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Using Biomarkers in Vaccine Development and Evaluation

Using Biomarkers in Vaccine Development and Evaluation. Biostat 578A Lecture 10 Contributor: Steve Self. Immunological “Correlates of Protection”. Key concept in vaccine development/evaluation An immunologic measurement in response to vaccination that is “correlated with protection” Uses

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Using Biomarkers in Vaccine Development and Evaluation

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  1. Using Biomarkers in Vaccine Development and Evaluation Biostat 578A Lecture 10 Contributor: Steve Self

  2. Immunological “Correlates of Protection” • Key concept in vaccine development/evaluation • An immunologic measurement in response to vaccination that is “correlated with protection” • Uses • Guide for vaccine development • Bridging studies in vaccine production • Guide refinements of vaccine formulation • Basis for regulatory decisions • Guides for vaccination policy • Precise meaning often confused- needs clarification and new terminology

  3. Many Licensed Vaccines do not have a Known Correlate of Protection: List of FDA Licensed Vaccines (from FDA Website)

  4. Many Licensed Vaccines do not have a Known Correlate of Protection: List of FDA Licensed Vaccines (from FDA Website)

  5. Many Licensed Vaccines do not have a Known Correlate of Protection: List of FDA Licensed Vaccines (from FDA Website)

  6. Many Licensed Vaccines do not have a Known Correlate of Protection: List of FDA Licensed Vaccines (from FDA Website)

  7. Summary of Licensed Vaccines and Correlates of Protection • The immune responses responsible for protection of most licensed vaccines are unknown • Correlates known: 5 vaccine types • Correlates partially known: 7 vaccine types • Correlates unknown: 9 vaccine types • Only antibody responses have been identified as correlates of protection • For many licensed vaccines T cell responses are suspected to play a role in protection, but T cells have not yet been documented as correlates of protection

  8. Utility of Biomarkers: Prediction • Correlates are useful only to the extent that they build bridges… predicting effects in a new setting based on effects observed in another setting • Different types and sizes of bridges: • Across vaccine lots, across different vaccine formulations, across human populations, across viral populations, across species • One correlate can be useful in building one type of bridge but not another • Propose using the term predictor of protection (POP) to clarify and specify two essential elements: • What measurement(s) are used as basis for prediction? • What target for prediction? • Need typology for empirical basis of prediction

  9. “Surrogates of Protection” (SOPs) vs Correlates of Risk (CORs) • Correlates of risk: • Individual-level predictors of risk • Estimable from cohort, nested case-control or nested case-cohort) studies of different types of individuals • CORs among vaccinees • CORs among non-vaccinees • Natural history studies (general high-risk cohorts, highly exposed seronegative cohorts) • Control groups in randomized vaccine trials • Surrogates of protection: • Individual- or group-level predictors of vaccine efficacy (i.e., individual- or group-level surrogate endpoints) • An immune response identified to be a COR may be studied further to see if it is also a SOP and/or a POP

  10. How Find a COR? • Examine immune responses of individuals who recover naturally from disease • Traditional approach to vaccine development • Immune responses preferentially present in those who recover are CORs • In HIV, very few individuals naturally recover • The Center for HIV/AIDS Vaccine Immunology (CHAVI) is initiating a large study of Highly Exposed Seronegatives to identify CORs • Animal challenge models • Challenge animals with a pathogen • Just prior to challenge, measure the immune response to vaccination • Compare immune response levels in protected and unprotected animals • The Gates Foundation may be funding large monkey challenge studies to facilitate “discovery” of CORs

  11. Direct Assessment of a POP by Meta- Analysis • N pairs of immunologic and clinical endpoint assessments among vaccinees and non-vaccinees • Pairs chosen to reflect specific target of prediction • Examples • 1. Predict efficacy of vaccine to new viral strain: N strain-specific assessments of immunogenicity and efficacy • 2. Predict efficacy of new vaccine formulation: N vaccine efficacy trials of “comparable vaccines but with different formulations” • Plot of vaccinee/non-vaccinee contrast in endpoint rates (VE) vs contrast in immunologic response • Prediction for target based on observed immunologic response • Prediction error read directly from scatter in plot • Data intensive approach; often infeasible

  12. Schematic Example 1. Plot of Estimated VEs(s) versus Mean Difference in Antibody Titers to Strain s [10 strains s]; Large Phase III Trial This result would support that strain- specific antibody titer is a fairly reliable POP for predicting vaccine efficacy against new viral strains

  13. Indirect Assessment of POPs:From CORs to SOPs to POPs • Data for direct assessment of POPs are rarely available but CORs can often be identified (e.g., Vax004) • Two indirect strategies for assessing a COR as a SOP/POP • Prentice (1989) criterion for a “statistical surrogate” endpoint: • COR to SOP: Can an individual-level regression model for risk be identified that is 1) consistent across vaccinated and unvaccinated individuals and 2) fully explains differences in risk between vaccinees and non-vaccinees? • SOP to POP: Can an individual-level regression model with the properties described above be used as the basis for prediction of protective effects in novel settings? • Frangakis and Rubin (2002) criterion for a “principal surrogate” endpoint: • COR to SOP: Do causal vaccine effects on the immune response predict causal vaccine effects on risk? [addressed further in Lecture 12] • SOP to POP: Can the estimated “causal effect predictiveness” of the immune response be used as the basis for prediction of protective effects in novel settings?

