1. Vaccines and Related Biological Products Advisory Committee Meeting DMID/NIAID efforts that support the pathway to licensure for protective antigen-based anthrax vaccines for a post- exposure indication using the animal rule Ed Nuzum DVM, PhD Chief, Biodefense Vaccines and other Biological Products Development Section Division of Microbiology and Infectious Diseases NIAID/NIH/HHS November 16, 2010

2. Presentation Overview Presentation includes –Background and DMID/NIAID efforts –Data and concepts meant to be independent of sponsor-associated issues Presentation does not include –Vaccine comparison –Specific vaccine doses or immune response metrics –Discussion of “how good is good enough”

3. Underlying Themes Correlates of Protection-the immune response/protection relationship “Humanized” dose for animals-how the model is made relevant

4. Overview of Anthrax Vaccine Model Development Program DMID-led Animal Studies Group Key elements: –participation by funding and regulatory agencies, product sponsors and SMEs –weekly to monthly teleconferences –draft protocol review and standardization –“real time” data review to facilitate discussion of next steps and subsequent study start –essentially USG-driven that included sponsor awareness and input 4

5. Program Purpose Support approval of new vaccines using the FDA “Animal Rule” To develop animal models, generate efficacy data, analyze and extrapolate that data in a manner that will provide for a prediction of vaccine efficacy in humans. Utilize as much data as possible…meta analysis

6. Summary of GUP Studies Used for Meta-analysis SpeciesNumberVaccines Used Challenge Time Rabbit14rPA2 weeks (2) 4 weeks (5) 10 weeks (7) Rabbit2AVA10 weeks Rhesus3rPA AVA 10 weeks (2) 12, 30 and 52 months (1) Cyno5rPA2 weeks (1) 5 weeks (1) 10 weeks (3) Total24 6

7. Predicting Survival Based on TNA Peak Strong predictive TNA Peak effect for all species and vaccine types. Included only challenges of at least 10 weeks. For challenges before 2 weeks after last vaccination, do not obtain peak TNA before challenge. May be some small additional dose and/or adjuvant effects, but these do not change TNA peak effects very much.

8. Terminology and Definitions Fleming Definitions LevelTermVaccine TermDefinition 1true clinical efficacy clinical endpoint 2 validated surrogate (Prentice Criteria) surrogate variable (endpoint) explains all of the clinical benefit 3 non-validated surrogate ‘reasonably likely to predict clinical efficacy’ predictive correlate Variable (endpoint) statistically related to clinical endpoint, reasonably likely to predict clinical benefit, mechanism (science) based 4 correlate (non-validated surrogate) correlate variable (endpoint) statistically related to clinical endpoint Fleming T, Health Affairs 24: 67-78, 2005

9. Prediction of Human Efficacy Vaccine Efficacy (VE) = 100 * (1-RR) RR = relative risk of event Prob (event when vaccinated) / Prob (event when not vaccinated) = ratio of two %’s (binomial) or ratio of two Poisson rates or hazard ratio (survival models) VE usually requires a true clinical trial which measures disease rates Animal Rule allows bridging animal efficacy data to human immunologic data to predict clinical benefit Clinical Endpoint Immunological (animal death) Response Clinical Benefit (biomarker, etc.) CoP

10. The Animal Rule and our approach for applying it.

11. “Animal Rule” 21 CFR 601.91(a)(1-4) –(a) FDA may grant marketing approval….based on adequate and well-controlled animal studies when….product is reasonably likely to produce clinical benefit in humans. (1) There is a reasonably well-understood pathophysiological mechanism of the toxicity of the substance and its prevention or reduction…. (2) The effect is demonstrated in more than one animal species with response expected to be predictive for humans….or a sufficiently well-characterized single species model predictive of human response… (3)The animal study endpoint is clearly related to the desired benefit in humans, generally survival enhancement or prevention of major morbidity (4) The….kinetics and pharmacodynamics of the product or other relevant data or information, in animals and humans, allows selection of an effective dose in humans. 11

12. “Humanized” Animal Dose Why important –Relevance of model to human vaccine response –Advanced studies: pivotal efficacy, immunity duration, challenge break-through, interference/interaction, time-to-protection, label changes Definition: A dose of final formulation vaccine that, in the animal model, elicits protective immune responses which are at or below those achieved in humans when humans are given an appropriately safe dose and regimen of the same vaccine. That a humanized dose is obtained is more important than how Clinical data is needed as soon as possible to guide model development…the “immunological bridge” 12

13. Two Approaches for Using Immune Response to Predict Protection/Clinical Benefit Procedures for Estimating the Relationship Protective Level (amenable to non continuous data) Cut-off value above which a certain percent are expected to be protected Survival cut-off model does not consider confidence intervals, any survivors below or any deaths above cut-off Lower confidence limit survival cut-off model does not consider any survivors below or any deaths above cut-off Continuous Data Relationship Logistic Regression: Estimates the probability of death at a given antibody level Relationship between response to vaccine and protection/clinical benefit Does consider CIs and all survivors and non-survivors Caveats: data may not be continuous, response may not be dose-dependent 13

