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, All Age X X X X X X X Framingham (Bachman et al, 1993) East Boston (Hebert et al, 1995) Chicago (Evans et al, 2003) Baltimore (Kawas et al, 2000) X X

The Amyloid Cascade Hypothesis A ,  -amyloid; AD, Alzheimer’s disease; APP, amyloid precursor protein A  Aggregation Amyloidplaque P3 Amyloidgenic Pathway     Nonamyloidgenic Pathway APP Neuronal loss and AD A  monomers Tau pathology Inflammation

Aβ PRODUCTION Aβ 40, 42 dimer A β – Aβ INTERACTIONS (Oligomeric intermediates) Aβ CLEARANCE APP BACE1 C99 γ-sec Integral membrane, ( α–helix) Membrane free diffusible, ( β–turn) Extracellular, ( β–sheet) Membrane associated Membrane associated and/or diffusible 1° nucleation (Zn ++ /Cu ++ ) dependent on [monomer/oligomer] Fibrillar amyloid deposits Aβ toxic oligomer Aβ 40, 42 oligomer ApoE, Clu, ABCA7, CD33, TYROBP, etc (phagocytosis pathway) 2° nucleation dependent on [fibril] ⇌ ⇌ ⇌ ⇌ ApoE

Aβ 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)?  -secretase inhibitor?  -secretase inhibitor? A  oligomer? Aggregated fibrillar A  ? A  clearance mechanism? APP/A  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) 18 month (972)* 36 month (824)* (33) (29)(50)(29) (97)(114)(7)(13)(4)(1)(3)(32) (39) (41) (62) (26) (1)(78)(62)(4)(5)(14) (1) (16) (220)(254)(161) (212)(241) (65) (35) (134) (14) (50)(72)(7)(4)(19)(1)(6)(202)(207)(27)(68) Non-return: 74 Deceased: NMC 2 SMC 1 MCI 1 AD 27 VDM 1 Non-AD dementia: MCI-X 2 VDM 2 Non-return: 74 Deceased: NMC 2 SMC 1 MCI 1 AD 27 VDM 1 Non-AD dementia: MCI-X 2 VDM 2 54 month ¤ (676) * 255 NMC 290 SMC 51 MCI 76 AD (2) ApoE4 carrier 107 (28.8%) ApoE4 carrier 97 (24.5%) ApoE4 carrier 66 (49.6%) ApoE4 carrier 132 (62.6%) ApoE4 carrier 90 (28.4%) ApoE4 carrier 94 (25.1%) ApoE4 carrier 32 (39.0%) ApoE4 carrier 136 (69.0%) ApoE4 carrier 82 (27.2%) ApoE4 carrier 80 (25.8%) ApoE4 carrier 24 (43.6%) ApoE4 carrier 106 (68.8%) ApoE4 carrier 64 (25.1%) ApoE4 carrier 74 (25.5%) ApoE4 carrier 19 (37.3%) ApoE4 carrier 52 (68.4%) 396 SMC 133 MCI 211 AD Non-return: 112 Deceased: NMC 2 SMC 4 MCI 5 AD 17 Non-AD dementia: PDD 1 Non-return: 112 Deceased: NMC 2 SMC 4 MCI 5 AD 17 Non-AD dementia: PDD 1 Non-return: 120 Deceased: NMC 3 SMC 3 MCI 4 AD 34 Non-AD dementia: PDD 1 MCI-X 1 VDM 3 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 Returned at 36 months: NMC 11 SMC 1 MCI 1 AD 3 Returned at 54 months: NMC 1 SMC 4 MCI 1 AD 1 Returned at 54 months: NMC 1 SMC 4 MCI 1 AD NMC 317 NMC 375 SMC 82 MCI 197 AD (20)(62)(10) 301 NMC 309 SMC 55 MCI 154 AD

Methodology: Key outcomes 11 CLINICAL/COGNITIVE BIOMARKERS LIFESTYLE NEUROIMAGING 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). Lifestyle information Detailed dietary information Detailed exercise information Objective activity measures (actigraph – 100 volunteers) Body composition scans (DEXA) Neuroimaging scans (in 287 volunteers) PET Pittsburgh Compound B (PiB) Magnetic Resonance Imaging 3D T1 MPRAGE T2 turbospin echo FLAIR sequence 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

