Amyloid imaging: Past, present and future perspectives

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Review

Amyloid imaging: Past, present and future perspectives Victor L. Villemagne a,b,c,∗ a

Department of Molecular Imaging & Therapy, Centre for PET, Austin Health, 145 Studley Road, Heidelberg, Victoria 3084, Australia The Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia c Department of Medicine, The University of Melbourne, Victoria, Australia b

a r t i c l e

i n f o

Article history: Received 15 November 2015 Received in revised form 21 January 2016 Accepted 22 January 2016 Available online xxx Keywords: Alzheimer’s disease A␤ Positron emission tomography Neurodegenerative disorders Brain imaging

a b s t r a c t Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterised by the gradual onset of dementia. The pathological hallmarks of the disease are A␤ amyloid plaques, and tau neurofibrillary tangles, along dendritic and synaptic loss and reactive gliosis. Functional and molecular neuroimaging techniques such as positron emission tomography (PET) using functional and molecular tracers, in conjuction with other A␤ and tau biomarkers in CSF, are proving valuable in the differential diagnosis of AD, as well as in establishing disease prognosis. With the advent of new therapeutic strategies, there has been an increasing application of these techniques for the determination of A␤ burden in vivo in the patient selection, evaluation of target engagement and assessment of the efficacy of therapeutic approaches aimed at reducing A␤ in the brain. © 2016 Elsevier B.V. All rights reserved.

Contents 1. 2.

3. 4.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Molecular neuroimaging in AD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 2.1. Functional neuroimaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 2.2. Amyloid imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 2.2.1. A␤ imaging BP (before PiB) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 A␤ imaging AP (after PiB) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 2.2.2. A␤ imaging in Alzheimer’s disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Future perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00

1. Introduction Alzheimer’s disease (AD), the leading cause of dementia in the elderly, is an irreversible, progressive neurodegenerative disorder clinically characterized by memory loss and cognitive decline (Khachaturian, 1985), leading invariably to death, usually within 7–10 years after diagnosis. Age is the dominant risk factor in AD. The progressive nature of neurodegeneration suggests an agedependent process that ultimately leads to synaptic failure and neuronal damage (Isacson et al., 2002) in cortical areas of the

∗ Correspondence to: Department of Molecular Imaging & Therapy, Centre for PET, Austin Health, 145 Studley Road, Heidelberg, Vic. 3084, Australia. Fax: +61 3 9496 5663. E-mail address: [email protected]

brain essential for memory and higher mental functions, eventually affecting activities of daily living. From a neuropathological perspective, the typical macroscopic picture of an AD brain shows gross cortical atrophy. Microscopically, there is widespread cellular degeneration and neuronal loss that affects primarily the outer three layers of the cerebral cortex. These changes are accompanied by reactive gliosis, diffuse synaptic and neuronal loss, and by the presence of the pathological hallmarks of the disease, intracellular neurofibrillary tangles (NFT) and extracellular amyloid plaques. (Jellinger, 1990; Masters, 2005; Masters and Beyreuther, 2005) Neurofibrillary tangles are intraneuronal bundles of paired helical filaments constituted by an abnormally phosphorylated form of the tau protein (Jellinger and Bancher, 1998; Michaelis et al., 2002). Plaques consist of extracellular aggregates of a 4 kDa self-aggregating, 39–43 amino acid metallopeptide, amyloid ␤-peptide (A␤) (Masters et al., 1985),

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derived from the proteolytic cleavage of the amyloid precursor protein (APP) by ␤ and ␥-secretases. (Cappai and White, 1999). For the last 20 years the clinical diagnosis of AD was based on progressive impairment of memory and decline in at least one other cognitive domain, and by excluding other diseases that might also present with dementia such as frontotemporal dementia (FTD), dementia with Lewy-bodies (DLB), stroke, brain tumor, normal pressure hydrocephalus or depression. (Cummings et al., 1998; Larson et al., 1996) In other words, dementia. (McKhann et al., 1984). Diagnostic accuracy for AD usually depends on the disease stage and can exceed 90% in academic settings in mid or late stages (Rasmusson et al., 1996). The new criteria for the diagnosis of AD introduces imaging and fluid biomarker information and does not require the presence of dementia (Dubois et al., 2010, 2007). A variable period of up to five years of prodromal decline in cognition characterized by a relatively isolated impairment in memory, known as Mild Cognitive Impairment (MCI), usually precedes the formal diagnosis of AD. (Petersen, 2000; Petersen et al., 1999, 2001) About 40–60% of carefully characterized subjects with MCI will subsequently progress to meet criteria for AD over a 3–4-year period (Petersen, 2000; Petersen et al., 1995, 1999). Despite all the tremendous corpus of knowledge of genetics, epidemiology, risk factors, and neuropathological mechanisms, there is still no cure for AD.