  14. Some Examples using the “Prentice Criterion” Framework • From CORs to SOPs: • Influenza vaccine: Strain-specific Ab titer and risk of clinical infection • rgp120 HIV-1 vaccine (Vax004): Binding Ab titers and risk of infection • From SOPs to POPs: • Influenza vaccine: Strain-specific Ab titer and strain-specific VEs

  15. 1943 Influenza Vaccine Field Trial (Salk, Menke, and Francis) • Study subjects • 1,776 men in 3651st Service Unit of ASTP at the University of Michigan) • Age 18-47 • Housed (mainly) in dormitories and fraternities • Dined in 3 mess halls • Common daily activities

  16. 1943 Influenza Vaccine Field Trial(Salk, Menke, and Francis) • Treatment • Trivalent vaccine w/ components Weiss Strain A, PR8 Strain A, Lee Strain B • Placebo control • Treatment assignment and delivery: • Men arranged alphabetically • Alternate individuals inoculated with 1 ml of vaccine/placebo subcutaneously • Subjects blinded to assignment • All inoculations completed over 7 day period (Oct 25-Nov 2)

  17. 1943 Influenza Vaccine Field Trial(Salk, Menke, and Francis) • Follow-up and serologic assessments • Blood for serology at vaccination, + 2 weeks and at end of study for sample of participants • Every 10th vaccinee and every 5th placebo recipient included in sample (approx 10% and 20% of study cohort, respectively) • 35 participants lost to follow-up (19 controls, 16 vaccinees) for retention rate of 98%

  18. 1943 Influenza Vaccine Field Trial • Clinical Endpoints • Daily “sick call”, clinic and hospital-based surveillance • Multiple throat washes for viral culture • Blood samples

  19. Results • Weiss Strain A • Case incidence • Controls: 8.45 / 100 • Vaccinees: 2.25 / 100 • Estimated VEs = 73% • PR8 Strain A • Case incidence • Controls: 8.22 / 100 • Vaccinees: 2.25 / 100 • Estimated VEs = 73%

  20. Strain-specific Ab Titer:COR? Also a SOP? • COR models • Estimate relationship between Ab titer and risk within control group (COR among non-vaccinees) • Estimate relationship between Ab titer and risk within vaccine group (COR among vaccinees) • Assess consistency between two COR models • Ab titer as SOP? • Compute predicted efficacy based on • Observed effect of vaccination on Ab titer • COR model among non-vaccinees (w/ extrapolation) • Observed risk in control group • Compare predicted VEs with observed VEs

  21. Estimated Incidence as a Function of Log Antibody Titer (from logistic regression) Observed Risk Expected Risk

  22. Logistic Regression Models:Estimated Coefficients (SE) Weiss Strain A Control Gp Only Control and Vaccine Gps Model 1 Model 2 Model 3 Model 4 Intercept 1.80 (0.54) -2.38 (0.12) 1.62 (0.45) 1.80 (0.54) log(Titer) -1.03 (0.14) - -0.98 (0.12) -1.03 (0.14) Tmt - -1.39 (0.25) 0.33 (0.32) -0.43 (1.28) Tmt*log(Titer) - - - 0.16 (0.25)

  23. Model-Fit is good, based on Observed and Expected Incidence

  24. Estimated and Predicted VEs:Weiss Strain A • Direct estimates of VEs (w/o use of Ab titer) • Est-VEsCrude = 73% • Predicted VEs • Based on [Risk | Ab, Controls] plus [Ab | Vaccine] • Pred-VEs = 82% • “Prentice Criterion” for a surrogate endpoint • Vaccine effect on surrogate completely explains effect on clinical endpoint • Log(Ab titer) satisfies criterion as a surrogate of protection

  25. Estimated Incidence as a Function of Log Antibody Titer, Weiss & PR8 Strains A

  26. Logistic Regression Models:Estimated Coefficients (SE) PR8 Strain A Control Gp Only Control and Vaccine Gps Model 1 Model 2 Model 3 Model 4 Intercept -1.37 (0.59) -2.41 (0.12) -1.27 (0.53) -1.37 (0.59) log(Titer) -0.27 (0.15) - -0.29 (0.14) -0.27 (0.15) Tmt - -1.36 (0.26) -0.89 (0.34) -0.22 (1.79) Tmt*log(Titer) - - - -0.13 (0.34)

