PURPOSE: Complementing clinical findings with those generated by biomarkers--such as β-amyloid-targeted positron emission tomography (PET) imaging--has been proposed as a means of increasing overall accuracy in the diagnosis of Alzheimer's disease (AD). Florbetaben ([(18)F]BAY 94-9172) is a novel β-amyloid PET tracer currently in global clinical development. We present the results of a proof of mechanism study in which the diagnostic efficacy, pharmacokinetics, safety and tolerability of florbetaben were assessed. The value of various quantitative parameters derived from the PET scans as potential surrogate markers of cognitive decline was also investigated. METHODS: Ten patients with mild-moderate probable AD (DSM-IV and NINCDS-ADRDA criteria) and ten age-matched (≥ 55 years) healthy controls (HCs) were administered a single dose of 300 MBq florbetaben, which contained a tracer mass dose of < 5 μg. The 70-90 min post-injection brain PET data were visually analysed by three blinded experts. Quantitative assessment was also performed via MRI-based, anatomical sampling of predefined volumes of interest (VOI) and subsequent calculation of standardized uptake value (SUV) ratios (SUVRs, cerebellar cortex as reference region). Furthermore, single-case, voxelwise analysis was used to calculate individual "whole brain β-amyloid load". RESULTS: Visual analysis of the PET data revealed nine of the ten AD, but only one of the ten HC brains to be β-amyloid positive (p = 0.001), with high inter-reader agreement (weighted kappa ≥ 0.88). When compared to HCs, the neocortical SUVRs were significantly higher in the ADs (with descending order of effect size) in frontal cortex, lateral temporal cortex, occipital cortex, anterior and posterior cingulate cortices, and parietal cortex (p = 0.003-0.010). Voxel-based group comparison confirmed these differences. Amongst the PET-derived parameters, the Statistical Parametric Mapping-based whole brain β-amyloid load yielded the closest correlation with the Mini-Mental State Examination scores (r = -0.736, p < 0.001), following a nonlinear regression curve. No serious adverse events or other safety concerns were seen. CONCLUSION: These results indicate florbetaben to be a safe and efficacious β-amyloid-targeted tracer with favourable brain kinetics. Subjects with AD could be easily differentiated from HCs by both visual and quantitative assessment of the PET data. The operator-independent, voxel-based analysis yielded whole brain β-amyloid load which appeared valuable as a surrogate marker of disease severity.
PURPOSE: Complementing clinical findings with those generated by biomarkers--such as β-amyloid-targeted positron emission tomography (PET) imaging--has been proposed as a means of increasing overall accuracy in the diagnosis of Alzheimer's disease (AD). Florbetaben ([(18)F]BAY 94-9172) is a novel β-amyloid PET tracer currently in global clinical development. We present the results of a proof of mechanism study in which the diagnostic efficacy, pharmacokinetics, safety and tolerability of florbetaben were assessed. The value of various quantitative parameters derived from the PET scans as potential surrogate markers of cognitive decline was also investigated. METHODS: Ten patients with mild-moderate probable AD (DSM-IV and NINCDS-ADRDA criteria) and ten age-matched (≥ 55 years) healthy controls (HCs) were administered a single dose of 300 MBq florbetaben, which contained a tracer mass dose of < 5 μg. The 70-90 min post-injection brain PET data were visually analysed by three blinded experts. Quantitative assessment was also performed via MRI-based, anatomical sampling of predefined volumes of interest (VOI) and subsequent calculation of standardized uptake value (SUV) ratios (SUVRs, cerebellar cortex as reference region). Furthermore, single-case, voxelwise analysis was used to calculate individual "whole brain β-amyloid load". RESULTS: Visual analysis of the PET data revealed nine of the ten AD, but only one of the ten HC brains to be β-amyloid positive (p = 0.001), with high inter-reader agreement (weighted kappa ≥ 0.88). When compared to HCs, the neocortical SUVRs were significantly higher in the ADs (with descending order of effect size) in frontal cortex, lateral temporal cortex, occipital cortex, anterior and posterior cingulate cortices, and parietal cortex (p = 0.003-0.010). Voxel-based group comparison confirmed these differences. Amongst the PET-derived parameters, the Statistical Parametric Mapping-based whole brain β-amyloid load yielded the closest correlation with the Mini-Mental State Examination scores (r = -0.736, p < 0.001), following a nonlinear regression curve. No serious adverse events or other safety concerns were seen. CONCLUSION: These results indicate florbetaben to be a safe and efficacious β-amyloid-targeted tracer with favourable brain kinetics. Subjects with AD could be easily differentiated from HCs by both visual and quantitative assessment of the PET data. The operator-independent, voxel-based analysis yielded whole brain β-amyloid load which appeared valuable as a surrogate marker of disease severity.