2. Molecular neuroimaging in AD 2.1. Functional neuroimaging The insight into the molecular mechanism of AD pathogenesis opened new avenues for the successful development of new neuroimaging approaches. (Selkoe, 2000) Modern functional neuroimaging techniques such as positron emission tomography (PET), tend to be more sensitive than structural imaging modalities, identifying subtle pathophysiologic changes in the brain, before structural changes are present (Bobinski et al., 1999; de Leon et al., 1997; De Toledo-Morrell et al., 2000; Dickerson et al., 2001; Juottonen et al., 1998; Killiany et al., 2000; Xu et al., 2000), therefore possessing greater potential for accurate and early diagnosis, monitoring disease progression, and better treatment follow-up (Silverman and Phelps, 2001; Villemagne et al., 2005). PET is a sensitive molecular imaging technique that allows in vivo quantification of radiotracer concentrations in the picomolar range, where either the radiotracer bears the same biochemical structure, is an analog or a substrate of the chemical process being evaluated, allowing the in vivo assessment of the molecular process at their sites of action, (Phelps, 2000) permitting detection of disease processes at asymptomatic stages when there is no evidence of anatomic changes on CT and MRI. Several radiolabeled PET tracers are already used to evaluate biological processes in vivo, (Camargo, 2001; Phelps, 2000; Silverman and Phelps, 2001; Van Heertum and Tikofsky, 2003) aiding in the differential diagnosis of AD from other conditions such as vascular dementia, frontotemporal dementia, DLB, and depression (Salmon et al., 1994; Van Heertum and Tikofsky, 2003). FDG PET is not only used in the differential diagnosis of AD, but also provides a diagnosis of prodromal AD two or more years before the full dementia picture is manifested. (Chang and Silverman, 2004; Silverman et al., 1999, 2001, 2002b) A pattern of reduced temporoparietal and posterior cingulate FDG uptake with sparing of the basal ganglia, thalamus, cerebellum, and primary sensorimotor cortex is the typical FDG ‘AD signature’ (Coleman, 2005; Devanand et al., 1997; Jagust et al., 2007; Salmon et al., 1994). Due to its high sensitivity (>90%) for detecting temporoparietal and posterior cingulate hypometabolism FDG–PET has improved diagnostic and prognostic accuracy in patients with probable AD (Kennedy et al., 1995;

Salmon et al., 1994; Silverman et al., 2002a, 2001; Small et al., 1995). A similar pattern of hypometabolism has been reported in normal elderly ApoE ␧4 carriers, (Reiman et al., 1996) MCI, (Chetelat et al., 2003; Chetelat et al., 2005; Mosconi et al., 2006a) asymptomatic subjects with mutations associated with familial AD, (Kennedy et al., 1995; Rossor et al., 1996) and in subjects with a strong family history of AD (Mosconi et al., 2006b). FDG hypometabolism is correlated with cognition (Furst et al., 2010; Landau et al., 2009) and is predictive of future cognitive decline (Drzezga et al., 2005, 2003; Mosconi et al., 2004). PET has also been used to assess neuroreceptor/neurotransmitter systems in vivo. Nicotinic acetylcholine receptors (nAChRs) have been implicated in a variety of central processes, such as memory and cognition (Nordberg et al., 1991; Villemagne et al., 1998). Abnormally low densities of nAChRs have been measured in vitro in autopsy brain tissue of AD patients. PET studies revealed a reduced uptake and binding of 11 C-nicotine in the temporal and frontal cortices of AD patients (Nordberg, 1993a,b; Nordberg et al., 1991). Though the main focus of neuroceptor studies in AD has been the study of nAChRs, several other neurotransmitter/neuroreceptor systems, such as the dopaminergic, opiate and histaminergic systems amomg others, were also evaluated in dementing neurodegenative conditions. (Cohen et al., 1997; Higuchi et al., 2000; Kemppainen et al., 2000; Kepe et al., 2006; Piggott et al., 2003; Sedvall et al., 1987; Small, 2004; Versijpt et al., 2003; Walker et al., 2002). Enzymes involved in the degradation of neurotransmitters, such as brain acetylcholinesterase, have also been the focus of several studies (Kikuchi et al., 2013; Okamura et al., 2008).

2.2. Amyloid imaging A␤ plaques and NFT are the hallmark brain lesions of AD. These microscopic aggregates are still well beyond the resolution of conventional neuroimaging techniques used for the clinical evaluation of patients with AD. Positron emission tomography (PET) is a sensitive molecular imaging technique that allows in vivo quantification of radiotracer concentrations in the picomolar range, allowing the non-invasive assessment of molecular processes at their sites of action, detecting disease processes at asymptomatic stages when there is no evidence of anatomic changes on computed tomography (CT) and magnetic resonance imaging (MRI) (Phelps, 2000). In the past most techniques focused on non-specific features derived mainly from dendritic and neuronal loss, which are relatively late and non-specific features in the progression of the AD, and secondary to the basic molecular dysfunction. While clinical criteria together with current structural neuroimaging techniques are sensitive and specific enough for the diagnosis of AD at the mid or late stages of the disease, the development of a reliable method of assessing A␤ burden in vivo has allowed early diagnosis at presymptomatic stages, more accurate differential diagnosis, as well as treatment follow up. (Villemagne et al., 2005). Moreover, Quantitative imaging of A␤ burden in vivo has provided insights into the relationship between A␤ burden and clinical and neuropsychological characteristics in the AD spectrum as well in other neurodegenerative conditions where A␤ plays a role. Furthermore, because new treatment strategies to prevent or slow disease progression through early-intervention are being evaluated, the accurate recognition of the underlying pathological process being targeted is essential. These fluid and imaging surrogate markers of pathology are being used for patient selection, target engagement and evaluation of efficacy of anti-A␤ therapy alongside clinical and neuropsychological tests.

