Design semi-automated radio-synthesis strategy and synthesis of benzimidazole-based radiotracer 2-(4-(2-(fluoro-18F) ethyl) piperidin-1-yl) benzo imidazo[1,2-a] pyrimidine for Tau as a PET imaging agent

IntroductionTau pathology is a major age-related event in Down syndrome with Alzheimer’s disease (DS-AD). Although recently, several different Tau PET tracers have been developed as biomarkers for AD, these tracers showed different binding properties in Alzheimer disease and other non-AD tauopathies. They have not been yet investigated in tissue obtained postmortem for DS-AD cases. Here, we evaluated the binding characteristics of two Tau PET tracers (3H-MK6240 and 3H-THK5117) and one amyloid (3H-PIB) ligand in the medial frontal gyrus (MFG) and hippocampus (HIPP) in tissue from adults with DS-AD and DS cases with mild cognitive impairment (MCI) compared to sporadic AD.MethodsTau and amyloid autoradiography were performed on paraffin-embedded sections. To confirm respective ligand targets, adjacent sections were immunoreacted for phospho-Tau (AT8) and stained for amyloid staining using Amylo-Glo.ResultsThe two Tau tracers showed a significant correlation with each other and with AT8, suggesting that both tracers were binding to Tau deposits. 3H-MK6240 Tau binding correlated with AT8 immunostaining but to a lesser degree than the 3H-THK5117 tracer, suggesting differences in binding sites between the two Tau tracers. 3H-THK5117, 3H-MK6240 and 3H-PIB displayed dense laminar binding in the HIPP and MFG in adult DS brains. A regional difference in Tau binding between adult DS and AD was observed suggesting differential regional Tau deposition in adult DS compared to AD, with higher THK binding density in the MFG in adult with DS compared to AD. No significant correlation was found between 3H-PIB and Amylo-Glo staining in adult DS brains suggesting that the amyloid PIB tracer binds to additional sites.ConclusionsThis study provides new insights into the regional binding distribution of a first-generation and a second-generation Tau tracer in limbic and neocortical regions in adults with DS, as well as regional differences in Tau binding in adult with DS vs. those with AD. These findings provide new information about the binding properties of two Tau radiotracers for the detection of Tau pathology in adults with DS in vivo and provide valuable data regarding Tau vs. amyloid binding in adult DS compared to AD.

BackgroundTau phosphorylation and aggregation are hallmark features of Alzheimer's disease pathology(AD). Previous studies have shown that plasma phosphorylated tau(p‐tau) at threonine 217 can detect AD‐related tau tangle pathology. However, the head‐to‐head association between p‐tau217 and different tau PET tracers remains unexplored. Here, we conducted a head‐to‐head comparison of the association between plasma p‐tau217 and four distinct tau PET tracers[MK6240, Flortaucipir(FTP), PI2620, RO948].MethodWe studied 338 individuals across the AD continuum from the HEAD study[190 cognitively unimpaired elderly(CU), 109 with mild cognitive impairment(MCI), and 39 with dementia] with available FTP, MK6240, and plasma p‐tau217 measured by ALZpath assay. A subset of 64 individuals(28 CU, 25 MCI, and 11 dementia) also had PI2620 and RO948, and p‐tau217 measured by Lumipulse. The strength of the association was determined using t‐value maps from regression models testing the association between each tau PET tracer and plasma p‐tau217, and the extent was measured by the percentage of significant voxels(t‐value>3.2). R‐square metric was used to determine how much of the variance in tau PET tracer estimates was explained by plasma p‐tau217 concentrations. Associations between p‐tau217 with tau PET were evaluated using the Spearman test.ResultWe found that in CU, both the extent and magnitude of the association with plasma p‐tau217 were higher for MK6240 compared to FTP (Figures 1A‐D). Plasma p‐tau217 explained a greater proportion of the variance in MK6240 than in FTP (Figures 1E‐F). In CI, the extent and magnitude of the association were similar for MK6240 and FTP. Additionally, plasma p‐tau217 explained a similar proportion of the variance in MK6240 and FTP. Using the subset of individuals who had all tau tracers, MK6240 showed a slightly higher extent of association with p‐tau217, followed by FTP, PI2620, and RO948 (Figures 2A‐B). The magnitude of association was stronger for MK6240, followed by PI2620, RO948, and FTP (Figures 2C‐D). Plasma p‐tau217 explained a greater proportion of the variance in MK6240, followed by FTP, RO948, and PI2620 (Figures 2E‐F). We compared the associations between different p‐tau217 assays, and both assays presented similar associations with all tau PET tracers (Figure 3).ConclusionIn summary, our results indicate that the four tau PET tracers exhibit robust associations with plasma p‐tau217 in similar topographical regions.

