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Alzheimer‘s diseaseTarget population and development of biomarkers
Harald Hampel
Department of Psychiatry
Trinity College Dublin &
University of Munich
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Open regulatory issues„AD is still an open research field“
• Which population do we study?
• How valid and reliable are biochemical markers?
• Focus on value regarding early characterisation, detection & prediction
• Potential role for enrichment of trial populations
• Current use as endpoints in proof of concept studies or confirmatory clinical trials
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Precsymptomatic and clinical continuum of AD
IPA Expert Conference on MCI - Gauthier et al. (2006) The Lancet; PCP: Braak und Braak (1991); SMI: Reisberg und Saeed (2004); MCI: Peterson und Morris (2005)
pre-clinical phase10-40 years
subjective cognitiveimpairment
15 yearsMCI
1-5 yearsAD
7 years
5 -15% / yrconversion to MCI
1SD Score undermemory tests in younger subjects
MCI-AD conversion rate:MCI 5-15 % / yr
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Alzheimer’s disease (AD)Target population I: (mild) - moderate – (severe) AD as reference
• Clinical diagnosis: dementia syndrome and criteria for severity (mild moderate, severe) are defined in DSM-IV-TR and in ICD-10 (F00-F03)
• Use of Screening test for degree of cogntive impairment (MMSE)
• Probablility assessment of AD: history, progressive course, exclusion of other diagnosable causes of dementia
• Subtype diagnosis can be further specified using NINCDS-ADRDA criteria
• Diagnostic criteria need revision and updating:• Sensitivity has been shown very good to excellent, specificity has been much
lower (optimised assessment and use of biomarkers)
• Revised criteria are being discussed in the APA DSM-V and WHO ICD-11 working groups
• Potential implementation of operationalised neurobiological criteria (using laboratory methods & neurochemical information) may aid to an earlier and more accurate characterisation of AD
Hampel et al. (2008) Alzheimer‘s & Dementia; Broich (2007) International Psychogeriatrics
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Alzheimer’s disease (AD)Target population II: early AD and prodromal stages
• Very early AD and prodromal stages
– MCI is proposed as a transitional stage to AD and a nosological entityin elderly patients with mild cognitive deficits
– Concept is in evolution and suffers limitations:
– Prevalence rates vary greatly depending on criteria used (high proportion returns to normal and up to 12%/a progress to dementia)
– MCI is not considered as a homogeneous clinical entity (role of subtypes such as aMCI and assessment tools need to be refined)
– Clinical research demonstrates that characterisation of an at riskpopulation such as aMCI and prediction of clinical AD may besubstantially supported by use of biochemical markers in the CSF & APOE genotyping
– recent evidence supporting characterisation of even earlierpresymptomatic at risk groups with CSF markers
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Biological markers in AD
• Biomarkers can play a critical role at all stages of the drugdiscovery / development process
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Development of biological markers
AD presents difficulties in distinct areas (phase II-III trials)
• diagnosis (early identification of homogenous populations whentreatment would have the greatest effect - fixed marker)
• classification (enhancing specificity)
• prognosis / prediction (in trials with decline and conversion to dementia as endpoint)
• progression (natural or pathological history)
• biological activity (mechanisms of action)
• surrogate (predicts clinical endpoints – dynamic marker)
NIH Biomarker Definitions Working Group (2001) Clin PharmacolHampel et al. (2008) in press
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Consensus Report (1998) Neurobiol Aging
Criteria of an ideal diagnostic biomarker of AD
• detects a fundamental feature of AD pathology
• is validated in neuropathologically confirmed cases
• sensitivity > 80 % (> 85 %)
• specificity > 80 % (> 75 %)
• reliable
• reproducible
• relatively inexpensive
• simple to perform
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1) Feasibility:
• validated assay
• properties including high precision & reliability
• reagents and standards well described
2) Core analyte:
• evidence of association with key mechanisms of pathology
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Development of a biomarker for ADe.g. p-tau (> 15 years so far)
Stage I
Stage II
Stage III
Description of neuropathology
Identification of NFT constituents
Detection of relevant p-tau epitopes
Development of antibodies