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2.2.1. Aˇ imaging BP (before PiB) Since A␤ plays a central role in AD pathogenesis, many efforts were focused on generating radiotracers for imaging A␤ in vivo (for review see (Villemagne and Rowe, 2010)). The years spanning the last decade of the las century saw the development of several compounds evaluated with different fortune as potential A␤ probes: self-associating A␤ amyloid fragments, (Friedland et al., 2000; Ghilardi et al., 1996; Kurihara and Pardridge, 2000; Lee, 2002; Marshall et al., 2002; Saito et al., 1995) monoclonal and anti-A␤ antibodies fragments, (Majocha et al., 1992; Walker et al., 1994) serum amyloid P, and basic fibroblast growth factor. (Shi et al., 2002) Almost a decade of unsuccessful trials, A␤ imaging showed the first promising results with the report of successful imaging in an AD patient with 18F-FDDNP, a very lipophilic radiofluorinated NSAID derivative developed by Barrio and colleagues at UCLA (Barrio et al., 1999). 18 F-FDDNP has been reported to bind non-selectively to both extracellular A␤ plaques and intracellular NFT (Agdeppa et al., 2001a,b, 2003; Barrio et al., 1999; ShoghiJadid et al., 2002; Small et al., 2002; Smid et al., 2013), while also binding to prion plaques in Creutzfeld–Jakob disease (CJD) brain tissue (Bresjanac et al., 2003). Higher 18 F-FDDNP retention was observed in brain regions associated with memory performance, regional glucose hypometabolism and brain atrophy in AD (Small et al., 2002, 2006). A truly pan-amyloid tracer, 18F-FDDNP has been applied to the assessment of several neurodegenerative conditions not only AD (Small et al., 2006), but also in adult Down syndrome patients (Nelson et al., 2011), American football players suspected of CTE (Small et al., 2013), in non-AD tauopathies such as PSP (Kepe et al., 2013), and frontotemporal dementia; as well as in prion disorders such as Creutzfeld–Jakob disease (CJD) (Bresjanac et al., 2003) and Gerstmann–Straussler–Scheinker disease (Kepe et al., 2010). Head-to-head comparison of 18 F-FDDNP with 11 C–PIB in human (Shin et al., 2008; Tolboom et al., 2009) and non-human primates (Noda et al., 2008) highlighted the very limited dynamic range of 18 F-FDDNP, and FDDNP in concentrations similar to those achieved during a PET scan showed limited binding to both NFT and A␤ plaques in vitro. (Thompson et al., 2009) In a longitudinal study FDDNP was found be less useful than 11 C-PiB and 18 F-fluoro-deoxy glucose (FDG) for the assessment of disease progression (Ossenkoppele et al., 2012) but in contrast with other A␤ tracers it correlates with cerebrospinal fluid (CSF) tau (Tolboom et al., 2009). While FDDNP might be unable to identify which is the misfolded protein responsible for a specific condition, it does rely on the regional brain distribution of the tracer to identify retention patterns that are characteristic of those conditions. Some of the compounds developed as PET radiotracers were also evaluated as contrast agents for MR imaging of plaques. (Benveniste et al., 1999; Higuchi et al., 2005; Sato et al., 2004; Vanhoutte et al., 2005; Wadghiri et al., 2003, 2005) Furthermore, some MRI approaches are trying to capitalize on the high Cu or Fe content of plaques for imaging. (Falangola et al., 2005) 2.2.2. Aˇ imaging AP (after PiB) The quest for A␤ tracers led to the development of derivatives of histopathological dyes such as Congo red, Chrysamine-G, Thioflavin S and T, and acridine orange (Bacskai et al., 2003; Klunk et al., 2002, 2004, 2001, 2003; Kung et al., 2001, 2004, 2002, 2003; Lee et al., 2001; Link et al., 2001; Mathis et al., 2002, 2001, 2003; Ono et al., 2003; Shimadzu et al., 2003; Skovronsky et al., 2000; Zhang et al., 2005a; Zhuang et al., 2001). The removal of the methyl group eliminated the charged quaternary amine from the heterocycle portion of the ThT, resulting in a basic Benzothiazole-aniline scaffold. PiB is a derivative of Thioflavin T, a fluorescent dye commonly used to assess fibrillisation into ␤-sheet conformation, (LeVine, 1999) and as such PiB has been shown to bind to fibrillar A␤ in neuritic