  27. Estimated and Predicted VE:PR8 Strain A • Direct estimate of VEs (w/o use of Ab titer) • Est-VEsCrude = 73% • Predicted VE • Based on [Risk | Ab, Controls] plus [Ab | Vaccine] • Pred-VEs = 33% • “Prentice Criterion” for a surrogate endpoint • Log(Ab titer) does not satisfy criterion as a surrogate of protection • Only ½ of overall protective effect is predicted from effect on Ab titer

  28. Discussion • Protection from PR8 Strain A only partly described by PR8 Ab titer • A (Prentice) surrogate of protection will have: • The same association between immune response and risk in vaccinees and in non-vaccinees • Consistency of the within-group association and the between-group association (VEs)

  29. Weiss Strain A Control Risk Vaccine Ab Titer

  30. PR8 Strain A Control Explained by COR model Risk Not explained by COR model Vaccine Ab Titer

  31. Discussion • Protection from PR8 Strain A only partly described by PR8 Ab titer • A possible explanation is that antibodies are protective, but the measurements reflect something else besides protective responses (i.e., measurement error) • Measurement error attenuates within-group association • Q. How to accommodate measurement errors in assessment of COR as a SOP?

  32. PR8 Strain A Control De-attenuated COR models to accommodate measurement error; Adjusted model consistent w/ SOP Risk Vaccine Ab Titer

  33. Discussion • Protection from PR8 Strain A only partly described by PR8 Ab titer • Another possible explanation is that there are other protective immune responses that were not measured • E.g., cell-mediated immune responses • Another possible explanation is that PR8 Strain A has different protective determinants than Weiss Strain A

  34. POP for Strain-specific VEs:Direct Assessment • Strain-specific Ab titer as a POP for emerging viral strains? • Basis of prediction from SMF study • N = 2 (2 pairs of strain-specific Ab responses and estimated VEs) • Plot observed strain-specific VEs vs • D mean Ab titer (Vaccine vs Control) • Predicted VE based on Ab titer distributions (Vaccine vs Control) and COR model among non-vaccinees

  35. Assessing ability to predict VEs across viral strains 100 80 PR8 Weiss 60 Observed VEs 40 20 0 0 20 40 60 80 100 Predicted VEs Prediction interval of efficacy for new viral strain?? P-VE for emergent viral strain

  36. Problems with Prentice Framework • COR models in non-vaccinees may not be estimable • If the COR is “response to vaccine” then cohort study relating COR to risk in non-vaccinees is impossible • If no variation in putative COR among non-vaccinees • In these cases the causal inference approach (based on Frangakis and Rubin) may be more useful • Statistical surrogates (satisfying the Prentice criteria for a surrogate endpoint) are based on net effects, not causal effects, implying this criterion may mislead • See Frangakis and Rubin (2002)

  37. Introduction to Causal Inference Approach from CORs to CSOPs (Expanded on in Lecture 12) • In the causal inference paradigm, causal vaccine efficacy is based on comparing risk within the same individual if he/she were assigned vaccine versus if assigned control • A difference within the same individual is directly attributable to vaccine, and thus is a causal effect • A CSOP, i.e., a “Causal Surrogate of Protection”, is defined in this framework (defined below)

  38. Causal Inference Approach from CORs to CSOPs • VEcausal = 1 – Pr[Y(1) = 1]/Pr[Y(0)=1] • Y(1) = indicator of outcome if assigned vaccine • Y(0) = indicator of outcome if assigned placebo • Interpretation of VEcausal: Percent reduction in risk for a subject assigned vaccine versus assigned control • In randomized, blinded trial, VEcausal can be estimated by comparing event rates in vaccine and control groups

  39. Causal Inference Approach: From CORs to CSOPs • Approach to assessing whether a COR is a CSOP: Study how causal vaccine efficacy varies over groups defined by fixed values of both the immune response if assigned vaccine, X(1), and the immune response if assigned control, X(0) • VEcausal(x1,x0) = 1- Pr[Y(1)=1|X(1)=x1,X(0)=x0] Pr[Y(0)=1|X(1)=x1,X(0)=x0] • Compares risk for the same individual who would have immune responses x1 under vaccine and x0 under control