BackgroundTau phosphorylation and aggregation are hallmark features of Alzheimer's disease pathology(AD). Previous studies have shown that plasma phosphorylated tau(p‐tau) at threonine 217 can detect AD‐related tau tangle pathology. However, the head‐to‐head association between p‐tau217 and different tau PET tracers remains unexplored. Here, we conducted a head‐to‐head comparison of the association between plasma p‐tau217 and four distinct tau PET tracers[MK6240, Flortaucipir(FTP), PI2620, RO948].MethodWe studied 338 individuals across the AD continuum from the HEAD study[190 cognitively unimpaired elderly(CU), 109 with mild cognitive impairment(MCI), and 39 with dementia] with available FTP, MK6240, and plasma p‐tau217 measured by ALZpath assay. A subset of 64 individuals(28 CU, 25 MCI, and 11 dementia) also had PI2620 and RO948, and p‐tau217 measured by Lumipulse. The strength of the association was determined using t‐value maps from regression models testing the association between each tau PET tracer and plasma p‐tau217, and the extent was measured by the percentage of significant voxels(t‐value>3.2). R‐square metric was used to determine how much of the variance in tau PET tracer estimates was explained by plasma p‐tau217 concentrations. Associations between p‐tau217 with tau PET were evaluated using the Spearman test.ResultWe found that in CU, both the extent and magnitude of the association with plasma p‐tau217 were higher for MK6240 compared to FTP (Figure 1A‐D). Plasma p‐tau217 explained a greater proportion of the variance in MK6240 than in FTP (Figure 1E‐F). In CI, the extent and magnitude of the association were similar for MK6240 and FTP. Additionally, plasma p‐tau217 explained a similar proportion of the variance in MK6240 and FTP. Using the subset of individuals who had all tau tracers, MK6240 showed a slightly higher extent of association with p‐tau217, followed by FTP, PI2620, and RO948 (Figure 2A‐B). The magnitude of association was stronger for MK6240, followed by PI2620, RO948, and FTP (Figure 2C‐D). Plasma p‐tau217 explained a greater proportion of the variance in MK6240, followed by FTP, RO948, and PI2620 (Figure 2E‐F). We compared the associations between different p‐tau217 assays, and both assays presented similar associations with all tau PET tracers (Figure 3).ConclusionIn summary, our results indicate that the four tau PET tracers exhibit robust associations with plasma p‐tau217 in similar topographical regions.