Assay development
Correlation to neuropathology
Investigation of selected patients and controls → sensitivity / specificity figures, cut-off
(diagnosis vs. healthy aging, differential diagnosis, early diagnosis)
Controlled diagnostic trials
Stage IV
Basic studies
Clinical studies (diagnostic validation)
Effectiveness studies
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Core feasible AD biochemical CSF marker candidates
prediction, enrichment, endpoint in trials on e.g. BACE1 inhibitors
BACE1 & APP isoforms, total Aβ
key marker for tau phosphorylationstate in trials, classification, prediction, enrichment
P-tau231 & P-tau181
key marker for intensity of neuronal & axonal degeneration in trials
Total Tau protein
key marker for Aβmetabolism
Aβ42
core feasible candidates function
Hampel et al. (in press)
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Candidate CSF biomarker for AD: Aβ42APP / Aβ metabolism ELISA for Aβ 1-42
Vanderstichele et al, 1998
β-sAPP
γ -secretase
SP KPI OX2
β-amyloid
β-secretase C99 CTF
3D6
21F12
β-amyloid 421
Mean decrease:50% of controls
Studies (n) 21AD cases 1163Controls 819Mean sens 88 %Mean spec 87 %
0
10
20
30
40
50
60
70
80
90
100
Genetic
sLu
minex ELISA - InnogeneticsAthena
Blennow & Hampel (2003) Lancet Neurology; Blennow updated (2006)
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Candidate CSF biomarker for AD: total tau
Blennow & Hampel (2003) Lancet Neurology; updated (2006)Hampel et al. (2008) Alzheimer’s & Dementia
Tau isoforms ELISA for total tau
N 352
N 381
N 410
N 383
N 412
N 441
HT7
AT120
BT2
Blennow et al, Mol Chem Neuropathol 1995;26:231Exon 2 3 10
0
10
20
30
40
50
60
70
80
90
100
Studies (n) 52AD cases 3255Controls 1955Mean sens 81 %Mean spec 90 %
Mean increase:320% of controls
ELISA - Innogenetics
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Candidate CSF biomarker for AD: phospho tau
Studies (n) 20AD cases 1214Controls 655Mean sens 81 %Mean spec 88 %
Mean increase:300% of controls
0102030405060708090
100
P-Ser 199 P-Thr 181 Thr 231Thr 181 +Thr 231
Ser 396 +Ser 404
Phospho tauFormation of tangles ? P-Thr231
Kohnken et al. (2000) Neurosci Lett
S S S S S
TTT T T T
S
T
S
T
S
T T
SS S S S S SS SS
T
SS SSS S
CP9
Tau1CP27
Blennow & Hampel (2003) Lancet Neurology; updated (2006)Hampel et al. (2008) Alzheimer’s & Dementia
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Comparative study: phosphorylated tau proteindiagnostic and classificatory accuracy [%] for groupcomparisons (ROC-analysis)
CACSpecSensCACSpecSensCACSpecSensAD vs.
888688898690959196OND
8110077889187979198HC
778372848087858586non-AD
p-tau 199 [fmol/ml]p-tau 181 [pM]p-tau 231 [pg/ml]
Hampel et al. (2004) Arch Gen Psychiatry
Negative predictive value: 87 % (negative test rules out AD with over 87 % probability)
Positive predictive value: 76 %
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European multicenter trial short-term predictive value of p-tau231 in incipient AD
Text4 centers, n: 144 - 56 HC, 88 MCI (43 conv / 45 non-conv)
Ewers et al. (2007) Neurology
Baseline analysis &short follow-upinterval: 1.5 years
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Prediction of conversion from MCI to AD is stable across centres using CSF P-Tau (ROC-analysis)
Ewers et al. (2007) Neurology
1 - Specificity0.0 0.2 0.4 0.6 0.8 1.0
Sens
itivi
ty
0.0
0.2
0.4
0.6
0.8
1.0
AmsterdamSwedenHeidelbergMunich
A priori defined cut-off (27.3 pg/ml of 1 reference center)
Sensitivity: 87.5%Specificity: 73.0%
Classification accuracy: 80.0%
Variable cut-offSensitivity: 81.1%Specificity: 79.8 %
Classification accuracy: 80.5%
4 European centers, n: 144 - 56 HC, 88 aMCI (43 conv / 45 non-conv)
A priori cut-off point = 27.32 pg/ml determined based on the Göteborg center
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Study design: Follow-up study over 4 - 6 years of aMCI and non-aMCI subjectsMCI n= 134 57 MCI → AD
56 MCI → MCI21 MCI → other dementias
Healthy controls n= 39 cognitively stable for 3 years
T-tau > 350 pg/mL +Aβ42 / P-tau ratio < 6.5
Hansson et al. (2006) Lancet Neurol
Improving prediction of incipient AD in MCI subjects combining three core CSF biomarker candidates
Sens MCI ⇒ AD 95 %
Spec MCI ⇒ MCI + other 87 %
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Hazard ratio : 25.5 (7.7 – 84.9)
T-tau > 350 pg / mL +Aβ42 / P-tau ratio < 6.5
Hansson et al. (2006) Lancet Neurol
Increased risk of AD in MCI subjects with pathological CSFPotential stratification & enrichment of MCI trials
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BACE1 & ApoE predict conversion from MCI to AD
4.003.002.001.000.00
Follow-up interval (in yrs)
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
Cum
Sur
viva
l
Cumulative survival
in ApoE & BACE model
MCI converter vs.
