Uses of Biomarkers and Surrogate Endpoints

TABLE 17.4 Some Applications of Biomaikeis and Surrogate Endpoints 

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The use of biomarkers and surrogate endpoints in patients is well established in virtually all therapeutic areas. After all, blood pressure has been used as a surrogate for cardiovascular risk for many decades. Some other examples are given in Table 4.5. 

Lesko LJ, Atkinson AJJ. Use of biomarkers and surrogate endpoints in drug development and regulatory decision making criteria, validation, strategies. Annu Rev Pharmacol Toxicol 2001 41 347-66. 

Examples of some commonly used biomarkers and surrogate endpoints are listed along with clinical endpoints for several therapeutic classes in Table 17.1. 

The limitations of the use of biomarkers in healthy volunteers must be recognised. For example, although there have been attempts to simulate migraine headache in volunteers, to date none of these models can be considered adequate to serve as a surrogate endpoint. Patients with migraine are not difficult to recruit and are usually healthy apart from their migraine. In this case, it maybe more appropriate to establish tolerability and pharmacokinetics in healthy volunteers and then to select a maximum well-tolerated dose with which to perform a small proof of principle clinical trial in patients. This will need to be followed by larger trials to establish the dose-response relationship. 

In Phase 2 the proof of concept study provides scientifically sound evidence supporting the postulated effect of the new drug, where the effect may be the relevant pharmacological action or a change in disease biomarkers, established surrogate endpoints, or clinical outcomes that may be beneficial and/or toxic in nature. The proof of concept is often used for go/no-go decisions and is therefore one of the most critical steps in the drug development process. 

The biomarker is not used because no synthetic analysis has been done. The data need to be pooled, synthesized, and analyzed. We have to xmderstand what the data are telling us about that biomarker and what the remaining gaps in understanding are. Studies have to be identified that will fill those gaps, and then somebody has to do that work, whatever it is and for a surrogate endpoint, of course, that work involves correlation with clinical outcomes. 

There are many examples of biomarkers, which have been used as surrogates in prominent clinical trials that have been subsequently formd to be inadequate, illustrating the difficulty in identifying a surrogate endpoint. One notable scenario is that of a biomarker that responds to therapy and is highly predictive of survival, but does not predict the effect of treatment on survival. The use of CD4-I- counts in HIV trials is an example of such a biomarker. ... 

Much of the discussion about disease biomarkers is in the context of markers that measure some aspect of disease status, extent, or activity. Such biomarkers are often proposed for use in early detection of disease or as a surrogate endpoint for evaluating prevention or therapeutic interventions. The validation of such biomarkers is difficult for a variety of reasons, but particularly because the molecular pathogenesis of many diseases is incompletely understood, and hence it is not possible to establish the biological relevance of a measure of disease status. 

To explain the framework for statistical surrogacy (see Figure 17.1), dehne Z as the treatment, S as the biomarker, and T as the true chnical endpoint The effect of the treatment (Z) on the biomarker (S) is called or, the effect of the treatment on the clinical endpoint (T) is called and the effect of the biomarker (5) on the clinical endpoint (T) is called 7(10). Statistically speaking, the biomarker can only be used as a surrogate endpoint if an estimated treatment effect on S (a 5 0) can be used to predict a treatment effect on T 0) and if no treatment effect on S (a= 0) predicts no treatment effect on T (P= 0) with sufficient accuracy (10). 

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