Longitudinal studies of familial and
sporadic Alzheimer’s disease
provide strategies for preclinical
intervention trials
ADI, Puerto Rico
May 2014
The Incidence of Alzheimer’s Disease
100
90
80
70
Crude
Annual
Incidence
per 1,000
X
60
50
40
30
X
20
X
10
X X
0
0-39
40-49
50-59
60-69
X
X X
70-79
80-89
90+
Age
Framingham (Bachman et al, 1993)
Chicago (Evans et al, 2003)
East Boston (Hebert et al, 1995)
X
Baltimore (Kawas et al, 2000)
All
The Amyloid Cascade Hypothesis
b
Ab
monomers
g
Ab
Aggregation
a
g
APP
Amyloidgenic
Pathway
P3
Amyloid
plaque
Nonamyloidgenic
Pathway
Inflammation
Neuronal loss
and AD
Ab, b-amyloid; AD, Alzheimer’s disease; APP, amyloid precursor protein
Tau pathology
Aβ PRODUCTION
APP
BACE1
C99
γ-sec
Aβ40, 42
dimer
Integral
membrane, (α–helix)
⇌
Aβ – Aβ
INTERACTIONS
Membrane
associated
(Oligomeric
intermediates)
Aβ toxic
oligomer
Membrane associated
and/or diffusible
ApoE
Aβ40, 42
oligomer
2° nucleation
dependent on
[fibril]
Fibrillar
amyloid
deposits
Membrane free
diffusible, (β–turn)
Aβ40, 42
oligomer
1° nucleation (Zn++/Cu++)
dependent on
[monomer/oligomer]
Extracellular, (β–sheet)
Aβ CLEARANCE
ApoE, Clu, ABCA7,
CD33, TYROBP,
etc (phagocytosis
pathway)
Aβ Metabolic Pools (CSF reflecting brain ISF)
sAD (Mawuenyega, Bateman et al 2010)
•Production rate Aβ42 is equal to controls
•Clearance rate of Aβ42 is 49% slower in AD
•It takes 13 hours for the complete turnover of the CSF pool for controls and
19 hours for sAD. There is a 42% impairment in the production:clearance
ratio in sAD
•Protective effect of A673T substitution in APP adjacent to BACE1 cleavage
site results in 40% reduction in Aβ in vitro production (Jonsson et al., 2012)
ADAD (Potter, Bateman et al., 2013
•Production rate of Aβ42 is increased 18%. No change Aβ38,40
•Soluble Aβ42:40 fractional turnover rate is increased 65%, consistent with the
increased removal of Aβ42 through extracellular deposition
•Newly formed Aβ is in exchange equilibrium with pre-existing Aβ, possibly in
oligomers or other aggregates, possibly by 20 nucleation events derived from
existing fibrillar aggregates
What Is the Best Target for a
Disease-Modifying Drug (DMD)?
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g-secretase inhibitor?
b-secretase inhibitor?
Ab oligomer?
Aggregated fibrillar Ab?
Ab clearance mechanism?
APP/Ab processing?
….or a combination of any above?
P3 oligomer model based on
crystal structure
Streltsov, Nuttall
2011
The Australian Imaging, Biomarkers and Lifestyle
Study of Aging
Australian ADNI
AIBL: Longitudinal cohort: Baseline to 54 months.
