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 Crude Annual Incidence per 1,000

60 50 40 30 20 10 0 100 90 80 70 X X X X X X X X 0-39 40-49 50-59 60-69 70-79 80-89 90+ All

Age

Framingham (Bachman et al, 1993) East Boston (Hebert et al, 1995)

The Amyloid Cascade Hypothesis

P3 g a b g A b monomers

A

b

Aggregation APP

Amyloidgenic Pathway Nonamyloidgenic Pathway

Amyloid plaque

Inflammation A b , b -amyloid; AD, Alzheimer’s disease; APP, amyloid precursor protein

Neuronal loss and AD

Tau pathology

Aβ PRODUCTION APP BACE1 C99 γ-sec Aβ 40, 42 dimer Integral membrane, ( α–helix) A

β

– Aβ INTERACTIONS (Oligomeric intermediates) Membrane associated and/or diffusible Membrane associated Aβ toxic oligomer ApoE Aβ 40, 42 oligomer 2 ° nucleation dependent on [fibril] Fibrillar amyloid deposits Membrane free diffusible, ( β–turn) Aβ 40, 42 oligomer 1 ° nucleation (Zn ++ /Cu ++ ) dependent on [monomer/oligomer] Extracellular, ( β–sheet) Aβ CLEARANCE ApoE, Clu, ABCA7, CD33, TYROBP, etc (phagocytosis pathway)

A β Metabolic Pools (CSF reflecting brain ISF)

sAD (Mawuenyega, Bateman et al 2010) •Production rate Aβ 42 is equal to controls •Clearance rate of Aβ 42 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 Aβ 42 is increased 18%. No change Aβ 38,40 •Soluble Aβ 42:40 fractional turnover rate is increased 65%, consistent with the increased removal of Aβ 42 through extracellular deposition •Newly formed Aβ is in exchange equilibrium with pre-existing Aβ, possibly in oligomers or other aggregates, possibly by 2 0 nucleation events derived from existing fibrillar aggregates

What Is the Best Target for a Disease-Modifying Drug (DMD)?

• •

g

-secretase inhibitor?

b

-secretase inhibitor?

•

A

b

oligomer?

• Aggregated fibrillar A

b

?

• A

b

clearance mechanism?

• APP/A

b

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.

372 NMC 396 SMC Baseline (1,112)

Non-return:

112

Deceased:

NMC 2 SMC 4 MCI 5 AD 17

Non-AD dementia:

PDD 1

(33) ApoE4 carrier 107 (28.8%) (29) ApoE4 carrier 97 (24.5%)

(220)

(97) (114)

(254)

(7)

18 month (972)* 36 month (824)* 54 month ¤ (676) *

Non-return:

120

Deceased:

NMC 3 SMC 3 MCI 4 AD 34

Non-AD dementia:

PDD 1 MCI-X 1 VDM 3

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 1 317 NMC

(39) ApoE4 carrier 90 (28.4%)

(212)

(78)

301 NMC

(14) ApoE4 carrier 82 (27.2%)

(202)

(50) (2)

255 NMC

ApoE4 carrier 64 (25.1%)

375 SMC

(41) ApoE4 carrier 94 (25.1%) (62)

(241)

(5)

309 SMC

(20) ApoE4 carrier 80 (25.8%) (72)

(207)

(7)

290 SMC

ApoE4 carrier 74 (25.5%) (29)

133 MCI

ApoE4 carrier 66 (49.6%) (4) (13)

(65)

(26)

82 MCI

ApoE4 carrier 32 (39.0%) (4) (14)

(35)

(1) (10)

55 MCI

ApoE4 carrier 24 (43.6%) (62) (1) (16)

(134) 154 AD

ApoE4 carrier 106 (68.8%) (4) (19)

(27) 51 MCI

ApoE4 carrier 19 (37.3%) (50) (62) (1)

211 AD

ApoE4 carrier 132 (62.6%) (1) (3) (32)

(161) 197 AD

ApoE4 carrier 136 (69.0%) (6)

(68) 76 AD

ApoE4 carrier 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)

Medical History, Medications and demography

LIFESTYLE

Lifestyle information

Detailed dietary information Detailed exercise information Objective activity measures (actigraph – 100 volunteers) Body composition scans (DEXA) BIOMARKERS

Comprehensive clinical blood pathology Genotype

• Apolipoprotein E, WGA in subgroup

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).

