Modeling Critical Illness

1. Modeling Critical Illness Gilles Clermont MD, MSc Center for Inflammation and Regenerative Modeling (CIRM) and The CRISMA Laboratory Critical Care Medicine School of Medicine University of Pittsburgh

2. Objectives • The clinical problems facing Critical Illness • Our work at the Center for Regenerative and Inflammatory Modeling • The challenges We face

3. The Goal of Critical Care ? Health Disease Death Zone of opportunity

4. The big challenges in Critical Illness • Timely diagnosis • Outcome prediction • Development of targetted (personalized) therapies • Modulation of the inflammatory response

5. Root causes of these challenges • Insufficient, inaccurate data • What to measure • Point-of-care technologies • Insufficient interpretative framework • Uncertain biological mechanisms • Biological variability • Dearth of in silico disease models • Insufficient mathematics

6. Critical Illnesses Trauma/Shock Severe infections (sepsis) Inflammation Stroke Acute coronary syndrome

7. INSULT O r g a n I n j u r y Current Paradigm of Injury/Recovery Stress response • Innate immunity • Coagulation • Metabolism Anti- Inflammation Inflam- mation Recovery Time

8. The canvas…of opportunitites Physiologic manifestations Metabolic manifestations • Early mediators • TNF • IL-1 • IL-10 • IL-6 • Cells • Infection • Pathogen • Toxins • Detection • Dendritic cells • Macrophages Organ dysfunction Late mediators Death Coagulation

9. Opportunities for Immune Support Anti-LPS Anti-TNF Anti-IL-1 Anti-IL-10 Anti-HMGB1 Blood Purification Immunologic Support HMGB1 RECOVERY IL-10 IL-6 TNF INSULT IL-1 Time

10. What about immunomodulation in sepsis and trauma? • 25 years of global disaster • Simplistic rationales • Ineffective products • Poor patient selection • >70 phase II trials, > 1B dollars invested • Two possible leads • Low dose anti-inflammatory treatment • Activated protein C • Contradictory results!

11. Treating sepsis: the strength of the Consensus 2 5 3 2 18 Good or Bad?

12. The inflammatory response Huang Q, Science 2001

13. Calvano, Nature 2005

14. Modeling Inflammation at the CIRM • Multiple models of acute inflammation (sepsis, trauma/hemorrhage, biowarfare agents, phonotrauma, wound healing), organ damage/dysfunction, and healing/regeneration • Qualitative and quantitative predictions • Probing mechanisms • Have simulated device usage and guided device design • Have outlined an iterative strategy for rational drug design and administration • Have carried out simulated clinical trials in the settings of sepsis and trauma, including biowarfare applications

15. Small models of inflammation • Top-down • “Understand” the biology • Biological plausibility • High-level “map” of the biology • Building blocs for more complex models

16. Reduced models of inflammation

17. Transients for 3 possible regimen Health Aseptic death Septic death

18. Bifurcation analysis on Kpg “Septic death” “Aseptic death” “Heatlh”

19. 2-D bifurcation diagram Opportunity

20. Manipulating anti-inflammatories

21. Why complicate things • To produce a calibrated • To “intervene” in the dynamics in a realistic way, more realistic “handles” are needed • Not all “modules” need to be equally detailed • The analysis of large models: • May rapidly become intractable • May not yield useful results

22. Simulating Inflammatory Disorders at the CIRM Develop Representative Models Research Biological Mechanisms Collect Biomarker Data Use Model for Predictions Calibrate Models to Data

23. A Unified Inflammatory Response

24. Simulations of infectious agents with bioterror potential Shock 2007 JTB 2007

25. Probing mechanisms

26. In silico design of RCTs

27. Anti-TNF treatment for sepsis:A simulation study Clermont et al. 2004

28. Outcome by subgroups Pathogen load (Quartile) Q4 Q4 Q4 Q4 TNF responsiveness (Quartile) Q3 Q3 Q3 Q3 Q2 Q2 Q2 Q2 Q1 Q1 Q1 Q1 Helped by treatment Lived irrespective of treatment Harmed by treatment Died irrespective of treatment 0% 0% 25% 25% 50% 50% 75% 75% 100% 100% Percent Percent Pathogen virulence (Quartile) Anti-inflammatory responsiveness (Quartile)

29. Validating in silico simulation? • Face validity of the disease model • Knowledge of key driving factors • Disease model includes these factors • Intervention model • Mechanism of action of the proposed treatment • PK/PD data • Predictive ability on empirical data • Controlled experiments • Existing trial data

30. Disease model Must account for uncertain mechanisms Model structure recapitulates biology Predictors in a statistical model Equations/rules in white box models Must make best use of observations at hand which are often incomplete Within a given model structure, develop an understanding of the breath of parameter realizations that fit data equally well Uncertainty in the relative importance of mechanisms/interactions Many model realizations are necessary

31. Some core theoretical challenges • The variability problem • The inverse problem • The “cogent-reduction” problem

32. The variability problem • Can inter-individual variability be characterized in some fashion? • Sepsis as an example • How can mathematical models to capture variability? • Can meaningful diagnostic and therapeutic insights be achieved in the presence of such variability?

