As the pipeline fills with potential therapies for Alzheimer (AD) and other neurodegenerative diseases, the research community is paying increased attention to how those therapies will be evaluated in clinical trials. Biomarkers, which are sorely needed for both AD and Huntington disease (HD), emerged as a central theme at the fourth meeting on Neurodegenerative Diseases at the Cold Spring Harbor Laboratory from 30 November through 3 December. Not only can biomarkers sharpen the diagnosis, but, equally importantly, they can improve the efficiency of clinical trials by monitoring treatment response and, in the best case, provide surrogate endpoints of the disease.
Since these diseases progress slowly, monitoring treatment response simply by assessing symptoms is too slow and requires too many subjects to allow an efficient trial. Moreover, by the time symptoms appear, much of the damage has already been done. In HD, for example, approximately half of the striatum has degenerated before the onset of symptoms, said Elizabeth Aylward from the University of Washington, Seattle. Aylward used magnetic resonance imaging (MRI) to track striatal volume in people who carry the HD gene. She claimed that this measure fulfills all the requirements of a biomarker: it can be objectively and reliably measured, it changes in a predictable manner over time, it predicts a known endpoint (onset of symptoms), and is associated with a known mechanism of pathology (neurodegeneration).
Aylward plotted volumes of the caudate and putamen, the two areas of the striatum, against estimated years to onset of disease. Years to onset was calculated using a formula based on the number of CAG repeats and the age of onset of the affected parent. The HD mutation represents an expansion of the trinucleotide cytosine-adenine-guanine (CAG) in the huntingtin gene. Having more than 39 CAG repeats virtually guarantees that the person will get Huntington’s, while fewer than 27 means (s)he will not. Aylward’s studies showed that the changes in putamen and caudate volume were linear and predictable: over a period of 2 years, the caudate shrank by some 4.2 percent, and the putamen by 5.6 percent. In addition, Aylward showed that the rate of change becomes significant about 10-12 years before onset. Subjects with caudate volumes smaller than 4.6 cc were symptomatic, while those with volumes greater than 5.3 cc were not. Control subjects had a caudate volume of 9.8 cc. In Aylward’s hands, caudate volume predicted which patients would be diagnosed with HD within 2 years with 100 percent accuracy (Aylward et al., 2004).
In designing clinical trials, MRI volumetric measures could serve to enrich for people who would be expected to develop symptoms during the course of the trial. This would allow onset to be used as a primary outcome measure, said Aylward. As the MRI studies show, however, significant striatal degeneration has already occurred by the time symptoms appear, suggesting that drugs to prevent neuron loss must be given sooner. Even more valuable would be to use striatal volume as a surrogate endpoint that would herald a clinical benefit during a treatment trial in presymptomatic patients. “We don’t have a treatment, so we can’t demonstrate this yet,” said Aylward. For the meantime, she recommended that MRI striatal volume be used provisionally as a surrogate endpoint to screen candidate treatments for full clinical trials that would use clinical outcome measures. The advantages, she said, are that striatal volume can be used in subjects who are many years away from onset, that its longitudinal use generates no practice or placebo effects, and that it could help flag the efficacy of a treatment even in small sample sizes.
While monitoring changes in the striatum may be particularly useful during the presymptomatic stages of HD, Herminia Diana Rosas from Massachusetts General Hospital said that cortical changes during the early symptomatic stages may help explain why, despite more than 50 percent striatal loss at the time of diagnosis, clinical symptoms continue to progress as further loss occurs and spreads. Data from 33 early symptomatic HD patients demonstrated significant thinning across the sensorimotor cortex early in the disease, which appeared to extend to other areas of the cortex in a regionally specific and temporally defined manner (Rosas et al., 2005). Rosas has investigated whether cortical thinning might serve as a surrogate marker in a pilot study of a neuroprotective agent in patients with HD. Earlier studies in HD transgenic mice had shown that the drug slows progression. In the human trial, after six months, patients showed a statistically significant slowing of the rate of change of cortical thinning in several regions; data on clinical symptoms are too mature for publication at this point, Rosas said. While further validation of these results is needed, Rosas concluded that MRI measures of cortical degeneration may provide a surrogate endpoint, and thus may improve the efficiency of clinical trials. Moreover, these methods may serve to clarify the role of the cortex in the pathogenic process.
Because a definitive genetic test is available for HD, and all HD is caused by huntingtin expansions, biomarkers are not needed for diagnosis. With AD the situation is more complex, according to Chester Mathis from the University of Pittsburgh, Pennsylvania. Although many experienced clinicians at Alzheimer’s Disease Centers (ADCs) report being able to diagnose the disease with up to 95 percent accuracy, neurologists and psychiatrists outside of specialized centers have a higher error rate. Moreover, at ADCs only 20 percent of patients do not have confirmed AD, said Mathis, while the proportion of people without AD likely is much higher at other practices. The specificity of diagnosing non-AD dementia cases hovers around 55 percent. There are no clear measures to predict whether, or when, someone with mild cognitive impairment (MCI) will progress to AD (10-15 percent convert to AD per year, while 40 percent prove to have some other cause). In light of all this uncertainty, an objective marker that identifies AD would help identify appropriate subjects for clinical trials, and track their response.