Baseline
(1,112)
372 NMC
Non-return:
112
Deceased:
NMC 2
SMC 4
MCI 5
AD 17
Non-AD dementia:
PDD 1
36 month
(824)*
54
month¤
(676)*
Returned at 36
months:
NMC 11
SMC 1
MCI 1
AD 3
Non-return:
74
Deceased:
NMC 2
SMC 1
MCI 1
AD 27
VDM 1
Non-AD dementia:
MCI-X 2
VDM 2
Returned at 54
months:
NMC 1
SMC 4
MCI 1
AD 1
211 AD
(29)
(50)
ApoE4 carrier
ApoE4 carrier
ApoE4 carrier
ApoE4 carrier
107 (28.8%)
97 (24.5%)
66 (49.6%)
132 (62.6%)
(97)
(114)
317 NMC
Non-return:
120
Deceased:
NMC 3
SMC 3
MCI 4
AD 34
Non-AD dementia:
PDD 1
MCI-X 1
VDM 3
133 MCI
(29)
(33)
(220)
18 month
(972)*
396 SMC
(254)
(7)
(4)
(13)
(65)
375 SMC
(3)
(32) (161)
82 MCI
(41)
(39)
(1)
197 AD
(62)
(26)
ApoE4 carrier
ApoE4 carrier
ApoE4 carrier
ApoE4 carrier
90 (28.4%)
94 (25.1%)
32 (39.0%)
136 (69.0%)
(212)
(78)
(62) (241)
301 NMC
(5)
(4)
(35)
(14)
309 SMC
(20)
(14)
(1)
(1)
55 MCI
(10)
(16)
(134)
154 AD
(62)
ApoE4 carrier
ApoE4 carrier
ApoE4 carrier
ApoE4 carrier
82 (27.2%)
80 (25.8%)
24 (43.6%)
106 (68.8%)
(202) (50)
(2)
(72)
(207)
(7)
(4)
(19)
(27)
51 MCI
(1)
(6)
(68)
255 NMC
290 SMC
76 AD
ApoE4 carrier
ApoE4 carrier
ApoE4 carrier
ApoE4 carrier
64 (25.1%)
74 (25.5%)
19 (37.3%)
52 (68.4%)
Methodology: Key outcomes
CLINICAL/COGNITIVE
Clinical and cognitive measures
• MMSE, CDR, Mood measures, Neuropsychological
battery
Clinical classification information
• NINCDS-ADRDA (possible/probable) AD classifications
• ICD-10 AD classifications
• MCI classifications
• Memory complaint status (in HC)
LIFESTYLE
Lifestyle information
Detailed dietary information
Detailed exercise information
Objective activity measures (actigraph – 100 volunteers)
Body composition scans (DEXA)
Medical History, Medications and demography
BIOMARKERS
NEUROIMAGING
Comprehensive clinical blood pathology
Neuroimaging scans (in 287 volunteers)
Genotype
• Apolipoprotein E, WGA in subgroup
PET Pittsburgh Compound B (PiB)
Stored fractions (stored in LN within 2.5 hrs of collection)
• Serum
• Plasma
• Platelets
• red blood cell,
• white blood cell (in dH20)
• white blood cell (in RNAlater, Ambion).
Magnetic Resonance Imaging
• 3D T1 MPRAGE
•T2 turbospin echo
•FLAIR sequence
11
11C-PIB
HC
for Ab imaging
AD
SUVR
3.0
1.5
0.0
Villemagne / Rowe
Ab burden quantification
NEOCORTICAL SUVR40-70
3.50
*†
*
*
†
HC
MCI
AD
DLB
FTD
(n = 117)
30% pos
(n = 79)
64% pos
(n = 68)
(n = 14)
(n = 21)
3.00
2.50
2.00
1.50
1.00
(n = 299)
Villemagne and Rowe 13
Longitudinal PiB PET follow-up
HC
Progression to aMCI
Progression to naMCI
Progression to AD
(n=104)
3.5
3.3
Neocortical SUVR
3.0
2.8
2.5
2.3
2.0
1.8
1.5
1.3
1.0
55
60
65
70
75
80
85
90
95
Age (years)
* PiB+/PiB- SUVR cut-off = 1.5
Villemagne / Rowe
Longitudinal PiB PET follow-up
MCI
Progression to FTD
Progression to VaD
Progression to AD
(n=48)
3.5
3.3
Neocortical SUVR
3.0
2.8
2.5
2.3
2.0
1.8
1.5
1.3
1.0
55
60
65
70
75
80
85
90
95
Age (years)
* PiB+/PiB- SUVR cut-off = 1.5
Villemagne / Rowe
Longitudinal PiB PET follow-up
AD
(n=33)
3.5
3.3
Neocortical SUVR
3.0
2.8
2.5
2.3
2.0
1.8
1.5
1.3
1.0
55
60
65
70
75
80
85
90
95
Age (years)
* PiB+/PiB- SUVR cut-off = 1.5
Villemagne / Rowe
AIBL: Aβ deposition over time
MCI+
3.0
Neocortical SUVRcb
AD
HC+
2.5
Mean SUVR AD+
(2.33)
2.9%/yr
2.0
(95%CI 2.5-3.3%/yr)
1.5
19.2 yr
(95%CI 17-23 yrs)
Mean SUVR HC(1.17)
12.0 yr
HC- MCI-
(95%CI 10-15 yrs)
1.0
0
10
20
Time (years)
30
40
AIBL: Relationship between
“abnormality” and CDR of 1.0
Plasma Ab Levels Compared With
CSF Ab Levels
Plasma (pg/mL)
HC (n = 576)
MCI (n = 69)
AD (n = 125)
↑/↓ in
MCI or AD
Ab1-40
157.7 ± 31
166.8 ± 37
172.3 ± 41
↑
Ab1-42
34.8 ± 10
33.6 ± 11
34.5 ± 10
↓
0.22 ± 0.06
0.20 ± 0.05*
0.20 ± 0.04*
↓
Ab1-42/Ab1-40
CSF (pg/mL)
HC (n = 24)
MCI (n = 62)
AD (n = 68)
↑/↓ in
MCI or AD
Ab1-40
9600 ± 3000
9500 ± 3200
8500 ± 2800*
↓
Ab1-42
403 ± 125
307 ± 114)*
263 ± 83*
↓
tau
104 ± 59
155 ± 109*
156 ± 87*
↑
p-tau-181
31 ± 17
42 ± 29
43 ± 26*
↑
*P < 0.05 vs HC.