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plaques and to a range of additional A␤ containing lesions including diffuse plaques and cerebral amyloid angiopathy (CAA), (Lockhart et al., 2007) as well as to A␤ oligomers—albeit with lower affinity. (Maezawa et al., 2008). The introduction of PiB was a game-changer. For the first time there was a selective A␤ tracer that allow the noninvasive assessment of A␤ pathology in vivo. Since then, human A␤ imaging studies have been conducted in AD patients, normal controls and patients with other dementias using 11 C-PiB (Klunk et al., 2004) (Fig. 1), as well as other 11 C or 18 F tracers, that can be classified as first generation tracers following very close on the steps of PiB such as 11 C-SB13 (Verhoeff et al., 2004), 11 C-ST1859 (Bauer et al., 2006), 11 C-BF227 (Kudo et al., 2007), 11 C-AZD2138 (Nyberg et al., 2009). A second generation of tracers labeled with 18 F such as 18 F-florbetaben (Rowe et al., 2008) 18 F-flutemetamol (Serdons et al., 2009a), and 18 F-florbetapir (Wong et al., 2010) (Fig. 1). These three tracers have already been approved for clinical application in the US and Europe by the FDA and EMA, respectively. The best characterized candidate of the third generation of A␤ PET tracers is 18 F-NAV4694-a.k.a. as AZD4694- (Cselenyi et al., 2012; Rowe et al., 2013b) (Fig. 1). The first generation: the life and times of PiB. 11 C-PiB, (PiB) the most successful and widely used of the A␤ tracers has high affinity and high selectivity for fibrillar A␤ in plaques and in other A␤ containing lesions (Cohen et al., 2012; Klunk et al., 2001; Mathis et al., 2002, 2005; Price et al., 2005; Ye et al., 2005). PiB also displays a much lower affinity toward other misfolded proteins with a similar ␤-sheet secondary structure such as ␣-synuclein (FoderoTavoletti et al., 2007; Ye et al., 2008) and tau (Ikonomovic et al., 2008; Lockhart et al., 2007). Most importantly, these studies have shown that, at the concentrations achieved during a PET scan, 11 CPiB cortical brain retention primarily reflects A␤-related cerebral amyloidosis and not binding to Lewy bodies (LB) or NFT (FoderoTavoletti et al., 2007; Ikonomovic et al., 2008; Klunk et al., 2003; Lockhart et al., 2007). 11 C-PiB has consistently provided quantitative information on A␤ burden in vivo, contributing new insights into A␤ deposition in the brain, allowing earlier detection of AD pathology (Cohen et al., 2012; Klunk et al., 2004; Mintun et al., 2006; Rowe et al., 2007) and accurate differential diagnosis of the dementias (Ng et al., 2007; Rabinovici et al., 2007; Rowe et al., 2007). Post mortem studies using [11 C]PiB revealed high correlations with regional A␤ retention as measured at autopsy (Ikonomovic et al., 2012), as well as with A␤1-42 in CSF (Zwan et al., 2014). The second generation: worldwide access to amyloid imaging. Unfortunately, the 20-min radioactive decay half-life of carbon-11 (11 C) limits the use of 11 C-PiB to centers with an on-site cyclotron and 11 C radiochemistry expertise, making routine clinical use very expensive. To overcome these limitations, several A␤ tracers labeled with fluorine-18 (18 F; half-life of 110 min) were developed allowing centralized production and regional distribution as is routinely done with FDG. This second generation of 8 F labeled A␤-specific radiotracers, 18 F-florbetapir, (Clark et al., 2011; Wong et al., 2010), 18 F-florbetaben, (Barthel et al., 2011; Rowe et al., 2008; Villemagne et al., 2011a) and 18 F-flutemetamol, (Nelissen et al., 2009; Vandenberghe et al., 2010) replicated the results obtained with 11 C-PiB and expanded the access to amyloid imaging around the world. 18 F-florbetaben. 18 F-florbetaben, (a.k.a. 18 F-AV-1, 18 F-BAY-949172, Neuraceq® ) synthesized by Kung et al. (Zhang et al., 2005b) and developed by Bayer Healthcare and market by Piramal Imaging, binds with high affinity to A␤ in plaques and CAA in post-mortem brain tissue sections with lack of binding to Lewy bodies or NFT at low nanomolar concentrations (Fodero-Tavoletti et al., 2012; Zhang et al., 2005b). Florbetaben accurately disriminated AD from clinically normal controls and FTLD patients with high sensitivity

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Fig. 1. A␤ ligands in Alzheimer’s disease. Chemical structure of first (PiB), second (florbetapir, florbetaben, flutemetamol), and third (NAV4694) generation of A␤ ligands that have been used in clinical studies (left column). Surface projection showing the typical regional distribution of A␤ in the brain of AD patients. High tracer retention is observed in prefrontal, temporal and parietal cortices as well as in the anterior and posterior cingulate/precuneus areas, with relative sparing of occipital and sensori-motor cortex (right column).