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17. Using Immune Responses to Predict Protection/Clinical Benefit Procedures for Estimating the Relationship Protective Level (amenable to non continuous data) Cut-off value above which a certain percent are expected to be protected Survival cut-off model does not consider confidence intervals, any survivors below or any deaths above cut-off Lower confidence limit survival cut-off model does not consider any survivors below or any deaths above cut-off Continuous Data Relationship Logistic Regression: Estimates the probability of death at a given antibody level Relationship between response to vaccine and protection/clinical benefit Does consider CIs and all survivors and non-survivors Caveats: data may not be continuous, response may not be dose-dependent 17

18. Using the Population Vaccine Efficacy Model Rabbit Human Average probability of survival (estimated VE) in human population 18

19. An overview of our studies and results

20. Vaccines and Diluents Diluents (for most recent studies) –Cyno studies: saline; constant antigen/adjuvant ratio –Rabbit studies: saline with adjuvant; constant adjuvant concentration Vaccines –Recombinant Protective Antigen expressed in E. coli or avirulent B. anthracis; various formulations –BioThrax® (Anthrax Vaccine Adsorbed); cell-free filtrate of microaerophilic cultures of an avirulent, nonencapsulated strain of Bacillus anthracis

21. General Use Prophylaxis (GUP) Scenario: Vaccine administered IM prior to aerosol challenge exposure Meant to simulate classical use of vaccines for prevention of disease

22. Purpose: To evaluate host dose-dependent response to vaccine and associated protection against lethal aerosol challenge Species: NZW Rabbit and Cynomologus macaque Treatment groups: 4-6 doses; 2-5 fold dilutions; 6-12 per group Vaccination days 0 and 28 IM Challenge Day 70; 200 LD50 (Ames) Parameters evaluated: survival, clinical signs, antibody response (ELISA and TNA), hematology, clinical chemistry, CRP, bacteremia, B-cell memory, gross and histopathology GUP Study Design (most typical)

23. Immune Kinetics For 2 Rabbit GUP Studies That Used Different Vaccines 23

24. Survival Curves For 2 Rabbit GUP Studies That Used Different Vaccines 24

25. Regression Curve (TNA) for 2 Rabbit GUP Studies That Used Different Vaccines 25

26. Regression Curve (ELISA) for 2 Rabbit GUP Studies That Used Different Vaccines 26

27. Rabbit Antibody Response Associated with 90% Survival Probability

28. Immune Kinetics (TNA) for a NHP GUP Study Error bars not displayed when based on a sample size of n=2 28

29. Immune Kinetics (ELISA) for a NHP GUP Study Error bars not displayed when based on a sample size of n=2 29

30. Survival Curves for NHP GUP Study 30

31. Week 6 Regression Curve for Combined Data (TNA) From 2 NHP GUP Studies 31

32. Anthrax Vaccine NHP and Rabbit Regression Curves-Week 6 32

33. Passive Immunization Study Design Purpose: To assess protective efficacy of IP administered human AVA plasma or IgG purified from human AVA plasma Species: NZW Rabbit Treatment groups: 3 per test material---7, 14 and 28 mg/kg IP; 8 per group; Dosing day 0 Aerosol challenge: 24-36 hours after dosing; 200 LD50 Parameters evaluated: survival, clinical signs, antibody kinetics (ELISA and TNA), bacteremia

34. Survival Curves for 1 Rabbit Passive Immunization Study 34

35. Regression Curves for Rabbit Passive Immunization in Relation to NHP and Rabbit Active Immunization 35

36. Anthrax Vaccine Post Exposure Prophylaxis (PEP) Scenario: In conjunction with antibiotics, vaccine is administered IM 6-12 hours after aerosol challenge exposure but prior to onset of clinical signs Meant to simulate use of vaccine in a post-event situation to enhance protection after latent spore germination Objective is to statistically demonstrate benefit of vaccination, when combined with antibiotic -Antibiotic and vaccine regimen not required to mimic human regimens Sought an antibiotic regimen that keeps animals alive just long enough for the vaccine response to prevail –Straightforward in rabbits, not in NHPs

37. Rabbit Vx PEP Model NZW rabbits used in our model development Day 0: Challenge w/ 200 LD 50 Ames, aerosol 6-12 hours post challenge, initiate: –Levofloxacin: 1X daily for 7 days, 50 mg/kg, oral –Vaccine: Days 0 & 7, intramuscular, 3 doses Monitor survival for 28 days –Immune response, bacteremia, clinical signs, clinical chemistry, hematology, pathology & histopathology