Authors: Brian J Lopresti; William E Klunk; Chester A Mathis; Jessica A Hoge; Scott K Ziolko; Xueling Lu; Carolyn C Meltzer; Kurt Schimmel; Nicholas D Tsopelas; Steven T DeKosky; Julie C Price Journal: J Nucl Med Date: 2005-12 Impact factor: 10.057
Authors: Juha O Rinne; David J Brooks; Martin N Rossor; Nick C Fox; Roger Bullock; William E Klunk; Chester A Mathis; Kaj Blennow; Jerome Barakos; Aren A Okello; Sofia Rodriguez Martinez de Liano; Enchi Liu; Martin Koller; Keith M Gregg; Dale Schenk; Ronald Black; Michael Grundman Journal: Lancet Neurol Date: 2010-02-26 Impact factor: 44.182
Authors: Ansgar J Furst; Gil D Rabinovici; Ara H Rostomian; Tyler Steed; Adi Alkalay; Caroline Racine; Bruce L Miller; William J Jagust Journal: Neurobiol Aging Date: 2010-04-24 Impact factor: 4.673
Authors: Kerryn E Pike; Greg Savage; Victor L Villemagne; Steven Ng; Simon A Moss; Paul Maruff; Chester A Mathis; William E Klunk; Colin L Masters; Christopher C Rowe Journal: Brain Date: 2007-10-10 Impact factor: 13.501
Authors: A Drzezga; T Grimmer; G Henriksen; M Mühlau; R Perneczky; I Miederer; C Praus; C Sorg; A Wohlschläger; M Riemenschneider; H J Wester; H Foerstl; M Schwaiger; A Kurz Journal: Neurology Date: 2009-04-01 Impact factor: 9.910
Authors: N M Scheinin; S Aalto; J Koikkalainen; J Lötjönen; M Karrasch; N Kemppainen; M Viitanen; K Någren; S Helin; M Scheinin; J O Rinne Journal: Neurology Date: 2009-09-02 Impact factor: 9.910
Authors: L A Farrer; L A Cupples; J L Haines; B Hyman; W A Kukull; R Mayeux; R H Myers; M A Pericak-Vance; N Risch; C M van Duijn Journal: JAMA Date: 1997 Oct 22-29 Impact factor: 56.272
Authors: Zhaolin Chen; Sharna D Jamadar; Shenpeng Li; Francesco Sforazzini; Jakub Baran; Nicholas Ferris; Nadim Jon Shah; Gary F Egan Journal: Hum Brain Mapp Date: 2018-08-04 Impact factor: 5.038
Authors: Young Kyoung Jang; Chul Hyoung Lyoo; Seongbeom Park; Seung Jun Oh; Hanna Cho; Minyoung Oh; Young Hoon Ryu; Jae Yong Choi; Gil D Rabinovici; Hee Jin Kim; Seung Hwan Moon; Hyemin Jang; Jin San Lee; William J Jagust; Duk L Na; Jae Seung Kim; Sang Won Seo Journal: Eur J Nucl Med Mol Imaging Date: 2017-11-16 Impact factor: 9.236
Authors: A P Nayate; J G Dubroff; J E Schmitt; I Nasrallah; R Kishore; D Mankoff; D A Pryma Journal: AJNR Am J Neuroradiol Date: 2015-03-12 Impact factor: 3.825