  40. Simplification of Causal Vaccine Efficacy Parameter • For many immunological measurements, X(0) is constant (e.g., ~0) for all subjects, because placebo does not induce responses • Causal VE can be rewritten as VEcausal(x1,x0=c) = VEcausal(x1) = 1-Pr[Y(1)=1|X(1)=x1]/Pr[Y(0)=1|X(1)=x1] Simplified interpretation: Percent reduction in risk for a vaccinated individual with response x1 compared to if he/she had not been vaccinated • E.g., VEcausal(x1=high response) = 0.5: an individual with high immune response to vaccine has halved risk compared to if he/she had not been vaccinated

  41. Interpretation of VEcausal(x1) • VEcausal(0)=0 implies the immune response is causally necessary as defined by Frangakis and Rubin (FR) (2002): the vaccine can only have efficacy in a person if it stimulates x1 > 0 • VEcausal(x1) increasing with x1 implies a higher immune response to vaccine directly causes lower risk- implies a COR is a CSOP • Motivates terminology “Causal Surrogate of Protection” (CSOP) • The slope of increase of VEcausal(x1) with x1 measures the strength of the causal correlation of x1 with protection • This slope is a measure of the associative effect in the terminology of FR • VEcausal(x1) constant in x1 implies that this immune response has no causal effect on risk, i.e., x1 is a COR but not a CSOP

  42. Interpretation of VEcausal(x1) • Note that there must be some protection in order for a COR to be a CSOP • VEcausal = 0 and no enhancement of risk at any immune response level implies VEcausal(x1) = 0 for all x1- not a CSOP • “Causal surrogate of protection” is only meaningful when there is some protection (VEcausal > 0)!

  43. Fundamental Problem of Causal Inference Approach • In controls, X(1) is not measured- it is the immune response he/she would have had had he/she been vaccinated • To estimate VEcausal(x1) a technique is needed for predicting the X(1)’s of controls • Approaches suggested by Dean Follmann (Covered in Lecture 12) • Exploit correlations of X(1) with subject-specific characteristics measured in both vaccinees and controls • Immunological measurements • Immune response to a non-HIV vaccine or blank-vector • Closeout vaccination of uninfected control subjects • Assume the (unmeasured) X(1) during the trial equals the immune response Xc measured after the trial

  44. Causal Inference Approach • This approach most useful when: • The range of immune responses in controls is very narrow [e.g., X(0) ~ zero for the VaxGen trials], which simplifies VEcausal(x1) to vary only in x1 • Limited variability of X(0) in controls makes difficult assessing whether a COR is a SOP within the Prentice framework

  45. Causal Inference Approach: VaxGen Illustration [U.S. Trial] • ? is the risk for a placebo recipient with Qk quartile antibody response that he/she would have had had he/she been vaccinated Risk of Infection by Antibody Quartile

  46. Causal Inference Approach: VaxGen Illustration • Idea: Control/adjust for the antibody response if assigned vaccine • Decreasing relative risks (vaccine/placebo) with increasing antibody levels implies a CSOP- some causal effect • Constant relative risks (vaccine/placebo) with increasing antibody levels implies not a CSOP- no causal effect

  47. VaxGen Illustration: Example 1 [COR is a CSOP] • A CSOP- a higher vaccine-induced antibody response directly causes a lower risk of infection (relative risks 1, 0.56, 0.56, 0.44)

  48. VaxGen Illustration: Example 2 [COR Not a CSOP] • Not a CSOP- the level of vaccine-induced antibody response does not causally effect the risk of infection (relative risks 0.5, 0.5, 0.5, 0.5)

  49. VaxGen Illustration • Estimates for Example 1: • VEcausal(Q1) = 1 – 0.18/0.18 = 0 • VEcausal(Q2) = 1 – 0.10/0.18 = 0.44 • VEcausal(Q3) = 1 – 0.10/0.18 = 0.44 • VEcausal(Q4) = 1 – 0.08/0.18 = 0.56 VEcausal(x1) increasing in antibody quartile implies a CSOP • Estimates for Example 2: • VEcausal(Q1) = 1 – 0.18/0.36 = 0.5 • VEcausal(Q2) = 1 – 0.10/0.20 = 0.5 • VEcausal(Q3) = 1 – 0.10/0.20 = 0.5 • VEcausal(Q4) = 1 – 0.08/0.16 = 0.5 VEcausal(x1) constant in antibody quartile implies not a CSOP

  50. Illustration with 1943 Influenza Trial [Much Variation in X(0)] • Imputation of X(1) (= log ab titer) for controls • Assume any two control subjects with log ab titers X1(0) < X2(0) have X1(1) < X2(1); i.e., a higher response for a control subject implies a higher response had he/she received vaccine • This equipercentile assumption is X(1) = Fv-1(Fc(X(0))) • Fv = empirical distribution of log ab titer in vaccine group • Fc = empirical distribution of log ab titer in control group • This assumption allows construction of a complete dataset of {X(1),X(0)} for all trial participants

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