Villemagne / Rowe 11 C-PIB for A  imaging SUVR ADHC

13 A  burden quantification HC (n = 117) 30% pos MCI (n = 79) 64% pos AD (n = 68) DLB (n = 14) FTD (n = 21) NEOCORTICAL SUVR * †**† (n = 299) Villemagne and Rowe

Neocortical SUVR Age (years) * * PiB+/PiB- SUVR cut-off = 1.5 HC (n=104) Progression to aMCI Progression to naMCI Progression to AD Longitudinal PiB PET follow-up Villemagne / Rowe

Neocortical SUVR Age (years) * * PiB+/PiB- SUVR cut-off = 1.5 MCI (n=48) Progression to FTD Progression to VaD Progression to AD Longitudinal PiB PET follow-up Villemagne / Rowe

* * PiB+/PiB- SUVR cut-off = 1.5 Neocortical SUVR Age (years) AD (n=33) Longitudinal PiB PET follow-up Villemagne / Rowe

Neocortical SUVR cb Time (years) Mean SUVR AD+ (2.33) 19.2 yr (95%CI yrs) Mean SUVR HC- (1.17) 12.0 yr (95%CI yrs) 2.9%/yr (95%CI %/yr) HC- MCI+ AD MCI- HC AIBL: Aβ deposition over time

AIBL: Relationship between “abnormality” and CDR of 1.0

Plasma (pg/mL) ↑/↓ in MCI or AD HC (n = 576)MCI (n = 69)AD (n = 125) A  ± ± ± 41↑ A  ± ± ± 10↓ A  1-42 /A  ± ± 0.05*0.20 ± 0.04*↓ CSF (pg/mL) ↑/↓ in MCI or AD HC (n = 24)MCI (n = 62)AD (n = 68) A  ± ± ± 2800*↓ A  ± ± 114)*263 ± 83*↓ tau104 ± ± 109*156 ± 87*↑ p-tau ± 1742 ± 2943 ± 26*↑ * P < 0.05 vs HC. Data are represented as mean ± standard deviation. Kester MI, et al. Neurobiol Aging. 2012;33: ; Rembach A, et al. Alzheimers Dement. In press. Plasma A  Levels Compared With CSF A  Levels

Xilinas, Barnham, Bush, Curtain Prana Biotechnology, founded 1998 (Geoffrey Kempler) Model: metal-chaperones with moderate affinity for metals (nanomolar ) (low picomolar )

Strong SAR 180+ screened PBT2Tox testing Phase Ia & Ib ‘POC’ Clinical trials CQ(PBT1) POCnon 8-OHq activity 130+ screened PBT3 – PBT-x > 45 in vivo candidates Tox. testing PBT2: SAR based on rational drug design PBT2: SAR based on rational drug design Follow Ups Multiple scaffolds Phase IIa Barnham, Kripner, Kok, Gautier (Prana Biotechnology), 2002

Analysis of CQ / PBT2 interactions with Aß CQ and PBT2 induce the formation of low molecular weight Aß oligomers (consistent with dimers/trimers) Tim Ryan, Blaine Roberts Analytical Ultracentrifugation Large oligomers Small oligomers Monomer

Effect of PBT2 and placebo on the change in biomarkers from baseline at 12 weeks Lannfelt et al., Lancet Neurology (2008) (A) CSF Aβ 42, (B) CSF Aβ 40, (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 Aβ 42 and Aβ 40 are shown, with mean values (horizontal bars) included for each treatment group. 13% fall in CSF Aβ 42

Protein misfolding diseases: strategy for disease modification 24 Stabilize! Neutralize! Clear!

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 – 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 baseline then 12/52 from week 6 Aβ amyloid and tau baseline, years 1, 2, 3. Aβ amyloid and tau baseline and year 3. Blood for biomarkers (AIBL baseline, weeks 12, 24, 48, 108 and 168?

The AIBL Study Team 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 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 The University of Melbourne The Mental Health Research Institute

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