BackgroundDeep learning models, particularly convolutional neural networks (CNNs), have shown promise in Alzheimer’s disease (AD) classification using tau PET data. However, limited sample sizes and unharmonized tau tracers present challenges to developing an agnostic tau tracer tool to predict AD using machine learning. Transfer learning, which leverages pre‐trained models for related tasks, may address these issues. Here we evaluate the effectiveness of transfer learning in optimizing 3D CNNs for AD classification with distinct cohorts and tau tracers.MethodWe used tau PET images from ADNI ([18F]Flortaucipir, n = 437) and TRIAD ([18F]MK‐6240, n = 423) cohorts, categorizing patients into CU (cognitively unimpaired) and CI (cognitively impaired). Standardized uptake value ratios (SUVR) were used for tau PET data. Separate 3D CNNs were trained for each tracer, with SUVR volumes as input and diagnosis as output. For transfer learning, we trained a model on [18F]Flortaucipir data with a reduced learning rate, using a pre‐trained model from [18F]MK‐6240. Models underwent 5‐fold cross‐validation, and metrics were computed as the average of validation metrics across folds. To avoid data leakage, images from the same subject were assigned to the same fold.ResultThe model trained on [18F]MK‐6240 tracer demonstrated higher classification performance than [18F]Flortaucipir (AUC = 0.84 vs 0.67; Figure 1. F1‐score = 79.77% vs 64.66%; Table 1). To enhance the classification performance of [18F]Flortaucipir model, we employed a transfer learning approach by leveraging the model pre‐trained with [18F]MK‐6240. With this approach, we observed a slight improvement in all classification metrics compared to the model trained solely on [18F]Flortaucipir data (AUC = 0.71 vs 0.67; Figure 1. Accuracy = 71.39% vs 67.72, F1‐score = 67.97% vs 64.66%; Table 1).ConclusionThis finding highlights the value of transfer learning in optimizing deep learning models for Alzheimer’s disease classification, particularly when handling tau tracers with varying performance levels. Our results are consistent with previous on transfer learning’s effectiveness in this context. These preliminary findings indicate that applying this technique to larger datasets of tau tracers may further enhance model performance, potentially leading to the development of a tau tracer‐agnostic tool that overcomes the need of tracer harmonization for predicting dementia.

Abnormal accumulation of Tau protein is closely associated with neurodegeneration and cognitive impairment and it is a biomarker of neurodegeneration in the dementia field, especially in Alzheimer’s disease (AD); therefore, it is crucial to be able to assess the Tau deposits in vivo. Beyond the fluid biomarkers of tauopathy described in this review in relationship with the brain glucose metabolic patterns, this review aims to focus on tauopathy assessment by using Tau PET imaging. In recent years, several first-generation Tau PET tracers have been developed and applied in the dementia field. Common limitations of first-generation tracers include off-target binding and subcortical white-matter uptake; therefore, several institutions are working on developing second-generation Tau tracers. The increasing knowledge about the distribution of first- and second-generation Tau PET tracers in the brain may support physicians with Tau PET data interpretation, both in the research and in the clinical field, but an updated description of differences in distribution patterns among different Tau tracers, and in different clinical conditions, has not been reported yet. We provide an overview of first- and second-generation tracers used in ongoing clinical trials, also describing the differences and the properties of novel tracers, with a special focus on the distribution patterns of different Tau tracers. We also describe the distribution patterns of Tau tracers in AD, in atypical AD, and further neurodegenerative diseases in the dementia field.

BackgroundTau PET is useful for in vivo diagnosis of Alzheimer’s disease (AD) and may be useful in detecting non‐AD tauopathies. Diagnostic applications vary by tracer and AD disease stage.MethodThis presentation will review existing literature on the diagnostic utility of tau PET tracers across the AD continuum and in non‐AD tauopathies. Data from in vitro (autoradiography, brain homogenate binding) and in vivo studies will be reviewed, with an emphasis on large clinical studies and PET‐to‐autopsy correlations.ResultIn vitro and in vivo data suggest that [18]FTP, [18F]MK6240, [18F]GTP‐1, [18F]RO948 and [18F]JNJ‐067 are AD‐selective tau tracers, while [18F]PI2620 and [18F]PM‐PBB3 (also known as [18F]APN‐1607) may bind to non‐AD tauopathies. FTP‐PET quantification shows high accuracy in differentiating AD dementia from other disorders. In a multi‐site study conducted in Lund, Seoul and UCSF (N=719), FTP SUVR in in a temporal meta‐ROI showed 89.9% sensitivity and 90.6% specificity for AD dementia versus non‐AD disorders. Sensitivity in Aβ+ MCI was lower (61.5%) with similar specificity (90.6%). RO948 PET in the Swedish BioFinder‐2 study (N=613) showed similar diagnostic performance, though sensitivity in Aβ+ MCI was lower (37.5%). Data from Mayo Clinic (N=1,281) revealed that Aβ‐PET is more sensitive than FTP‐PET for detecting the AD continuum in cognitively unimpaired (29% of subjects Aβ+ vs. 5% tau+) and MCI (55% Aβ+ vs. 30% tau+), while sensitivity was similar in clinical AD dementia (97% Aβ+ vs. 89% tau+). PET‐to‐autopsy data from the Avid A16 trial, Mayo Clinic and UCSF cohorts show high sensitivity and specificity for FTP‐PET visual reads and quantification in detecting Braak Stages V‐VI, but low sensitivity for Braak Stages I‐IV. Antemortem FTP binding in these cohorts showed poor correlation with non‐AD tau pathology. In a multi‐site German study, [18F]PI‐2620 showed 77% sensitivity and specificity in discriminating Progressive Supranuclear Palsy (N=60) from disease (N=20) and healthy (N=10) controls.ConclusionTau PET is most useful in differentiating AD from other causes of dementia but is less sensitive in detecting preclinical and prodromal AD, which are associated with earlier Braak stages. While most tau PET tracers are AD‐specific, [18F]PI2620 and [18F]PM‐PBB3 warrant further study in non‐AD tauopathies.

