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Introduction - the need for biomarkers in AD
In 1999 the US National institute of Health convened a workshop to try and resolve some of the perceived ambiguity regarding the purpose and status of biomarkers. As a consequence a set of definitions was derived that have subsequently become widely adopted (1). A biomarker, according to these definitions is " a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacological responses to a therapeutic intervention". A biomarker might be used for diagnosis, or staging of disease or to follow disease progression or monitor response to therapy. A clinical endpoint is "a characteristic or variable that reflects how the patient feels, functions or survives." A surrogate endpoint on the other hand, is a sub-set of biomarkers that are intended to substitute for clinical end-points. "A surrogate end-point is expected to predict clinical benefit (or harm or lack of benefit or harm) based on epidemiologic, therapeutic, pathophysiologic or other scientific evidence". Biomarkers have been gradually adopted as surrogate endpoints in clinical trials in recent years, for example, HIV RNA and CD4 count being accepted as surrogate markers for trials of HIV/AIDS interventions.
In the Alzheimer's disease (AD) field there has been considerable progress in the search for a biomarker with promising markers being developed using a wide variety of neuroimaging techniques and biochemical changes in CSF, blood and other tissues and fluids. However progress in translating these findings to surrogate endpoints is in its infancy with no evidence yet that regulatory authorities are prepared to accept putative AD biomarkers as surrogate endpoints. Establishing that a biomarker is a true surrogate endpoint is challenging both statistically and practically. To be used as a surrogate endpoint the biomarker should ideally both predict clinical endpoints and capture effects of treatment on these endpoints. In other fields, such as HIV/AIDS, where surrogate endpoints have been accepted for licensing purposes then large numbers of trials were available for meta-analysis where the proportion of the treatment effect explained by the biomarker (such as CD4 counts) could be calculated (2). There are alternative statistical approaches to the evaluation of surrogacy (3,4) but whatever methods are used the practical challenges remain considerable as sample sizes are...