Data are represented as mean ± standard deviation.
Kester MI, et al. Neurobiol Aging. 2012;33:1591-1598; Rembach A, et al. Alzheimers Dement. In press.
Model: metal-chaperones with moderate
affinity for metals
(nanomolar 10-9)
(low picomolar 10-11)
Xilinas, Barnham, Bush, Curtain
Prana Biotechnology, founded 1998 (Geoffrey Kempler)
PBT2: SAR based on
rational drug design
Follow Ups
CQ(PBT1)
‘POC’
Clinical trials
180+ screened
Strong SAR
Multiple scaffolds
130+ screened
PBT2
non 8-OHq activity
Tox testing
POC
PBT3 – PBT-x
Phase Ia & Ib
Phase IIa
> 45 in vivo candidates
Tox. testing
Barnham, Kripner, Kok, Gautier (Prana Biotechnology), 2002
Analysis of CQ / PBT2 interactions with Aß
Monomer
Analytical Ultracentrifugation
Small oligomers
Large oligomers
CQ and PBT2 induce the formation of low molecular weight Aß
oligomers (consistent with dimers/trimers)
Tim Ryan, Blaine Roberts
Effect of PBT2
and placebo
on the change in
biomarkers
from baseline at
12 weeks
(A) CSF Aβ42, (B) CSF Aβ40,
(C) CSF T-tau, and (D) CSF P-tau.
Data are least mean squares (SE). Scatter
plots of individual actual changes from
baseline at 12 weeks for CSF Aβ42 and
Aβ40 are shown, with mean values
(horizontal bars) included for each
treatment group.
13% fall in CSF Aβ42
Lannfelt et al., Lancet Neurology (2008)
Protein misfolding diseases:
strategy for disease modification
Stabilize!
Neutralize!
Clear!
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DIAN and A4: early intervention in
preclinical AD
DIAN-TU
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Autosomal Dominant AD – genetic mutation causing early onset
dementia across generations of the same family
50% risk of inheriting gene from mutation +ve parent
If mutation +ve penetrance of ADAD is nearly 100%
DIAN observational trial has been following ADAD families since 2009,
giving valuable insight into changes that occur decades before
symptoms appear
PRIMARY AIM: to determine whether Solanezumab or Gantenerumab
can prevent, delay or possibly even reverse AD changes in the brain
 Monthly infusion/injection for 2 years
 Measures include: MRI, PET scans (PiB, FDG, AV-45), CSF and blood
biomarkers, cognitive function
STARTING TREATMENT BEFORE SYMPTOMS APPEAR MAY GIVE BETTER OUTCOME
Anti-Amyloid Treatment in Asymptomatic
Alzheimer’s disease (the A4 Study)
The Melbourne Composite Site
Key Objectives
• Cognitive:
– To test the hypothesis that in preclinical AD, an anti-amyloid therapy
(solanezumab) will slow Aβ-associated cognitive decline as compared with
placebo.
• Neuroimaging:
– To test the hypothesis that solanezumab reduces Aβ amyloid burden, as
compared with placebo, as assessed using florbetapir PET imaging ligand.
