Ask any Alzheimer disease researcher about the most pressing need in the field today and many will cite biomarkers. A consensus surrogate marker that predicts disease and responds to treatment tops investigators’ priority lists. Indeed, such a marker would be an invaluable tool for obtaining the ultimate prize, the mechanism-based therapeutic that is more effective than current treatments. But scratch the surface a bit more, and you’ll quickly discover that scientists’ notions of that seemingly uniform thing, an AD biomarker, can diverge quite widely. While academic and industry researchers easily agree on what they ultimately want—a practicable, robust, noninvasive, inexpensive readout that enables early diagnosis and indicates if a therapy is working—it is also true that the word "biomarker" can mean different things to different people. In particular, there is a great need for mutual exchange on how best to employ translational research and animal models toward the development of the shared goal.
Last November, a satellite workshop to the 35th Annual Conference of the Society for Neuroscience in Washington, DC, drew scientists from academia, the biotechnology, and the pharmaceutical industry for a day of talks and discussion. Entitled “Translational Biomarkers in AD Drug Discovery: From Animal Models to the Clinic,” the workshop was free of charge and sponsored by the Alzheimer Research Consortium (see ARF related news story). This public-private initiative supports the development of new research models that mimic features of AD and requires that the models be made freely available to investigators in academia and industry. (View/download a .pdf version of all five parts of this news story.)
Patrick May of Eli Lilly and Company in Indianapolis and Lennart Mucke of the Gladstone Institute of Neurological Disease in San Francisco assembled a program of six speakers, each from academia and industry. They defined the terms, the problems, the opportunities in taking biomarkers from bench to bedside, and introduced the latest data in AD biomarker studies. The day began with a lesson on successes and failures from previous efforts in AD and other fields by Ivan Lieberburg of Elan Pharmaceuticals in South San Francisco. May described how his company used soluble Aβ as a biomarker all the way through a development program of γ-secretase inhibitors, and Peter Seubert, of Elan, did the same for the use of amyloid plaques as a biomarker in Elan/Wyeth’s joint immunotherapy program. Karen Ashe at the University of Minnesota, Minneapolis, Mucke, at University of California, San Francisco, and Greg Cole of the University of California, Los Angeles, each introduced new biomarker candidates related to cognitive function and to the biology of synapses that new academic research has uncovered. Henry VanBrocklin, also of UCSF, outlined the path to developing radiopharmaceutical probes so researchers can one day image changes in these new markers. Michael Greicius, of Stanford University in Palo Alto, California, described early work on a surprising new functional imaging opportunity based on measuring changes in a “stream of consciousness” network of brain activity. Bill Klunk related a cautionary tale about reliance on mouse models by showing that the Pittsburgh Compound B (PIB) imaging agent that beautifully displays amyloid deposits in human brains fails utterly in mice. Floyd Bloom of Neurome Inc. reinforced the utility of those models in other aspects of biomarker research. He illustrated how new techniques for high-throughput morphometrics can analyze biomarkers in mouse models in a more powerful way than did earlier approaches. Peter Davies, of Albert Einstein College of Medicine, The Bronx, New York, updated the audience on where diagnosis based on measuring CSF phospho-tau concentration stands to date after having been tested in thousands of samples, and Tony Wyss-Coray of Stanford University, Palo Alto, California, introduced a new blood test built on a proteomic fingerprint of AD-specific inflammation.
By day’s end, the one complaint heard was that the program deprived the audience of those refreshing catnaps that are part and parcel to surviving a day packed with 12 lectures and discussion; sleep was not an option because every talk was excellent.
This report first pulls together the main themes that emerged during the day, and then expands on them in summaries of each presentation. The workshop drove home the message that it is critical to distinguish clearly among types of biomarkers, and to work those distinctions into one’s study design. For example, a target-related biomarker differs from a disease-related, and again from a surrogate marker. A target-related biomarker represents a readout that changes close to the action of the drug at hand, that is, Aβ concentration in a test of a secretase inhibitor, or cytokine levels in tests of an anti-inflammatory drug. Disease-related biomarkers tend to change further downstream from the drug intervention; examples for this are cognitive readouts, or the measurement of tau changes in an Aβ immunotherapy trial.
On this issue, researchers agreed that it is important that drug trials measure defined target-related biomarkers. This assures researchers that the drug hits the intended target in the relevant tissue, and allows them to test hypothesis of whether that target truly plays an important role in the disease process. This is not always done. For example, trials of NSAIDs failed on disease-related endpoints that were far removed from the action of the drug without also determining whether the drug actually tamped down brain inflammation. For this reason, the trials left the field unable to learn whether inflammation remains a valid approach. In the area of target-related biomarkers, mouse models that are often criticized for mimicking only aspects of AD, such as models of amyloidosis, can be extremely useful, speakers agreed. In general, preclinical studies in animal models need to employ target-related biomarkers separately from disease-related endpoints so researchers can draw conclusions about the value of the target and the intervention.
A second take-home message was that candidate diagnostic biomarkers tend to bump up against a ceiling set by an imperfect clinical diagnosis. Their accuracy is judged against a clinical diagnosis that itself is prone to error. This raises the question of whether any biochemical or imaging diagnostic can ever reach 100 percent accuracy short of postmortem confirmation, and suggests that the field consider finding a consensus on what is good enough.
Thirdly, scientists agreed that it is important to use a diverse panel of animal models, not just one, in a given translational research program. Frequently in the history of drug development, translational studies in one model did not fully predict the human response, but integrated data from several models did. For example, Elan’s preclinical work on mice did not predict the inflammation seen in the phase 2 trial of their first-line active vaccine. The PIB amyloid marker does not work in APP-transgenic mice, and had the program hinged on mouse data, it might have ended at this stage. Using an array of different animal models today is a less onerous standard than even 5 years ago, in part because a variety of brain imaging techniques for mice, rats, and monkeys have come online in recent years.
Researchers also took away a distinction among the different uses of a given biomarker. A marker that is useful for predicting AD may fail at responding to treatment, and different markers may work for different stages of this decade-long disease process. Finally, researchers shared a sense that to get a better grip on the pathophysiology of AD, the field needs to move past classical markers of plaques/tangles and soluble Aβ/tau. For one, it must develop ways of quantifying a range of different incarnations of Aβ and tau; for another, it should begin to exploit their interacting proteins from across the emerging area of synaptic biology. Such an effort might finally yield functional readouts that are likely to be clinically relevant. —Gabrielle Strobel.
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- Translational Biomarkers in Alzheimer Disease Research, Part 3
- Translational Biomarkers in Alzheimer Disease Research, Part 4
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- Translational Biomarkers in Alzheimer Disease Research, Part 2