PurposeMK6240 is a second-generation tau PET tracer designed to detect the neurofibrillary tangles in the brains of patients with Alzheimer’s disease (AD). The aim of the study was to characterize 3H-MK6240 in AD and control brain tissue and to compare its binding properties with those of first-generation tau PET tracers.MethodsSaturation binding assays with 3H-MK6240 were carried out in the temporal and parietal cortices of AD brains to determine the maximum number of binding sites (Bmax) and the dissociation constants (Kd) at these sites. Competitive binding assays were carried out between 3H-MK6240 and unlabelled MK6240, AV-1451 (aka T807, flortaucipir) and THK5117, and between 3H-THK5351 and unlabelled MK6240. Regional binding studies with 3H-MK6240 were carried out in homogenates from six AD and seven control brains and, using autoradiography, on large frozen sections from two AD brains and one control brain.ResultsThe saturation binding assays gave Bmax and Kd values of 59.2 fmol/mg and 0.32 nM in the temporal cortex and 154.7 fmol/mg and 0.15 nM in the parietal cortex. The competitive binding assays revealed two binding sites with affinities in the picomolar and nanomolar range shared by 3H-MK6240 and all the tested unlabelled compounds. There were no binding sites in common between 3H-THK5351 and unlabelled MK6240. Regional binding of 3H-MK6240 was significantly higher in AD brain tissue than in controls. Binding in brain tissue from AD patients with early-onset AD was significantly higher than in brain tissue from patients with late-onset AD. Binding of 3H-MK6240 was not observed in off-target regions. Autoradiography showed high regional cortical binding in the two AD brains and very low binding in the control brain.Conclusions3H-MK6240 has a high binding affinity for tau deposits in AD brain tissue but also has different binding characteristics from those of the first-generation tau tracers. This confirms the complexity of tau tracer binding on tau deposits with different binding affinities for different binding sites.

BackgroundRecent CryoEM studies in tauopathies clearly bought forward one more time the complexity of these pathologies, and showed that it goes beyond the tau isoforms. Tau PET tracers play an important role in the understanding of this aspect. We have observed in post‐mortem brain tissue from Alzheimer’s disease (AD) patients that the second‐generation of tau PET tracers 3H‐PI2620, 3H‐MK6240, 3H‐RO948, all show similar regional distribution, but also comparable binding affinities to multiple binding sites in AD brain large hemispherical tissue section autoradiography and homogenates. In vitro autoradiography can mirror the in vivo binding pattern of PET tracers and be useful in pre‐clinical validations. The aim of this study was to perform a comparative multi‐tracer study in AD, PSP, CBD and control brains with several second generation tau‐tracers to obtain a deeper insight into tau spatial distribution and discriminative power of tau tracers in degenerative proteinopathies. We also want to study how the binding of the tracers correlate with other pathological hallmarks such as amyloid‐β and reactive astrogliosis.Methodswe performed radioligand binding assays in brain homogenates, in small tissue sections and large frozen hemispherical brain sections autoradiography using tau PET tracers (3H‐PI2620, 3H‐MK6240, 3H‐RO948, and 3H‐JNJ067) in comparison to reactive astrogliosis PET tracers (3H‐BU99008 and 3H‐Deprenyl) and amyloid tracer (3H‐PIB).ResultsIn ongoing studies, 3H‐PI2620, 3H‐MK6240 and 3H‐RO948 clearly followed a two binding sites model in AD brains. 3H‐PI2620 bound to two binding sites in the frontal cortex of both AD and CBD but to one site in PSP brains, but in similar high affinity range in the three pathologies. 3H‐RO948 and 3H‐MK6240 showed low binding in comparison to 3H‐PI2620 in CBD and PSP.ConclusionTau PET tracers showed similar affinities to multiple binding sites and similar regional distribution in AD brains. 3H‐MK6240 and 3H‐RO948 displayed selectivity for AD brains, while 3H‐PI2620 also showed similar binding affinity for CBD and PSP brains. Ongoing studies with several PET tracers targeting different pathological hallmarks will increase possible discriminative properties and provide new knowledge on the spatial distribution of tau and its correlation with other biomarkers in different tauopathies.