MCI Non converter
follow-up 2.5 yrs
• Intitial multimodal prediction set:
• CSF: BACE1 protein, total tau, p-tau(181), abeta1-42
• Neuropsychology: free recall, recognition, naming, word fluency (CERAD)
• ApoE genotype
Ewers et al. (accepted)
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CSF core feasible biomarker candidates altered inpresymptomatic and preclinical AD
• Same CSF marker phenotype as established in advanced clinical AD:
• decreased abeta42 predicts cognitive decline among older womenwithout MCI & dementia, Prospective Population Study; (Gustafson et al. (2007) J Neurol Neurosurg Psychiatry)
• aβ42 & P-Tau combination predicted later subjective cognitive impairment & decline in quality of life in healthy elderly subjects; (Stomrud et al. (2007) Dement Geriatr Cogn Disord)
• tau/abeta42 ratio predicts later cognitive decline in non-demented adults in a community setting (Fagan et al. (2007) Arch Neurol)
• tau/abeta42 ratio predicts later cognitive decline in normal controls at risk for MCI (Li et al. (2007) Neurology)
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Current stages of multimodal development of (bio-and imaging) markers in AD (after basic studies)
Stage I• Methodological study
• Establishing technicalcharacteristics
Stage II• Selected patients
• Determining sensitivityand specificity
• Determining norm values
Stage III • Controlled dx trials
(multicenter initiatives)
• Intent to diagnosepopulation
• Determining prevalenceand positive/negative predictive values
• Validate norm values
• Determination of addedvalue of diagnosticmethods (multimodal marker set)
• blood markers• proteome analysis• abeta oligomers• APP isoforms• total abeta• ....
• BACE 1• abeta 42/40-ratio• abeta-Ab• ...
• t-tau• phospho-tau 181, 231• abeta1-42
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Conclusion: current biochemical marker research isa dynamic field
• core feasible candidates are currently beeing validated in prospective, well controlled clinical studies
• using multi-institutional teamwork through large collaborative groups (ADNI trials)
• already established intra-individual stability (longitudinal CV), characteristics of the immunoassays (within-day and between-day CV)
• current validation of within-lab repeatability and between-lab reproducibility and of multicenter diagnostic and predictive performance (sensitivity, specificity, PPV, NPV)
• multi-center validation time frame ends within next 2-5 years
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Klinik für Psychiatrie und PsychotherapieLudwig-Maximilians-UniversitätMünchen
CSF biomarkers as endpoints in clinical trials on anti-Aβ compounds
Safety monitoring
CSF biomarkers
• CSF poly- / mononuclear cells General indicators of CNS inflammation• Albumin ratio Blood-brain barrier function / damage• IgG index Intrathecal IgG production
IgG oligoclonal bands• IgM index Intrathecal IgM production
IgM oligoclonal bands• T-tau Neuronal / axonal damage?
Neurofilament protein Damage to white-matter axons? Glial fibrillary acidic protein Damage to glial cells / gliosis?
• Aβ42 Primary efficacy measure• Aβ40 Primary efficacy measure• other Aβ isoforms Optional efficacy measures
• sAPPα Effect on non-amyloidogenic APP processing• BACE1 act., sAPPβ Effect on amyloidogenic APP processing
• Total tau Downstream biomarker for effect on neurodegeneration• Phospho-tau Downstream biomarker for effect on tau phosphorylation
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Open regulatory issuesdiscussion: role of biochemical markers
• as the development of such biochemical markers has been improved considerably there is still the question of how they should be used in clinical trials:
• for early characterisation, detection & prediction
• enrichment & stratification of trial populations
• endpoints in proof of concept studies or confirmatory clinical trials
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Harald Hampel Michael Ewers Arun L.W. BokdeStefan J. TeipelKatharina Bürger
University of Munich, Germany
Alzheimer Memorial Center
Trinity College, Dublin, Ireland