Biomarkers as Endpoints in Clinical Research December 6, 2005 Victor De Gruttola Some slides and ideas provided by Thomas Fleming

Biomarkers as Endpoints Criteria for Study Endpoints Correlates and Surrogates Validation of Surrogates

Criteria for Study Endpoints in Clinical Trials • Measurable/Interpretable • Sensitive • Clinically relevant ~ Prevention of Disease Related Symptoms ~ Prolongation of Survival

Use of Surrogate Endpoints Treatment Effects on Surrogate Endpoints eg: ~ Oncology: Tumor Burden Outcomes ~ HIV/AIDS: CD4, Viral Load ~ Cardiovascular Dis: B.P., Cholesterol ~ Vaccines: Antibody titers • Establishes Biological Activity • But Not Necessarily Clinical Efficacy

Biomarkers as Endpoints Criteria for Study Endpoints Correlates and Surrogates Validation of Surrogates

Not in Causal Pathway of Disease Process Surrogate Endpoint: Not in Causal Pathway of Disease Process Surrogate True Clinical Endpoint Endpoint Causal Pathway Disease

Disease Biomarker Mother-to-Child e.g., CD4 Trans of HIV The Surrogate Endpoint is not in the Causal Pathway of the Disease Process. Biomarker Mother-to-Child e.g., CD4 Trans of HIV HIV Viral Load Biomarker Ca. Symptoms e.g., CEA, PSA & Death Tumor Burden “Correlates”: Useful for Disease Diagnosis, or Assessing Prognosis “Valid Surrogates”: Replacement Endpoints Disease

Intervention Disease Intervention Disease Multiple Pathways of the Disease Process Intervention Surrogate True Clinical Endpoint Endpoint Disease Intervention True Clinical Endpoint Disease Surrogate Endpoint

Intervention Disease Intervention Disease Multiple Pathways of the Disease Process Intervention Transient True Clinical Tumor Control Outcome Disease Intervention True Clinical Outcome Disease Tumor Shrinkage

Interventions having Mechanisms of Action Independent of the Disease Process Intervention Surrogate True Clinical Endpoint Endpoint Disease

Interventions having Mechanisms of Action Independent of the Disease Process Intervention Arrhythmia Overall Suppression Survival Disease

Time Intervention Disease Objective Dis Related Sx Response Rate & Death (Negative) Adverse treatment effects inducing significant M/M (Positive) Anti-tumor effects delaying long term tumor progression Disease

Biomarkers as Endpoints Criteria for Study Endpoints Correlates and Surrogates Validation of Surrogates

Validation of Surrogate Endpoints Property of a Valid Surrogate · Effect of the Intervention on the Clinical Endpoint is reliably predicted by the Effect of the Intervention on the Surrogate Endpoint

Prentice’s Sufficient Conditions 1. The surrogate endpoint must be correlated with the clinical outcome 2. The surrogate endpoint must fully capture the net effect of treatment on the clinical outcome

 (t | Z,S(t) ) = 0(t) eZ +  S(t) Z = 0 : Control ; Z = 1 : Treatment S(t) : Surrogate Endpoint at t (1)  (t | Z) = 0(t) eZ (2)  (t | Z,S(t) ) = 0(t) eZ +  S(t) Proportion of net treatment effect explained by the surrogate endpoint: DeGruttola et al, J Infectious Diseases 175:237-246, 1997  p = 1 - 

Analyses across different studies are required to explore the validity of surrogate endpoints

Validation of Surrogate Endpoints Statistical · Meta-analyses of clinical trials data Clinical · Comprehensive understanding of the ~ Causal pathways of the disease process ~ Intervention’s intended and unintended mechanisms of action

Assessment of Surrogates from across Multiple Studies STEP 1 Demonstrate that a proposed marker predicts clinical response (necessary but not sufficient for a surrogate). Determine whether the relationship between marker and clinical endpoint depends on specific treatment or is consistent across studies.

Estimated Hazard Ratio for Progression to AIDS/death by treatment in each trial (for each 1 log10 drop in HIV-1 RNA)

Estimated Hazard Ratio for Progression to AIDS/death by treatment in each trial (for each 33% increase in CD4)

Prediction of Endpoint Based on Marker STEP 2 Examine the ability of the marker to predict the clinical endpoint across studies.

Does Marker (HIV-1 RNA) Predict Clinical Benefit Across Studies?

Surrogate Endpoints Benefit of treatment partially, but not fully, mediated through viral load.   Other factors: viral fitness, resistance, toxicity, presence of HIV in compartments besides plasma.  For trials comparing highly potent therapies to suboptimal therapies, HIV-1 RNA response to therapy may predict clinical endpoint, but HIV-1 RNA is not a surrogate endpoint in all settings.

Formal Prediction Approach Combining Across Studies Goal: assessment of the reliability of predicting the effect of X on T from the effect of S on T, using information from a range of “similar” studies. Example: Meta-analysis of the relationship between Rx effect on change CD4 count from baseline and Rx benefit (Daniels and Hughes, SIM 1997).

Meta-analysis of Surrogates The impact of X on T and on S is assumed multivariate normal with mean and variance parameters that vary across studies. By "borrowing" information regarding estimates of the effects of X on T and on the relationships between T and S given X from previous studies, one predicts effects of a new treatment from data on S.

Surrogate Meta-analysis Association of log hazard ratio of AIDS or death and difference in mean change in CD4 cell count for trials that are placebo-controlled (p) and active-controlled (a). Ellipses are 95% CI’s.

Cross Protocol Analyses Markers are measured with error and relationship between marker and risk of disease may be complex and vary over time. Xu and Zeger (JRSSC, 2001) proposed a model for investigations across studies that also allowed the observed surrogate S to reflect only indirectly the true, unobservable, surrogate . Such models accommodate situations in which S is measured with error and involve other factors besides .

For study 1, For study 2, S1 S2 1 2 X1 X2 T1 T2 And in the mth study , Sm m Xm Tm

Hierarchy for Outcome Measures True Clinical Efficacy Measure Validated Surrogate Endpoint (Rare) Non-validated Surrogate Endpoint that is “reasonably likely to predict clinical benefit” Correlate that is solely a measure of Biological Activity …Fleming (2005), Health Affairs

Conclusions Validation of surrogate endpoints requires a considerable amount of information from a range of studies as well as understanding of disease mechanism. Surrogates are most likely to be useful when applied to new drugs that are from a class that has been extensively studied and is well understood.