NEUROIMAGING

Neuroimaging scans (in 287 volunteers)

PET Pittsburgh Compound B (PiB) Magnetic Resonance Imaging • 3D T1 MPRAGE •T2 turbospin echo •FLAIR sequence 11

HC

11

C-PIB for A

b

imaging

AD

SUVR 3.0

1.5

0.0

Villemagne / Rowe

A

b

burden quantification 3.50

* † 3.00

2.50

2.00

1.50

1.00

(n = 299)

HC

(n = 117) 30% pos

MCI

(n = 79) 64% pos

* AD

(n = 68)

* † DLB

(n = 14)

FTD

(n = 21) Villemagne and Rowe 13

Longitudinal PiB PET follow-up

HC (

n=104

) Progression to aMCI Progression to naMCI Progression to AD 3,5 3,3 3,0 2,8 2,5 2,3 2,0 1,8 1,5 1,3 1,0 55 60 65 70 75 Age (years) 80 85 90 95

*

PiB+/PiB- SUVR cut-off = 1.5

Villemagne / Rowe

Longitudinal PiB PET follow-up

MCI (

n=48

) Progression to FTD Progression to VaD Progression to AD 3,5 3,3 3,0 2,8 2,5 2,3 2,0 1,8 1,5 1,3 1,0 55 60 65 70 75 Age (years) 80 85 90 95

*

PiB+/PiB- SUVR cut-off = 1.5

Villemagne / Rowe

Longitudinal PiB PET follow-up

AD (

n=33

) 3,5 3,3 3,0 2,8 2,5 2,3 2,0 1,8 1,5 1,3 1,0 55 60 65 70 75 Age (years) 80 85 90 95

* PiB+/PiB- SUVR cut-off = 1.5

Villemagne / Rowe

HC+

AIBL: Aβ deposition over time

MCI+ AD 3,0 2,5 Mean SUVR AD+ (2.33) 2,0 2.9%/yr (95%CI 2.5-3.3%/yr) 1,5 HC MCI 1,0 19.2 yr (95%CI 17-23 yrs) 0 12.0 yr (95%CI 10-15 yrs) 10 20 Time (years) 30 40 Mean SUVR HC (1.17)

AIBL: Relationship between

“

abnormality

”

and CDR of 1.0

Plasma A

b

Levels Compared With CSF A

b

Levels

Plasma (pg/mL) A

b

1-40 A

b

1-42 A

b

1-42 /A

b

1-40 HC (n = 576)

157.7 ± 31 34.8 ± 10 0.22 ± 0.06

CSF (pg/mL) A

b

1-40 A

b

1-42 tau p-tau-181 HC (n = 24)

9600 ± 3000 403 ± 125 104 ± 59 31 ± 17

MCI (n = 69)

166.8 ± 37 33.6 ± 11 0.20 ± 0.05*

MCI (n = 62)

9500 ± 3200 307 ± 114)* 155 ± 109* 42 ± 29

AD (n = 125)

172.3 ± 41 34.5 ± 10 0.20 ± 0.04*

AD (n = 68)

8500 ± 2800* 263 ± 83* 156 ± 87* 43 ± 26*

↑/↓ in MCI or AD ↑ ↓ ↓ ↑/↓ in MCI or AD ↓ ↓ ↑ ↑ *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 POC 180+ screened Strong SAR PBT2 Tox testing Phase Ia & Ib Multiple scaffolds 130+ screened non 8-OHq activity PBT3 – PBT-x > 45 in vivo candidates Phase IIa Tox. testing

Barnham, Kripner, Kok, Gautier (Prana Biotechnology), 2002

Analysis of CQ / PBT2 interactions with Aß

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) (C)

CSF A β 42 ,

(B)

CSF A β 40 , 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 A β 42 A β 40 are shown, with mean values and (horizontal bars) included for each treatment group.

13% fall in CSF A β 42

Lannfelt et al., Lancet Neurology (2008)

Protein misfolding diseases: strategy for disease modification

Stabilize!

Neutralize!

Clear!

24

DIAN and A4: early intervention in preclinical AD

DIAN-TU

• • • • • 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 (BDNF Val/Val , BDNF Met ) and other genetic loci in the extent to which they moderate the rate of Aβ related 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 The Mental Health Research Institute

• • • • • • • • • • • • • • • • 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 • • • • • • • • • • • • • • 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