38. Immune Kinetics for 3 Rabbit PEP Studies with 3 Different Vaccines 38

39. Survival Curves for 3 Rabbit PEP Studies with 3 Different Vaccines 39

40. NHP Vx PEP Model Same approach as rabbit Saw greater survival compared to rabbits with antibiotics alone Antibiotic approaches evaluated –earlier start –shorter duration –lower dose –bacteriostatic antibiotic All were unsuccessful 40

41. PEP Summary Rabbit model is standardized and robust –Pivotal studies will utilize humanized vaccine dose and final formulation Standardized and robust NHP model that demonstrates vaccine benefit has been unattainable –NIAID will not be developing model further 41

42. GUP Summary A data-driven, iterative USG and Industry effort has resulted in an extensive animal efficacy database for anthrax vaccines Multiple studies that span many years, multiple species, multiple vaccines Presented an approach for extrapolation of animal efficacy data to predict efficacy in humans A strong correlative relationship exists between anti-PA Ab and protection Foundation formed for future pivotal studies Passive immunization data demonstrate that anti-PA antibodies are protective in a dose-dependent manner but probably not useful for CoP Model studies should incorporate the “humanized” dose as soon as possible Attainment of clinical data and final formulation product as soon as possible are important to guide model development and “humanized” dose determination 42

43. “All models are wrong, but some are useful.” George E.P. Box 43

44. Participating Organizations USG –DMID/NIAID –CBER/FDA –USAMRIID (DoD) –CDC –BARDA Consultants –Meade Biologics, LLC (regulatory) –Blair and Company (statistical) –Columbia University of New York (statistical) Industry –Battelle Biomedical Research Center –Product sponsors Emergent BioSolutions, Inc. (VaxGen) PharmAthene, Inc. (Avecia)

45. GUP Summary A data-driven, iterative USG and Industry effort has resulted in an extensive animal efficacy database for anthrax vaccines Multiple studies that span several years, multiple species, multiple vaccines An approach for extrapolation of animal efficacy data to predict efficacy in humans has been presented A strong correlative relationship exists between anti-PA Ab and protection Foundation formed for future pivotal studies Passive immunization data demonstrate that anti-PA antibodies are protective in a dose-dependent manner but probably not useful for CoP Model studies should incorporate the “humanized” dose as soon as possible Attainment of clinical data and final formulation product as soon as possible are important to guide model development and “humanized” dose determination 45

46. Backup Statistical Analysis Slides

47. Overview of Separate Analyses SpeciesVaccine type Significant Peak Ed50 effect? Additional significant challenge time effect? Additional significant dose effect? Additional significant diluent effect? RhesusAVAYes, p<0.0001No, p=0.28No, p=0.49Not testable rPaYes, p<0.0001Not testableNo, p=0.29Not testable CynosAVAYes, p=0.027 (but N=6) Not testable rPAYes, p<0.0001Yes, p=0.008 (weeks 2, 5, 10 challenge groups) Yes, p=0.0365 (chall. week 10 only) Yes, p=0.008 (additional effect after dose, c. week 10 only) RabbitsAVAYes, p<0.0001Yes, p<0.0001 (weeks 2 and 10 challenge) No, p=0.420 (challenge week 10 only) Not testable rPAYes, p<0.0001No, p=0.93 (diluent= saline, chall. weeks 4, 10) Yes, p<0.0001 (d=adjuvant, weeks 2,4, 10) Yes, p=0.042 (challenge week 10 only) No, p=0.204 (additional effect after dose, challenge week 10 only) 47

48. Cynos rPA (challenge week 10 only): Logistic models Variables Included P-values (analysis of deviance ) Coefficient of Discrimination* Odds Ratio= odds(x+1)/odds( x), with 95% CI LogB(ed50Peak)<0.00010.4585.9 (3.6, 10.6) LogB(ed50Peak) Dose (ug) (continuous) <0.0001 0.0365 0.4763.8 (2.1, 7.6) logB(ed50Peak) Dose (ug) Diluent+diluent by dose <0.0001 0.0365 0.0080 0.5252.2 (1.1, 4.5) *Coefficient of discrimination is a measure of how well a logistic model predicts survial. (Like an R-square for logistic regression) 48

49. Rabbit rPA (challenge week 10 only): Logistic models Variables Included P-values (analysis of deviance ) Coefficient of Discrimination* Odds Ratio= odds(x+1)/odds( x), with 95% CI LogB(ed50Peak)<0.00010.57510.0 (6.5, 16.6) LogB(ed50Peak) Dose (ug) (continuous) <0.0001 0.042 0.5828.1 (5.1, 13.7) logB(ed50Peak) Dose (ug) Diluent+diluent by dose <0.0001 0.042 0.204 0.5857.9 (5.0, 13.3) *Coefficient of discrimination is a measure of how well a logistic model predicts survial. (Like an R-square for logistic regression) 49

50. 4 Week Challenge Nearly identical results when you include the challenge week 4 data. There was no challenge week 4 for the AVA studies. 50

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