BackgroundThe first‐generation tau PET tracer AV‐1451 has been widely used to study tau pathology in Alzheimer's disease (AD), but it's high off‐target binding in sub‐cortical structures has limited our ability to examine possible tau pathology in these structures. With the increasing popularity of second‐generation tau PET tracers such as PI‐2620 and MK‐6240 that exhibit low off‐target binding signals in sub‐cortical structures, it is important to examine whether their signals can be useful in evaluating the severity of tau pathology of sub‐cortical structures in AD.MethodThis study utilized tau PET data based on the PI‐2620 tracer from the Health and Aging Brain Study: Health Disparities (HABS‐HD). Overall, data used in this study are from 1,492 participants with both T1‐weighted MRI and tau PET imaging, along with the available clinical and cognitive metrics such as the Consensus Diagnosis (CDX). The cohort included 1097 cognitively unimpaired (CU) and 395 cognitively impaired (CI) participants, which are comprised of 479 Black, 447 Hispanic, and 566 non‐Hispanic White subjects. Subcortical regions of interest included key structures from both the left and right hemispheres including the thalamus, caudate, putamen, pallidum, hippocampus, amygdala, and accumbens. Automated segmentation maps were generated from T1‐weighted MRI using FreeSurfer and applied to the corresponding PET images to calculate mean standard uptake value ratio (SUVR) values for each subcortical region.ResultSignificant differences in tau SUVR values were observed in the caudate (p‐value < 1e‐2), hippocampus (p‐value < 1e‐2), and amygdala (p‐value < 1e‐4) across diagnostic categories in the entire cohort. Ethnic group analyses for CU and CI subjects revealed statistically significant differences in different sub‐cortical regions. Among non‐Hispanic White participants, differences were significant in the putamen (p‐value < 1e‐2), hippocampus (p‐value < 1e‐2), and amygdala (p‐value < 1e‐5). Significant differences were observed in the caudate (p‐value < 1e‐2) for Black participants, while the amygdala showed statistically significant differences (p‐value < 5e‐2) for Hispanic participants.ConclusionEthnicity plays an important role on sub‐cortical tau PET signal for CU and CI, especially for non‐Hispanic White in the amygdala area.Funding Sources: RF1AG077578, RF1AG064584, R01EB022744, U19AG078109, P30AG066530.

Tau PET tracers are expected to be sufficiently sensitive to track the progression of age-related tau pathology in the medial temporal cortex. The tau PET tracer N-(4-[18F]fluoro-5-methylpyridin-2-yl)-7-aminoimidazo[1,2-a]pyridine ([18F]SNFT-1) has been successfully developed by optimizing imidazo[1,2-a]pyridine derivatives. We characterized the binding properties of [18F]SNFT-1 using a head-to-head comparison with other reported 18F-labeled tau tracers. Methods: The binding affinity of SNFT-1 to tau, amyloid, and monoamine oxidase A and B was compared with that of the second-generation tau tracers MK-6240, PM-PBB3, PI-2620, RO6958948, JNJ-64326067, and flortaucipir. In vitro binding properties of 18F-labeled tau tracers were evaluated through the autoradiography of frozen human brain tissues from patients with diverse neurodegenerative disease spectra. Pharmacokinetics, metabolism, and radiation dosimetry were assessed in normal mice after intravenous administration of [18F]SNFT-1. Results: In vitro binding assays demonstrated that [18F]SNFT-1 possesses high selectivity and high affinity for tau aggregates in Alzheimer disease (AD) brains. Autoradiographic analysis of tau deposits in medial temporal brain sections from patients with AD showed a higher signal-to-background ratio for [18F]SNFT-1 than for the other tau PET tracers and no significant binding with non-AD tau, α-synuclein, transactiviation response DNA-binding protein-43, and transmembrane protein 106B aggregates in human brain sections. Furthermore, [18F]SNFT-1 did not bind significantly to various receptors, ion channels, or transporters. [18F]SNFT-1 showed a high initial brain uptake and rapid washout from the brains of normal mice without radiolabeled metabolites. Conclusion: These preclinical data suggest that [18F]SNFT-1 is a promising and selective tau radiotracer candidate that allows the quantitative monitoring of age-related accumulation of tau aggregates in the human brain.

