Piramal (Germany)

BackgroundAlzheimer's Disease (AD) pathology accumulates early in the medial temporal lobe (MTL), crucial for spatial navigation. As spatial navigation is among the first cognitive functions affected by AD, it may benefit from targeted behavioral interventions. We investigated the potential of a novel smartphone‐assisted real‐world wayfinding training, tailored for healthy older adults, to improve their spatial abilities and explored associations with hippocampal vascularization and AD biomarkers.Method38 cognitively healthy older adults (62–84 years; 18 females) participated in a 3‐week navigation training, using our smartphone application “Explore” (Figure 1). Training involved finding several locations displayed on a map in the medical campus area of Magdeburg, Germany, while GPS data were recorded. Pre‐ and post‐training, participants underwent fMRI, performed a pointing task in a virtual campus version, and completed the VWLT. At pre‐assessment, AD pathology was characterized by plasma sampling (Abeta1‐42/1‐40, Ptau217) and [18F]PI‐2620 PET in a subsample. Hippocampal vascularization was assessed by 7T angiography. Performance in the virtual pointing task and a map drawing test was compared to a control group (n = 20) who performed a walking task of equal length without a navigational component. Additionally, changes in different mobile wayfinding performance indicators and their associations with AD biomarkers and hippocampal vascularization (i.e., mean distance of hippocampus to surrounding vessels) were examined.ResultPerformance in the pointing task and map drawing, but not in the VWLT (p = .321), significantly improved due to the training (all p < .001; Figure 2A C). The control group showed no improvements in navigation. Training benefits were also evident in the mobile data (all p ≤.017; Figure 3A‐E). Better wayfinding efficiency was associated with less vessel distance to hippocampus, r=.44, p = .012, and the number of orientation stops was negatively related to pTau217, r=‐.38, p = .019 (Figure 3F).ConclusionWe provide evidence that a remotely administered real‐world wayfinding training enhances wayfinding abilities and improves spatial memory in older adults. Importantly, hippocampal vascularization may benefit wayfinding efficiency. Higher pTau217 was related to fewer orientation stops during navigation. As a next step, potential mediating effects between vascularization and AD pathology on wayfinding performance will be investigated.

Abstract BackgroundThe prevention of symptomatic Alzheimer’s disease (AD) is a major endeavor currently in the field, with the prevalence of subjects on the AD continuum (i.e. presence of amyloid‐ß [Aß] pathology) being much higher than previously estimated. However, the extent to which Aß pathology and its interaction with common risk factors in the aging population relates to disease progression is still limited. The AMYPAD Prognostic and Natural History Study (PNHS) aims to evaluate the value of amyloid‐PET for predicting AD‐related disease progression.MethodA total of 1423 non‐demented subjects were included from the AMYPAD‐PNHS pan‐European cohort (17 sites across 10 Parent Cohorts), for who a baseline [18F]flutemetamol or [18F]florbetaben amyloid‐PET scans that passed quality control was available (Table‐1). Amyloid burden was assessed using the Centiloid (CL) method (whole cerebellum reference region). Independent linear mixed‐effect models were used to assess the predictive value of continuous CL, and its interaction with time, across cognitive domains: mini‐mental state examination (MMSE), memory, attention, language fluency, visuospatial, and executive functions. Years from baseline, age, sex, years of education, cognitive dementia rating (CDR), and APOE‐e4 were used as independent variables. Clinical follow‐up was 3.4 ± 1.8 years (up to ±10 years) on average.ResultAt baseline, 90% of the subjects were classified as cognitively unimpaired (CDR = 0). Based on CL, most subjects were categorized as amyloid‐negative (59%, 0±9CL), followed by grey‐zone (24%, 25±11CL), and amyloid‐positive (17%, 82±21CL). Baseline CL and its interaction with time were predictive of MMSE scores, memory, attention, and language fluency. Baseline CL, but not its interaction with time, was predictive of executive function. Finally, the interaction of baseline CL with time, but not its main effect, was predictive of visuospatial functioning (Figure‐1). Age and sex were significant contributors to nearly all models.ConclusionHigher baseline CL was associated with lower cognitive performance in all explored domains, suggesting that emerging cerebral amyloid accumulation in non‐demented individuals is predictive of overall cognitive function. Also, higher CL values were predictive of decline across cognitive domains, except for executive function. Further analysis is ongoing, including different confounding factors, longitudinal PET outcomes, and other biomarkers of the disease.