33. Day 1 cytokine levels in non-septic patients for prediction of severe sepsis TNF IL-6 2.1 4.0 2.0 3.8 TNF at baseline and 95% CI (ln pg/ml) 3.6 IL6 at baseline and 95% CI 1.9 3.4 1.8 p = 0.0009 p = 0.0087 3.2 1.7 Severe sepsis(n=268) No SS(n=1073) Severe sepsis(n=268) No SS(n=1073) Analysis restricted to day 1 levels of those patients who do NOT have severe sepsis on first day

34. Day 1 levels and survival 2.4 2.2 Baseline TNF with 95% CI 2.0 p<0.0001 1.8 Alive(n=1410) Dead(n=212) TNF IL-6 5.0 4.6 Baseline IL6 with 95% CI 4.2 3.8 p<0.0001 3.4 Alive(n=1410) Dead(n=212) IL-10 2.8 2.6 Baseline IL10 with 95% CI 2.4 p<0.0001 2.2 Alive(n=1410) Dead(n=212)

35. Day 1 cytokine levels in patients who develop ARF and those that do not TNF ARF = RIFLE-I or F Pg/ml  SEM p<0.0001 ARF (n=258) No ARF(n=1544) IL-10 IL-6 Pg/ml  SEM Pg/ml  SEM p=0.0285 p<0.0001 ARF (n=258) ARF (n=258) No ARF(n=1544) No ARF(n=1544)

36. Trajectories • Trajectories are consistent within individuals • “Shapes” are often consistent across individuals Suffredini et al. 1996

37. Cytokines by Outcome (60 days)

38. Trajectory Analysis Log IL-10 Log IL-6 H H M M L L 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Day Day

39. Some good news • Standard (although by no means elementary) statistical techniques identify “classes” of patients • Physiology • Omics • Qualitative patterns, but not magnitude of response, often preserved across individuals • Within species

40. One model – one patient M M M

41. Disease models Must account for uncertain mechanisms Model structure recapitulates biology Predictors in a statistical model Equations/rules in white box models Must make best use of observations at hand which are often incomplete Within a given model structure, develop an understanding of the breath of parameter realizations that fit data equally well Uncertainty in the relative importance of mechanisms/interactions Many model realizations are necessary

42. Patient-specific metamodel M1 M2 Mn E(Mn)≡ Metamodel or Ensemble Where the individual models vary in their mathematical structure and parameters

43. Population-level Ensemble E(Mn) E(Mp) E(Mq) • Empirical rather than phenomenological

44. Targetted TherapyModel Predictive Control (MPC) • “Base” System • Real patient • Metamodel • INPUT • Actual data • OUTPUT • Actual data • Predicted data • Sensor • Error between • actual/desired Actuator Controller • The desired output is “health” • The MPC method uses actual data and model simulations to estimate output: the discrepancy is estimated (Sensor) • The MPC method suggest an optimal intention strategy which is time dependent (Actuator)

45. MPC Schematic Babatunde A. Ogunnaike and W. Harmon Ray. Process Dynamics, Modeling, and Control (Topics in Chemical Engineering). Oxford University Press: New York, 1994. pg. 997. Past Future Reference Trajectory, R R(k+2) R(k+1) Predicted Output, p R(k) p(k+1) p(k) Measured Output, M R(k-2) M(k) u(k+m-1) Control Action, u p(k-2) M(k-2) uk m – move horizon h – prediction horizon k – current simulation time step ΔU k k+1 k+2 k+m-1 k+h Horizon

46. Tailored Standard • Early treatment, frequent titration are key • Measurement frequency congruent with the time scale of the process being modulated is key Patient 20 Patient 405

47. The inverse problem

48. Observe Low Blood pressure Observe Blood pressure Normal Give a fluid challenge Mid-range Low time

49. Model reduction – Bottom-up models • Map “lumped observables” to biologically relevant functions/modules • Component aggregation • Identify aggregates (SVD – PCA) • Time aggregation • Spatial segregation • Data-driven vs. Knowledge-driven

50. Model reduction – Top-down models • Function vs components may be biased • What we think we know • What we can measure • Which parts of the model can/should be abstracted • Could this be driven by constraints imposed by biological laws?