and specificity to detect high A␤ burden against clinical diagnosis (Barthel et al., 2011; Rowe et al., 2008) and was able to rule out the presence of AD pathology in a mixed population of cognitive impaired subjects (Villemagne et al., 2011a). In MCI patients, [18 F]florbetaben was correlated with episodic memory and showed a predictive accuracy of 83% for conversion to AD after 2 years, and up to 94% after 4 years (Ong et al.). Global and regional 18 F-florbetaben retention was highly correlated with [11 C]PiB retention (Villemagne et al., 2012a). A multicenter phase 3 trail confirmed that florbetaben PET was able to detect cortical fibrillar A␤ plaques as assessed by visual reading with 98% sensitivity and 89% specificity sensitivity against histopathology (Sabri et al., 2015). [18 F]florbetaben received FDA and EMA approval for clinical use in February and March 2014, respectively. 18 F-florbetapir. As 18 F-florbetaben, 18 F-florbetapir (18 F-AV-45, Amyvid® ), is a stilbene derivative, also synthesized by Kung et al. at the University of Pennsylvania, (Zhang et al., 2005a) and developed by Avid Radiopharmaceuticals. Initial in vitro evaluation showed binding to A␤ plaques in AD brain sections (Choi et al., 2009; Lin et al., 2009). Florbetapir is characterized by its rapid reversible binding characteristics allowing scanning at just 45–50 min after injection, similar to 11 C-PiB (Wong et al., 2010). In a direct comparison study, [18 F]florbetapir was highly correlated with PiB (Landau et al., 2014) and found suitable to discriminate AD from controls (Wong et al., 2010). Several multicenter phase I and II studies along the AD spectrum have shown the ability of 18 F-florbetapir to discriminate between subjects with high or low 18 F-florbetapir retention (Fleisher et al., 2011). Multicenter studies using 18 Fflorbetapir to assess the relationship between A␤ burden and cognition, showed that in clinically normal older individuals A␤ burden in the brain is associated with poorer memory performance (Sperling et al., 2013) while MCI subjects with high A␤ burden in the brain are at a significantly higher risk of cognitive decline over 3 years (Doraiswamy et al., 2012, 2014). An initial Phase III study demonstrated a 96% agreement between 18 F-florbetapir and neuropathology for the detection of brain A␤ in vivo, and no retention in young controls (Clark et al., 2011). An extension of the same Phase III established 18 F-florbetapir has a 92% sensitivity and a 100% specificity for the detection of A␤ pathology (Clark et al., 2012), similar to the finding using a semiquantitative approach

(Camus et al., 2012). [18 F]Florbetapir was the first radiotracer—and the first 18 F labeled radiotracer approved by since FDG- approved by the Food and Drug Administration (FDA; April 2012) and the European Medicines Agency (EMA; January 2013) 18 F-florbetapir (Lister-James et al., 2011) has become the most widely used A␤ radiotracer after 11 C-PiB, and has been adopted as the A␤ radiotracer for ADNI2, and also used in patient selection and to evaluate treatment response in a large number of multicenter therapeutic trials around the world (Reiman et al., 2011; Sperling et al., 2014). 18 F-flutemetamol. [18 F]flutemetamol (a.k.a. GE–067, Vizamyl® ) is another second generation A␤ tracer, a fluoroderivative of [11 C]PiB developed by GE Healthcare (Serdons et al., 2009a,b). Phase I and II studies, demonstrated that 18 Fflutemetamol can robustly differentiate between AD and HC with a sensitivity and specificity of 93% (Nelissen et al., 2009; Vandenberghe et al., 2010) and that when combined with brain atrophy it could accurately predict disease progression in MCI subjects (Duara et al., 2012; Thurfjell et al., 2012). 18 F-flutemetamol brain retention is also highly correlated with 11 C-PIB (Vandenberghe et al., 2010), and with A␤ burden as measured immunohistochemical assessment of brain biopsy tissue (Wolk et al., 2011; Wong et al., 2012). A recently completed a phase III study established 18 F-flutemetamol has a 88% sensitivity and specificity for the detection of A␤ pathology (Curtis et al., 2015). 18 F]flutemetamol received FDA approval in October 2013 and EMA approval in September 2014 under the brand name of Vizamyl® The third generation: ironing creases. While the cortical retention of second generation fluorinated tracers—18 F-florbetapir, 18 Fflorbetaben, and 18 F-flutemetamol- yield a robust separation of AD from HC, the actual degree of cortical retention with 18 F-florbetapir and 18 F-florbetaben is lower than with 11 C-PiB, (Villemagne et al., 2012a; Wolk et al., 2012b) showing a narrower dynamic range of semiquantitative values, that visually appears as a relatively higher degree of non-specific binding to white matter. While the cortical retention of 18 F-flutemetamol is similar to that of 11 C-PiB, the nonspecific retention in white matter is much higher (Vandenberghe et al., 2010). Due to this lower signal-to-noise ratio, visual readouts of the images are more challenging than with 11 C-PiB (Rowe and Villemagne, 2011). While PiB PET images in subjects with AD