Mathis, William Klunk, and colleagues have developed a positron emission tomography (PET) imaging tracer known as Pittsburgh Compound B (PIB, N-methyl-[(11)C]2-(4'-methylaminophenyl)-6-hydroxybenzothiazole). This chemical binds selectively to Aβ plaques in vivo, allowing identification and quantification of the pattern of amyloid deposition in the brain. Control brain does not retain PIB, but AD brain does so selectively in the temporal, parietal, anterior cingulate, posterior cingulate/precuneus and frontal cortices, and striatum (Klunk et al., 2004). Colleagues at Washington University in St. Louis, Missouri, showed that one in 10 of nondemented control subjects, especially older people, had PIB binding that resembled the pattern seen in AD brains (Mintun et al., 2006). These individuals will be monitored over time to see whether they go on to develop dementia.
Mathis also reported on studies in people with MCI (Lopresti et al., 2005). About 60 percent of them had amyloid deposits suggestive of AD; the rest did not. The Pittsburgh group suspects that these are the 60 percent who will go on to develop AD; however, only continued follow-up can confirm whether these patients have AD. One big question in terms of clinical trials is whether PIB binding patterns change during treatment. The Pittsburgh group has initial data suggesting as much. Postmortem cortex from a subject in the AN-1792 anti-Aβ trial had shown plaque clearance (Masliah et al., 2005) and the same tissue bound as little PIB as does cortex from control. For upcoming trials, Mathis said that PIB should be able to reliably detect regional differences of 10 percent or greater, and that correlations will be made with other measures, including cognitive, volumetric, and biochemical markers such as CSF Aβ and tau.
Currently, about 20 sites worldwide are studying PIB binding in normal aging, familial AD, and Down syndrome, and some anti-amyloid clinical trials are evaluating it as a surrogate marker of efficacy. Christopher Rowe and colleagues from Austin Hospital in Melbourne, Australia, used PIB to examine Aβ deposition in healthy people, as well as in those with AD, dementia with Lewy bodies (DLB), frontotemporal dementia (FTD), and MCI. Concurrently, they performed [18-F] Fluorodeoxyglucose (FDG)-PET, MRI, neuropsychological evaluations, and ApoE4 testing. Rowe’s data indicate that PIB binding is strongly elevated in AD, less so in DLB, and not at all in FTD. Among MCI patients, 64 percent had elevated PIB binding similar to AD subjects. The amount of PIB binding correlated with cognitive status in people with MCI but not in AD or DLB patients. This might be because the cognitive status of AD and DLB patients declines with time, but the amyloid load has reached a plateau before symptom onset, so PIB binding remains more or less stable during the clinical phase. PIB binding was higher in people with the ApoE4 allele.
Comparing FDG-PET with PIB-PET for detection of AD, Rowe found that PIB was more accurate, specific, and sensitive than FDG-PET, particularly among older subjects. FDG-PET, however, may be useful for unusually early detection and tracking of AD, said Eric Reiman from the Banner Alzheimer’s Institute in Phoenix, Arizona. He presented a series of studies showing decline in cerebral glucose metabolism, as assessed by FDG-PET, in cognitively normal ApoE4 carriers (Reiman et al., 2005). Reiman suggested that FDG-PET could measure a presymptomatic quantitative endophenotype to evaluate putative modifiers of AD risk. In this manner, FDG-PET could assess the effectiveness of promising prevention therapies in a select group of higher-risk subjects over a shorter period of time, as compared to studying thousands of healthy volunteers and waiting many years to determine how many of the volunteers develop symptoms, and when. While PET and MRI measures were the hottest topics at the meeting, scientists also proffered a number of other measures as possible biomarkers. Peter Snyder of the University of Connecticut, Storrs, discussed using measures of voice acoustics in patients with Parkinson disease (PD). Paul Maruff from the University of Melbourne presented data on an assessment of cognitive decline in AD patients that measures the time it takes a person to complete a simple reaction time task. While a single measure would not be meaningful, said Maruff, multiple assessments over time show 100 percent specificity and sensitivity for AD. In another novel biomarker approach, Holly Soares, from Pfizer Global Research and Development, in Groton, Connecticut, presented data from a clinical trial of atorvastatin for AD, in which investigators used a multiplex panel that included 78 cardiovascular and inflammatory endpoints, as well as Aβ peptide levels, to evaluate the effect of the treatment. The study not only collected additional data regarding possible biomarkers for AD, but also provided a window to ask other questions about the pathogenesis of AD and the mechanism of action of statin drugs.
PET, MRI, biochemical markers, and clinical and neuropsychological assessments are all being evaluated as part of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), as was discussed by Clifford Jack, from the Mayo Clinic and Foundation in Rochester, Minnesota, and Leslie Shaw, from the University of Pennsylvania in Philadelphia. The biggest impact of biomarkers will come in conjunction with innovative clinical trial design, said Michael Krams of Wyeth Research. In a provocative talk, Krams presented a new way of designing clinical trials for disease-modifying AD drugs. The gist of Krams’s presentation was the idea that trial design must adapt to new information, including biomarker data, that emerges during the course of a trial. Consequently, the trial should adjust the sampling and allocation of subjects to different arms as it progresses. This kind of design would facilitate the implementation of seamless phase II/III trials that would require fewer subjects and take less time. In Phase I/II trials, Krams suggested scrapping traditional dose escalation studies in favor of continuous reassessments of subjects given increasing doses over time. Computer analysis of outcome data captured at different time points would be incorporated into the decision making process to determine whether a study should be continued as is, stopped, or tweaked. John Trojanowski, of the University of Pennsylvania, said this kind of innovation is desperately needed in the evaluation of treatments for AD. “We need a revolution in clinical trials for AD,” he said. “This is a great start.”—Lisa J. Bain.
Lisa Bain is a freelance science writer based in Philadelphia.
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