The first candidate PI-2014 was tested in healthy controls and subjects with Alzheimer's disease (AD). As PI-2014 displayed off-target binding to monoamine oxidase A (MAO-A), a new lead with improved binding to Tau and decreased MAO-A binding was required. For compound optimization, Tau binding assays based on both human AD brain homogenate and Tau-paired helical filaments were employed. Furthermore, two MAO-A screening assays based on (1) human-recombinant MAO-A and (2) displacement of 2-fluoro-ethyl-harmine from mouse brain homogenate were employed. Removing the N-methyl group from the tricyclic core resulted in compounds displaying improved Tau binding. For the final round of optimization, the cyclic amine substituents were replaced by pyridine derivatives. PI-2620 (2-(2-fluoropyridin-4-yl)-9H-pyrrolo[2,3-b:4,5-c']dipyridine) emerged as a best candidate displaying high Tau binding, low MAO-A binding, high brain uptake, and fast and complete brain washout. Furthermore, PI-2620 showed Tau binding on brain sections from corticobasal degeneration, progressive supranuclear palsy, and Pick's disease.

IntroductionStandardized uptake value ratios (SUVRs) calculated from cerebral cortical areas can be used to categorize 18F-Florbetaben (FBB) PET scans by applying appropriate cutoffs. The objective of this work was first to generate FBB SUVR cutoffs using visual assessment (VA) as standard of truth (SoT) for a number of reference regions (RR) (cerebellar gray matter (GCER), whole cerebellum (WCER), pons (PONS), and subcortical white matter (SWM)). Secondly, to validate the FBB PET scan categorization performed by SUVR cutoffs against the categorization made by post-mortem histopathological confirmation of the Aβ presence. Finally, to evaluate the added value of SUVR cutoff categorization to VA.MethodsSUVR cutoffs were generated for each RR using FBB scans from 143 subjects who were visually assessed by 3 readers. SUVR cutoffs were validated in 78 end-of life subjects using VA from 8 independent blinded readers (3 expert readers and 5 non-expert readers) and histopathological confirmation of the presence of neuritic beta-amyloid plaques as SoT. Finally, the number of correctly or incorrectly classified scans according to pathology results using VA and SUVR cutoffs was compared.ResultsComposite SUVR cutoffs generated were 1.43 (GCER), 0.96 (WCER), 0.78 (PONS) and 0.71 (SWM). Accuracy values were high and consistent across RR (range 83–94% for histopathology, and 85–94% for VA). SUVR cutoff performed similarly as VA but did not improve VA classification of FBB scans read either by expert readers or the majority read but provided higher accuracy than some non-expert readers.ConclusionThe accurate scan classification obtained in this study supports the use of VA as SoT to generate site-specific SUVR cutoffs. For an elderly end of life population, VA and SUVR cutoff categorization perform similarly in classifying FBB scans as Aβ-positive or Aβ-negative. These results emphasize the additional contribution that SUVR cutoff classification may have compared with VA performed by non-expert readers.

PurposeThe Centiloid (CL) method enables quantitative values from Aβ-amyloid (Aβ) imaging to be expressed in a universal unit providing pathological, diagnostic and prognostic thresholds in clinical practice and research and allowing integration of multiple tracers and methods. The method was developed for 11C-PiB scans with zero CL set as the average in young normal subjects and 100 CL the average in subjects with mild Alzheimer’s disease (AD). The method allows derivation of equations to convert the uptake value of any tracer into the same standard CL units but first requires head-to-head comparison with 11C-PiB results. We derived the equation to express 18F-florbetaben (FBB) binding in CL units.MethodsPaired PiB and FBB PET scans were obtained in 35 subjects. including ten young normal subjects aged under 45 years (33 ± 8 years). FBB images were acquired from 90 to 110 min after injection. Spatially normalized images were analysed using the standard CL method (SPM8 coregistration of PET data to MRI data and the MNI-152 atlas) and standard CL regions (cortex and whole cerebellum downloaded from http://www.gaain.org).ResultsFBB binding was strongly correlated with PiB binding (R2 = 0.96, SUVRFBB = 0.61 × SUVRPiB + 0.39). The equation to derive CL values from FBB SUVR was CL units = 153.4 × SUVRFBB − 154.9. The CL value in the young normal subjects was −1.08 ± 6.81 for FBB scans compared to −0.32 ± 3.48 for PiB scans, giving a variance ratio of 1.96 (SDFBB CL/SDPiB CL).Conclusions18F-FBB binding is strongly correlated with PiB binding and FBB results can now be expressed in CL units.