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pathology usually clearly show high radiotracer retention in the gray matter in excess of that in subjacent white matter, early cortical A␤ deposition of these second generation fluorinated tracers usually appears as a loss of the normal gray–white matter demarcation (Rowe and Villemagne, 2011). For novel A␤ radiotracers to be adopted by the wider community, they do have to really outperform the currently available tracers, or provide something unique that the others cannot deliver or deliver but not in a completely satisfactorily way. To our knowledge, only one radiofluorinated tracer seems to perform better that those in the previous generation. 18 F-NAV4694. Developed by Astra-Zeneca and subsequently by Navidea, 18 F–NAV4694 (a.k.a. 18 F-AZD4694) main features are fast tracer kinetics reaching aparent steady state about 40 min after injection of the tracer and, more importantly, low non-specific binding to white matter, similar to the one observed with 11 C-PiB, suggesting it might be helpful for the detection of small A␤ cortical deposits at very early stages of the disease process (Jureus et al., 2010; Sundgren-Andersson et al., 2009). Initial clinical studies assessing six healthy controls and ten AD patients both by visual inspection and quantitative measures showed a robust distinction in 18 F-NAV4694 retention between groups (Cselenyi et al., 2012). Direct comparison with [11 C]PiB revealed that [18 F]NAV4694 showed almost identical binding kinetics, dynamic retention range, and low non-specific binding to white matter with an excellent correlation between [18 F]NAV4694 and [11 C]PiB cortical retention (Rowe et al., 2008). While another Astra-Zeneca developed A␤ radiotracer, 11 C–AZD2138, displayed very low non-specific binding to white matter (Johnson et al., 2009; Nyberg et al., 2009), being radiolabeled with C-11 precludes its widespread, exactly the same way as with PiB.

3. A␤ imaging in Alzheimer’s disease To date, the vast majority of the A␤ imaging literature is based on 11 C-PiB studies, but with the advent and more frequent use of second generation tracers the landscape is changing. On visual inspection, as well as in quantitative and semiquantitative PET studies of the different A␤ radiotracers there is a marked difference in tracer retention between AD patients and age-matched controls (Barthel et al., 2011; Camus et al., 2012; Cselenyi et al., 2012; Fleisher et al., 2011; Furst et al., 2010; Jack et al., 2008; Klunk et al., 2004; Kudo et al., 2007; Mintun et al., 2006; Morris et al., 2009; Pike et al., 2007; Price et al., 2005; Rabinovici et al., 2007; Rowe et al., 2010, 2007; Villemagne et al., 2011a), with more than 90% of AD patients showing high cortical tracer retention higher in frontal, cingulate, precuneus, striatum, parietal, and lateral temporal cortices, while occipital, sensorimotor and mesial temporal cortices are much less affected (Fig. 1). These PET images present a pattern of radiotracer retention that seems to replicate the sequence of A␤ deposition described in autopsy studies, (Braak and Braak, 1997) but also similar to the anatomy of the ‘default network’ (Drzezga et al., 2011; Sperling et al., 2009). The regional distribution of these tracers varies with the characteristics of A␤ distribution among the different genotypes and phenotypes. For example, carriers of mutations associated with familial AD, (Klunk et al., 2007; Villemagne et al., 2009a) subjects with posterior cortical atrophy, (Ng et al., 2007; Tenovuo et al., 2008), or CAA (Dierksen et al., 2010; Johnson et al., 2007) presenting a different pattern of tracer retention to the one observed in sporadic AD. (Klunk et al., 2004; Rowe et al., 2007) While the accuracy of clinical assessments for the diagnosis of AD ranges between 70-90%, the regional retention of A␤ radiotracers is highly correlated with regional A␤ plaques as reported at autopsy or biopsy (Clark et al., 2011; Ikonomovic et al., 2008; Sojkova et al., 2011; Wolk et al., 2011), with higher A␤ burden in