18F-PI-2620 is a PET tracer with high binding affinity for aggregated tau, a key pathologic feature of Alzheimer disease (AD) and other neurodegenerative disorders. Preclinically, 18F-PI-2620 binds to both 3-repeat and 4-repeat tau isoforms. The purpose of this first-in-humans study was to evaluate the ability of 18F-PI-2620 to detect tau pathology in AD patients using PET imaging, as well as to assess the safety and tolerability of this new tau PET tracer. Methods: Participants with a clinical diagnosis of probable AD and healthy controls (HCs) underwent dynamic 18F-PI-2620 PET imaging for 180 min. 18F-PI-2620 binding was assessed visually and quantitatively using distribution volume ratios (DVR) estimated from noninvasive tracer kinetics and SUV ratio (SUVR) measured at different time points after injection, with the cerebellar cortex as the reference region. Time-activity curves and SUVR were assessed in AD and HC subjects, as well as DVR and SUVR correlations and effect size (Cohen's d) over time. Results:18F-PI-2620 showed peak brain uptake around 5 min after injection and fast washout from nontarget regions. In AD subjects, focal asymmetric uptake was evident in temporal and parietal lobes, precuneus, and posterior cingulate cortex. DVR and SUVR in these regions were significantly higher in AD subjects than in HCs. Very low background signal was observed in HCs. 18F-PI-2620 administration was safe and well tolerated. SUVR time-activity curves in most regions and subjects achieved a secular equilibrium after 40 min after injection. A strong correlation (R2 > 0.93) was found between noninvasive DVR and SUVR for all imaging windows starting at more than 30 min after injection. Similar effect sizes between AD and HC groups were obtained across the different imaging windows. 18F-PI-2620 uptake in neocortical regions significantly correlated with the degree of cognitive impairment. Conclusion: Initial clinical data obtained in AD and HC subjects demonstrated a high image quality and excellent signal-to-noise ratio of 18F-PI-2620 PET for imaging tau deposition in AD subjects. Noninvasive quantification using DVR and SUVR for 30-min imaging windows between 30 and 90 min after injection-for example, 45-75 min-provides robust and significant discrimination between AD and HC subjects. 18F-PI-2620 uptake in expected regions correlates strongly with neurocognitive performance.

