoc. Biol. 76 (2004), 291-299. 2) Julie Plowden, Mary Renshaw-Hoelscher, Carrie Engleman, Jacqueline Katz, and Suryaprakash Sambhara, Innate immunity in aging: impact on macrophage function, Aging Cell (2004), 161-167 3) Christoph Wenisch, Sanda Patruta, Felix Daxbock, Robert Krause, and Walter Horl, Effect of age on human neutrophil function, J. Leukoc. Biol. 67 (2000), 40-45

  30. Trial design 3 Populations • 10,000 simulated patients by varying: • Pathogen load and virulence (mean mortality of 40%) • Time to intervention • Genetic variability (polymorphism) in the production of TNF, NO, TGF- • Age distribution derived from clinical data of 3 hospitals a-TNF Intervention • Three durations (6h, 24h, 48h) • Three levels of potency (1,5,10) • Absorption of circulating TNF- proportional to circulating amounts

  31. Age response to anti-TNF • UPMC Hospital A (mean age: 61.5 yrs old) • Placebo Mortality 40.2% • UPMC Hospital M (mean age: 58.9 yrs old) • Placebo Mortality 40.2% • UPMC Hospital P (mean age: 56.9 yrs old) • Placebo Mortality 39.1% • Treated Mortality 36.5% (3.6% delta) • Treated Mortality 34.0% (6.1% delta) • Treated Mortality 33.7% (5.4% delta)

  32. Activation of Latent TGF- • Induced by: • Plasmin • Tissue transglutaminase • Thrombin • Ionizing radiation • Free radicals (hydroxyl radical; NO reaction products) • b-nicotinamide adenine dinucleotide (NAD+) • Suppressed by: • LAP • Decorin • Biglycan

  33. Simulation of novel DFU therapies:TNF-high case A B * * * * *# *: P< 0.05 vs. TNF-high baseline; †: P< 0.05 vs. TGF-b1 activator; #: P< 0.05 vs. anti-TNF (all by Kruskall-Wallis ANOVA on ranks followed by Tukey post-hoc test). C * D * * *# # † E F *† * * *

  34. Simulation of novel DFU therapies:TGF-b1-low case A B * * * * * *: P< 0.05 vs. TGF-b1-low baseline; †: P< 0.05 vs. TGF-b1 activator; #: P< 0.05 vs. anti-TNF (all by Kruskall-Wallis ANOVA on ranks followed by Tukey post-hoc test). C D *# * * * # E F *# #† * * * *

  35. Literature data Prospective data Addressing controversies Basic insights Translational applications Role of TNF and TGF-b1 in DFU In silico clinical trials Rational drug / device design Patient diagnostics Changes in inflammation with gene modification, aging, etc. Summary: Modeling Inflammation and Tissue Healing

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