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the frontal cortex than in hippocampus, consistent with previous reports (Naslund et al., 2000). About 25–35% of cognitively unimpaired elderly subjects present with high A␤ burden (Aizenstein et al., 2008; Mintun et al., 2006; Rowe et al., 2010, 2007; Villemagne et al., 2008), in perfect agreement with post mortem reports (Davies et al., 1988; Morris and Price, 2001), and likely to reflect preclinical AD (Price and Morris, 1999). While neuropsychological examination show that individuals with high A␤ burden perform within the normal limits, they do perform worse, and have a higher risk for disease progression than those with low A␤ burden (Rowe et al., 2013a; Sperling et al., 2013). The detection of A␤ pathology at the presymptomatic stage is of crucial importance because if this group truly represents preclinical AD, (Price and Morris, 1999; Thal et al., 2004) it is precisely the group that may benefit the most from therapies aimed at reducing or eliminating A␤ from the brain (Sperling et al., 2011). Furthermore, and one of the most important conclusions to be drawn from all these A␤ imaging studies, is that the likelihood of a cognitively unimpaired individual with low A␤ burden in the brain developing AD dementia is extremely small (Rowe et al., 2013a; Villemagne et al., 2011b). A␤ imaging has proven useful in identifying AD pathology in about 50–70% of individuals fulfilling criteria for MCI (Forsberg et al., 2008; Kemppainen et al., 2007; Mormino et al., 2009; Petersen, 2000; Pike et al., 2007; Winblad et al., 2004). Most studies reported that subjects classified as non-amnestic MCI −and more specifically those classified as non-amnestic single domain MCI- show low A␤ burden in the brain, consistent with a non-AD underlying pathological process (Pike et al., 2007; Villemagne et al., 2011b). Although at the early MCI stages A␤ and hippocampal volume have independent effects on cognition(Ong et al., 2013), at the late MCI stage, is thought that the A␤-related impairment is mediated by hippocampal atrophy, (Mormino et al., 2009; Ong et al., 2014) and modulated by cognitive reserve (Rentz et al., 2010; Roe et al., 2008). Both post-mortem (Bennett et al., 2006; Price and Morris, 1999) and A␤ imaging studies (Jack et al., 2013; Villemagne et al., 2013) indicate that A␤ deposition starts decades before the dementia phenotype is manifested, and non-demented individuals with high A␤ burden in the brain are at a much greater risk of cognitive decline (Resnick et al., 2010; Rowe et al., 2013a; Villemagne et al., 2011b; Villemagne et al., 2008), highlighting the clinical relevance and potential impact in patient management of A␤ imaging. The fact that high A␤ burden relates to episodic memory impairment in non-demented individuals, and that is associated with a significant higher risk of cognitive impairment, emphasizes the non-benign nature of A␤, and supports the hypothesis that A␤ deposition occurs well before the onset of symptoms, further suggesting that early disease-specific therapeutic intervention at the presymptomatic stage might be the most promising approach to either delay onset or halt disease progression (Sperling et al., 2011). On the other hand, the prevalence of high A␤ deposition among nondemented individuals, added to the absence of a strong association between A␤ deposition and measures of cognition, synaptic activity and neurodegeneration in AD, suggests that A␤ is an early and necessary, though not sufficient, cause for cognitive decline in AD, (Sojkova and Resnick, 2011; Villemagne et al., 2008) indicating that other -A␤-dependent or independent- factors, such as acceleration and/or spreading of preexisting tau aggregation beyond the MTL, lead to synaptic failure and eventually neuronal loss in cortical association areas, play a crucial role in cognitive decline and disease progression. Other factors such as age, years of education, IQ, occupational level, and brain volume among others, under the umbrella of “cognitive reserve”, appear to play a modulatory role between A␤ deposition and cognition (Chetelat et al., 2010; Roe et al., 2008). In regards to some of these environmental variables, age is the strongest risk factor in sporadic AD, with the prevalence of the dis-

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ease increasing exponentially with age. These risks are increased in the presence of the most consistent genetic risk factor associated with sporadic AD, the ApoE ␧4 allele (Farrer et al., 1997). The presence of the ApoE ␧4 allele has been associated with an earlier age of onset, and a gene dose dependent higher risk of developing AD (Farrer et al., 1997). Examination of ApoE ␧4 allele status revealed that, independent of clinical classification, ␧4 carriers present with significantly higher A␤ burdens than non-␧4 carriers, further emphasizing the crucial role ApoE plays in A␤ metabolism (Morris et al., 2010; Reiman et al., 2009; Rowe et al., 2010). Although ApoE ␧4 carriage is associated with higher A␤ burdens and early disease onset, it has no effect on the rates of A␤ deposition (Villemagne et al., 2013). A␤ imaging is also proving useful in the characterization of autosomal mutations in APP or presenilin 1 or 2 genes that lead to the overproduction of A␤, where mutation carriers present with abnormal A␤ deposition up to 25 years before the onset of dementia (Bateman et al., 2012; Benzinger et al., 2013). These studies are helping validate the relevance and change over time of several biomarkers (Benzinger et al., 2013; Fagan et al., 2014), assessments that are being applied to better-targeted disease specific therapeutic strategies (Mills et al., 2013). A␤ imaging has also been applied to a wide spectrum of neurodegenerative conditions. Although lower than in AD, similar patterns of A␤ deposition are usually observed in Dementia with Lewy bodies (DLB) (Gomperts et al., 2008; Maetzler et al., 2009; Rowe et al., 2007). While there is usually no detectable A␤ burden in patients with sporadic CJD (Villemagne et al., 2009b) or those diagnosed with FTLD (Drzezga et al., 2008; Rabinovici et al., 2007; Rowe et al., 2007) with the exception of patients presenting with logopenic aphasia that do have high A␤ burden in the brain, and are thought to represent a language-onset variant of AD (Leyton et al., 2011; Rabinovici et al., 2008). In contrast with FTLD, and as confirmed by autopsy and A␤ imaging studies, more than half of DLB patients present with high A␤ deposition (McKeith et al., 2005; Rowe et al., 2008). Therefore, differential diagnosis from AD can be better accomplished by assessing the integrity of the dopaminergic nigrostriatal terminals (Villemagne et al., 2012b). A␤ imaging has also facilitated differential diagnosis in cases of in patients with atypical presentations of dementia (Ng et al., 2007; Wolk et al., 2012a). Another rapidly expanding area is the examination of potential association between fluid and imaging biomarkers of pathology or neurodegeneration, and how these biomarker constructs are used to establish the criteria for the different stages of the disease (Albert et al., 2011; Clark et al., 2008; McKhann et al., 2011; Morris et al., 2005; Storandt et al., 2012; Thal et al., 2006). Clinical criteria for appropriate use of A␤ imaging has been established emphasizing the need to integrate the result of A␤ imaging with a comprehensive cognitive and clinical evaluation performed by a dementia specialist, to ensure a adequate patient management (Johnson et al., 2013). The criteria clearly stipulate the specific cases where A␤ imaging should be used, such as patients with persistent or progressive unexplained cognitive impairment, progressive atypical or unclear clinical presentation of dementia, or dementia onset in individuals 65 years or younger (Johnson et al., 2013). It also outlines the inappropriate use of A␤ imaging, such as cases of probable AD with typical age of onset or to determine dementia severity, asymptomatic individuals or unconfirmed cognitive complaint, a family history of dementia or presence of the ApoE ␧4 allele, as well as non-medical use such in litigation of for insurance purposes (Johnson et al., 2013). A␤ imaging is also assisting to the evaluation of anti-A␤ therapies in several ways, allowing better subject selection, assessing target engagement and evaluating treatment response of patients for therapy trials and providing a means to measure their impact

