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?
• Overview of possible trial designs
• Discussion
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
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
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
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)
Epidemiologic Studies (2002-2009):
BCG / IGRA Association
Soyal, 2005
Turkey (979)
Hill, 2006
The Gambia (718)
Hill, 2007
Eisenhut, 2009
Lucas, 2010
Roy, 2012
The Gambia (207)
UK (199)
Australia (524)
Europe (1128)
Weak evidence (non-RCT,
mixed) for BCG reducing
rate of IGRA conversion
0.0
0.5
1.0
1.5
Odds Ratio (95% CI)
2.0
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)
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
ofexposure
infection
per
exposure
Prob ofProbability
infection given
with
infectious
potential q
0.8
p(q,tau)
τ = 0.95
τ = 0.50
τ = 0.10
0.4
P(q;τ)
0.0
τ = 0.01
0
5
10
15
20
25
Infectious
Potential
(q)
q (magnitude
of exposure)
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
0.8
p(q,tau)
ofexposure
infection
per
exposure
Prob ofProbability
infection given
with
infectious
potential q
0.0
0.4
P(q;τ)
5
10
15
20
25
0.4
Distribution of
Infectious
Potential
(q) per exposure
Infectious
Potential
(q)
q (magnitude
of exposure)
0.0
Density
0.8
0
0
5
10
15
20
25
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
Expected Infectious Potential q
log exposure magnitude
(log-scale)
-4 -3 -2 -1 0
Contour plot of values for τ
Scaled value
of tau: Annual
population
infection rate =
calibrated
to 5%/yr
incidence
Too little exposure to be
consistent with 5%/yr
-2
-1
0
Expected # exposure events per year
log(log-scale)
exposure intensity
1
1. There must be sufficient exposure to be consistent with assumed incidence
2. For increased rate and/or infectious potential of exposure, the perexposure probability of infection must decrease to remain consistent with
assumed incidence
Model for Vaccine Effect
(τ reduced by 60%)
0.8
0.4
0.0
p(q,tau)
Probability of infection per exposure
0
5
10
15
20
25
Now can compute qVE(magnitude
assumed
exposure model
S for under of
exposure)
and calibrated to relevant epidemiologic incidence rate
1 – infection rate for vaccinees / infection rate for controls
Contour plot of values for VES
calibrated to 5%/yr incidence
τ = 0.60
-4 -3 -2 -1 0
Expected Infectious Potential q
log exposure
(log-scale)magnitude
VE: Annual population infection rate = 5%; biological VE
Too little exposure to be
consistent with 5%/yr
-2
-1
0
Expected # exposure events per year
log(log-scale)
exposure intensity
1
Principle risk for attenuating biological effect is
rare exposure with overwhelming infectious potential
(rather than) a high rate of exposure over time
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
Required Total # Endpoints (split)
True VES
60%
Type 1 Error (1sided)
Power (1 – Type 2 Error)
0.80
0.90
0.025
47
61
0.05
37
49
0.10
28
37
0.20
19
28
Design with Type 1 and Type 2 errors balanced
Required Total # Endpoints
Probability of passing VES criterion,
Probability of selection
True VES for Each
Vaccine Candidate
15%, 60%
Type 1 Error
(1-sided)
Power (1 – Type 2 Error)
0.80
0.90
0.025
72
NA, 0.07, 0.80
0.19, 0.01, 0.80
95
NA, 0.08, 0.90
0.10, <0.01, 0.90
0.05
58
NA, 0.11, 0.80
0.19, 0.01, 0.80
78
NA, 0.13, 0.90
0.10, <0.01, 0.90
0.10
45
NA, 0.18, 0.81
0.19, 0.02, 0.79
61
NA, 0.19, 0.90
0.10, 0.01, 0.89
30
NA, 0.25, 0.80
0.18, 0.05, 0.77
48
NA, 0.31, 0.92
0.08, 0.02, 0.90
Prob of passing efficacy test
Prob of selected as best
0.20
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 perprotocol analysis) will remove bias
• Impact on attenuation of VES
– High specificity is good news
– Reversion phenomenon?
1.0
0.8
0.6
True VE = 60%
VE(t)
VE PP
0.4
VE ITT
0.0
0.2
At 1 year:
• VE PP ~ 58%
• VE ITT ~ 44%
0
1
2
3
Years from Enrollment (t)
4
 = 0.05 (annual incidence of infection)
 = 2 weeks (95% convert within 6 weeks)
= expected time from infection to IGRA positivity
3 mo vaccination window
5
Discussion
• Searching under the lamp post?
• Potential contributions to search for immune
correlates
• Strategy for current or for next generation
vaccines?
END OF PRESENTATION
0.8
p(q,tau)
Probability of infection per exposure
0.0
0.4
P(q;τ)
5
10
15
20
25
0.4
Distribution of
Infectious
Potential
(q) per exposure
Infectious
Potential
(q)
q (magnitude
of exposure)
0.0
Density
0.8
0
0
5
10
15
20
25
Evaluating vaccine effects on TB infection
rates in adolescent populations
Title: Evaluating vaccine effects on TB infection rates among adolescent populations
Abstract: Based on general historical perspectives, the potential for a vaccine to demonstrate efficacy is
maximized if vaccination occurs prior to the establishment of infection. Current plans to evaluate TB
vaccines in adolescent populations propose to enroll subjects without regard to infection status at
baseline. Yet only a small fraction of the total information about rates of the primary endpoint rates
(active TB disease) will come from the subcohort that was vaccinated while still free of infection. In this
talk, we consider the rationale for and feasibility of conducting small, efficient vaccine trials in adolescent
populations to assess the ability of vaccines to reduce the rates of TB infection. The biological plausibility
for such a vaccine effect will be discussed and a simple mathematical model relating exposure intensity
to infection is used to illustrate concepts. Relevant pre-clinical and epidemiologic data are also reviewed.
Specific trial design calculations are presented for both multi-arm down-selection and screening test-ofconcept objectives. The impact on these designs of imperfect assays used for enrollment eligibility and
for primary endpoint ascertainment is also considered. We propose such trials as a natural complement
to vaccine trials in infants with disease endpoints and as a prelude to larger trials in adolescents to
evaluate pre- and post-exposure vaccination on rates of active TB disease.