1 / 24

Evaluating vaccine effects on TB infection rates in adolescent populations

Evaluating vaccine effects on TB infection rates in adolescent populations. Steve Self Vaccine and Infectious Disease Division, FHCRC. Outline. Introduction: Why infection endpoint? Modeling exposure to infection: what are risks for attenuating biological vaccine effect?

jonas-cunningham
Download Presentation

Evaluating vaccine effects on TB infection rates in adolescent populations

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Evaluating vaccine effects on TB infection rates in adolescent populations Steve Self Vaccine and Infectious Disease Division, FHCRC

  2. Outline • Introduction: Why infection endpoint? • Modeling exposure to infection: what are risks for attenuating biological vaccine effect? • Overview of possible trial designs • Discussion

  3. Why an infection endpoint? • it’s NOT a clinical endpoint • “a direct measure of how a person functions, feels and survives” –Bob Temple • it’s NOT a surrogate endpoint • a validated biomarker predictive of clinical effect

  4. Why an infection endpoint? • it’s NOT a clinical endpoint • “a direct measure of how a person functions, feels and survives” –Bob Temple • it’s NOT a surrogate endpoint • a validated biomarker predictive of clinical effect • it IS a measure of biologic activity • it IS weakly predictive of risk for clinical disease • it IS an appropriate endpoint for Phase II trials

  5. Why an infection endpoint? • HPV vaccine as illustrative example • Clinical endpoint (CIN2/3) is rare and occurs long after infection – Ph IIB/III trials large and $$$s • No known correlate of protection so plausibility for clinical vaccine efficacy difficult to argue from immunogenicity alone • Hope for effect from post-infection vaccination but count on pre-infection • Phase II test-of-concept trials used persistent HPV infection endpoint to develop stronger evidence for plausible clinical efficacy • Long term follow up provided early evidence of persistent infection as a valid surrogate • Hope for post-infection vaccine effect was dashed

  6. Is VES* plausible for TB vaccines? • Yes but not for vaccine candidates of this generation? (Kaufmann, 2012) • Animal challenge models still difficult to interpret relative to human exposures • Some epidemiologic data weakly supporting plausible vaccine effect (BCG and IGRA) * VES = vaccine efficacy to reduce infection rate (S = susceptibility) per Halloran et al (1996)

  7. Epidemiologic Studies (2002-2009):BCG / IGRA Association Soyal, 2005Turkey (979) Hill, 2006The Gambia (718) Hill, 2007The Gambia (207) Eisenhut, 2009 UK (199) Lucas, 2010 Australia (524) Roy, 2012 Europe (1128) Weak evidence (non-RCT, mixed) for BCG reducing rate of IGRA conversion 0.0 0.5 1.0 1.5 2.0 Odds Ratio (95% CI)

  8. Is VES* plausible for TB vaccines? • Yes but not for vaccine candidates of this generation (Kaufmann, 2012) • Animal challenge models still difficult to interpret relative to human exposures • Some epidemiologic data supporting plausibility (BCG and IGRA) • But VES ≈ 0 in recent infant trial • D in infant vs adolescent immune responses? • D in nature of exposure? * VES = vaccine efficacy to reduce infection rate (S = susceptibility) per Halloran et al (1996)

  9. Modeling TB exposure/infection • A biological effect at point of infection in lung might be attenuated via • rate of repeated exposure events over time • variability in infectious potential per event • Strain differences • Variation in # droplet micronuclei • Difficult to study directly; little is known • But a simple model, calibrated to known infection rate (eg 5%/yr) could answer “what if” questions

  10. Prob of infection given exposure with infectious potential q τ = 0.95 τ = 0.50 τ = 0.10 P(q;τ) τ = 0.01 P(q;τ): A family of curves, indexed by a parameter τ, that Translates an exposure event with infectious potential q to a probability that exposure will lead to a stable infection. Average of P(q;τ) over assumed distribution of q gives unconditional probability of stable infection for a single exposure Infectious Potential (q)

  11. Prob of infection given exposure with infectious potential q P(q;τ) Distribution of Infectious Potential (q) per exposure Infectious Potential (q)

  12. Rate of exposure and calibration • Assume a distribution for rate of exposure over time (N = # exposure events / year) • Average P(q;τ) over distributions of q AND N to compute expected annual rate of infection • Find which of the P(q;τ) curves (which value of τ), when averaged over q and N match the epidemiologic annual rate of infection

  13. Contour plot of values for τ calibrated to 5%/yr incidence Expected Infectious Potential q (log-scale) Too little exposure to be consistent with 5%/yr Expected # exposure events per year (log-scale) • There must be sufficient exposure to be consistent with assumed incidence • For increased rate and/or infectious potential of exposure, the per-exposure probability of infection must decrease to remain consistent with assumed incidence

  14. Model for Vaccine Effect (τ reduced by 60%) Now can compute VES for under assumed exposure model and calibrated to relevant epidemiologic incidence rate 1 – infection rate for vaccinees / infection rate for controls

  15. Contour plot of values for VES calibrated to 5%/yr incidence τ = 0.60 0.41 0.47 0.53 0.35 Expected Infectious Potential q (log-scale) Too little exposure to be consistent with 5%/yr Expected # exposure events per year (log-scale) Principle risk for attenuating biological effect is rare exposure with overwhelming infectious potential (rather than) a high rate of exposure over time

  16. Phase II trial designs with infection endpoints • Test of concept trial • Focus on testing VES > 0 • Tight control of false positive rate (0.025 1-sided) • Screening trial • Focus on testing VES > 0 • Willing to tolerate increased false positive error for better power • Ranking/selection trial • As secondary add-on to either TOC or screening • Based on VES, select best among multiple vaccines that pass initial efficacy testing stage

  17. Required Total # Endpoints (split) Design with Type 1 and Type 2 errors balanced

  18. Required Total # EndpointsProbability of passing VEScriterion, Probability of selection Prob of passing efficacy test Prob of selected as best

  19. IGRA: An imperfect test for TB infection • Impact on screening at baseline • Recent infections may be IGRA negative at baseline • Equal rates of prevalent infection between groups will attenuate estimated VES • If 95% convert to IGRA+ within 6 weeks of infection then 3 month delay in counting endpoint (eg per-protocol analysis) will remove bias • Impact on attenuation of VES • High specificity is good news • Reversion phenomenon?

  20. True VE = 60% VE PP VE ITT • At 1 year: • VE PP ~ 58% • VE ITT ~ 44% • = 0.05 (annual incidence of infection) • = 2 weeks (95% convert within 6 weeks) • = expected time from infection to IGRA positivity • 3 mo vaccination window

  21. Discussion • Searching under the lamp post? • Potential contributions to search for immune correlates • Strategy for current or for next generation vaccines?

  22. End of Presentation

  23. P(q;τ) Distribution of Infectious Potential (q) per exposure Infectious Potential (q)

  24. Evaluating vaccine effects on TB infection rates in adolescent populations

More Related