– To determine if there are downstream effects of solanezumab on brain tau
using the novel tau PET imaging ligand, T807.
• Biomarkers:
– To assess effects of solanezumab on CSF concentrations of Aβ, p-tau and tau.
– To explore the role of polymorphisms in apolipoprotein E (ε carrier [ε4+], ε4
non-carrier [ε4-] and brain derived neurotrophic factor (BDNFVal/Val, BDNFMet)
and other genetic loci in the extent to which they moderate the rate of Aβrelated memory decline in both treated and placebo groups.
Key inclusion criteria
– 65-85 years
– Evidence of Aβ amyloid (PET)
– Asymptomatic (Clinical dementia rating = 0)
Key points: Protocol
• Following screening, 4 weekly solanezumab/placebo
(1:1) infusions (IV) for 168 weeks
• Cognitive testing @ baseline then 12/52 from week 6
• Aβ amyloid and tau (PET) @ baseline, years 1, 2, 3.
• Aβ amyloid and tau (CSF) @ baseline and year 3.
• Blood for biomarkers (AIBL protocol)@ baseline,
weeks 12, 24, 48, 108 and 168?
The AIBL Study Team
Osca Acosta
David Ames
Jennifer Ames
Manoj Agarwal
David Baxendale
Kiara Bechta-Metti
Carlita Bevage
Lindsay Bevege
Pierrick Bourgeat
Belinda Brown
Ashley Bush
Tiffany Cowie
Kathleen Crowley
Andrew Currie
David Darby
Daniela De Fazio
Denise El- Sheikh
Kathryn Ellis
Kerryn Dickinson
Noel Faux
Jonathan Foster
Jurgen Fripp
Christopher Fowler
Veer Gupta
Gareth Jones
Jane Khoo
Asawari Killedar
Neil Killeen
Tae Wan Kim
Eleftheria Kotsopoulos
Gobhathai Kunarak
Rebecca Lachovitski
Nat Lenzo
Qiao-Xin Li
Xiao Liang
Kathleen Lucas
James Lui
Georgia Martins
Ralph Martins
Paul Maruff
Colin Masters
Andrew Milner
Claire Montague
Lynette Moore
Audrey Muir
Christopher O’Halloran
Graeme O'Keefe
Anita Panayiotou
Athena Paton
Jacqui Paton
Jeremiah Peiffer
Svetlana Pejoska
Kelly Pertile
Kerryn Pike
Lorien Porter
Roger Price
Parnesh Raniga
Alan Rembach
Miroslava Rimajova
Elizabeth Ronsisvalle
Rebecca Rumble
Mark Rodrigues
Christopher Rowe
Olivier Salvado
Jack Sach
Greg Savage
Cassandra Szoeke
Kevin Taddei
Tania Taddei
Brett Trounson
Marinos Tsikkos
Victor Villemagne
Stacey Walker
Vanessa Ward
Michael Woodward
Olga Yastrubetskaya
Neurodegeneration Research Group
The University of
Melbourne
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Paul Adlard
Kevin Barnham
Shayne Bellingham
Martin Boland
Ashley Bush
Roberto Cappai
Michael Cater
Robert Cherny
Joe Ciccotosto
Steven Collins
Peter Crouch
Cyril Curtain
Simon Drew
James Duce
Genevieve Evin
Noel Faux
The Mental Health
Research Institute
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Michelle Fodero-Tavoletti
David Finkelstein
Catherine Haigh
Andrew Hill
Ya Hui Hung
Vijaya Kenche
Vicky Lawson
Qiao-Xin Li
Gawain McColl
Chi Pham
Blaine Roberts
Laura Vella
Victor Villemagne
Tony White
Collaborators
• Alfred Hospital: Catriona McLean
• Austin Health: Chris Rowe, Victor Villemagne
• Chemistry (Uni Melb): Paul Donnelly
• Cogstate: Paul Maruff
• CSIRO (Structural Biology): Jose Varghese, Victor Streltsov,
Stewart Nuttall
• Imperial College London: Craig Ritchie
• Mass General Hospital / Harvard Med School: Rudy Tanzi
• NARI: David Ames, Kathryn Ellis
• SVIMR: Michael Parker, Luke Miles
• Network Aging Research (Heidelberg): Konrad Beyreuther