on A␤ burden (Ostrowitzki et al., 2011; Rinne et al., 2010; Salloway et al., 2014). 4. Future perspectives Alzheimer’s disease is a neurodegenerative disorder characterized by a slow but relentless progressive cognitive decline and memory loss. It has a devastating effect not only on the sufferer but also on their caregivers, with a tremendous socioeconomic impact not only on families but also on the health system, which will only increase in the upcoming years. The neuropathologic hallmarks of the disease are extracellular deposits of A␤ in senile plaques, NFT, with selective neuronal and synaptic loss in cortical areas of the brain associated with cognitive and memory functions. A␤ is the main component of the amyloid plaques. All the available evidence points at the breakdown of the economy of A␤ as playing the key role in AD pathogenesis. Genetic studies have shed light into the pathogenesis and progression of AD. To date four genes have been linked to autosomal dominant, early onset familial AD: APP, PS1, PS2 and ApoE. All mutations linked to APP and PS proteins lead to an increase in A␤ production. A␤ not only aggregates into amyloid plaques but is toxic per se, while having an effect on intracellular tangle formation and other factors (e.g., cytokines, neurotoxins, etc.) that also play an important role in the neurotoxic progression of AD. A␤ is neurotoxic through a number of possible mechanisms including oxidative stress, excitotoxicity, energy depletion, inflammatory response and apoptosis, and while the exact mediated mechanism by which A␤ might produce synaptic loss and neuronal death is controversial, it is believed that a toxic oxidative interaction between various metal species and A␤ triggers an oxidative response with free radical production leading to progressive disruption of neuronal function and ultimately to cell death. At this point there is no cure for AD. A deeper understanding of the molecular mechanism of A␤ formation, degradation and neurotoxicity is being translated into new neuroimaging and therapeutic approaches. Most of the approved palliative treatments regimens involve the use of acetylcholinesterase inhibitors, glutamatergic agents, nonsteroidal antiinflammatory drugs, as well as antioxidants. The most promising approaches focus on either reducing A␤ formation through secretase inhibitors or increasing its removal either by immunotherapy or MPAC aiming at blocking the formation of A␤ oligomers and fibrils therefore inhibiting neurotoxicity. Currently, the clinical diagnosis of AD is based on progressive impairment of memory and decline in at least one other cognitive domain, and by excluding other diseases with structural neuroimaging techniques (CT or MRI). This approach is sensitive and specific enough for the diagnosis of AD only at the mid or late stages of the disease. The development of a reliable method of assessing A␤ amyloid burden in vivo may permit early diagnosis at presymptomatic stages, more accurate differential diagnosis, while also allowing treatment follow up. Because new treatment strategies to prevent or slow disease progression through early-intervention are being developed and implemented, there is an urgent need for early disease recognition, which is reflected in the necessity of developing sensitive and specific biomarkers, specific for a particular trait underlying the pathological process, as adjuncts to clinical and neuropsychological tests. Non-invasive in vivo amyloid imaging might be able to address some of these important issues. Acknowledgements This work was supported in part by grant 1071430 of the National Health and Medical Research Council (NHMRC) of

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Australia. VLV is supported by the NHMRC through a Senior Research Fellowship.

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