BackgroundThe role and implementation of tau PET imaging for predicting subsequent cognitive decline in Alzheimer’s disease (AD) remains uncertain. This study was designed to evaluate the relationship between baseline [18F]GTP1 tau PET and subsequent longitudinal change across multiple cognitive measures over 18 months.MethodsOur analyses incorporated data from 67 participants, including cognitively normal controls (n = 10) and β-amyloid (Aβ)-positive individuals ([18F] florbetapir Aβ PET) with prodromal (n = 26), mild (n = 16), or moderate (n = 15) AD. Baseline measurements included cortical volume (MRI), tau burden ([18F]GTP1 tau PET), and cognitive assessments [Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), 13-item version of the Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog13), and Repeatable Battery for the Assessment of Neuropsychological Status (RBANS)]. Cognitive assessments were repeated at 6-month intervals over an 18-month period. Associations between baseline [18F]GTP1 tau PET indices and longitudinal cognitive performance were assessed via univariate (Spearman correlations) and multivariate (linear mixed effects models) approaches. The utility of potential prognostic tau PET cut points was assessed with ROC curves.ResultsUnivariate analyses indicated that greater baseline [18F]GTP1 tau PET signal was associated with faster rates of subsequent decline on the MMSE, CDR, and ADAS-Cog13 across regions of interest (ROIs). In multivariate analyses adjusted for baseline age, cognitive performance, cortical volume, and Aβ PET SUVR, the prognostic performance of [18F]GTP1 SUVR was most robust in the whole cortical gray ROI. When AD participants were dichotomized into low versus high tau subgroups based on baseline [18F]GTP1 PET standardized uptake value ratios (SUVR) in the temporal (cutoff = 1.325) or whole cortical gray (cutoff = 1.245) ROIs, high tau subgroups demonstrated significantly more decline on the MMSE, CDR, and ADAS-Cog13.ConclusionsOur results suggest that [18F]GTP1 tau PET represents a prognostic biomarker in AD and are consistent with data from other tau PET tracers. Tau PET imaging may have utility for identifying AD patients at risk for more rapid cognitive decline and for stratification and/or enrichment of participant selection in AD clinical trials.Trial registrationClinicalTrials.gov NCT02640092. Registered on December 28, 2015

BackgroundRecent widespread availability of tau PET tracers, as well as large cross‐sectional and longitudinal datasets has now added tau PET as a well‐validated biomarker for feasible assessment and design of clinical trials. Tau PET shows potential for improved diagnosis, enrichment of populations likely to progress during a study, and for demonstration of target engagement or of a relevant downstream effect. Despite this great potential, there are still many challenges for effective implementation of tau PET in a clinical trial. Visual and quantitative methods for establishing tau positivity and even the meaningfulness of tau positivity are not well‐defined. There is no consensus on the best quantitative measures of tau progression. Further, while tau PET tracers currently used in AD drug studies show similar brain patterns of binding, enough differences exist that complicate use of multiple tau tracers for enrichment, or for comparing results from different study cohorts. Tau PET correlates more closely with clinical symptoms than amyloid PET, but further evidence needs to be generated for its acceptance as a surrogate by health authorities.ConclusionThis presentation will discuss current strategies and recent analyses in the use of tau PET for optimizing study populations in preclinical and early AD, and consideration of sample size requirements when tau PET is used as a pharmacodynamics marker. It will review use of baseline biomarkers including amyloid and tau burden to predict future tau accumulation and clinical progression. Practical considerations, including tau tracer production networks, dosimetry and participant burden will also be discussed.

BackgroundThe Alzheimer's disease (AD) Braak staging is a key framework for classifying tau pathology progression in AD based on histopathological post‐mortem brain examinations. However, adapting it to PET imaging can be challenging due to differences in tracer uptake patterns and binding properties, which affect sensitivity, specificity, and regional staging. This study compares Braak staging across four tau PET tracers: Flortaucipir, MK6240, PI2620, and RO948.MethodsWe assessed 90 participants across the AD spectrum (46 CU, 31 MCI, 13 dementia; mean age 66.1 ± 7.8) using Aβ PET and four tau PET tracers: (Flortaucipir, MK6240, PI2620, and RO948). Braak positivity was defined based on Aβ− CU individuals (mean +2.5 SD, SUVR). To evaluate systematic bias and agreement between tracers, we computed pairwise differences at corresponding Braak stage estimates and applied the Bland‐Altman method to assess mean bias and limits of agreement. Additionally, Tau PET Braak region trajectories were modeled as functions of Aβ burden (Centiloid scale) using the Lowess method.ResultsBraak stage trajectories as a function of Aβ differ depending on the tracer and the sequential order of abnormality is highly variable. For instance, while for MK6240, RO948 and PI2620, Braak I is the first region to became abnormal, for Flortaucipir the earliest region to became abnormal is Braak IV (Figure 1). This variable pattern of abnormality impacts in the concordance of Braak staging between tracers with the highest Braak staging agreement resulting in concordance levels of approximately 70%. The highest levels of agreement between tracers usually happen at Braak 0 or Braak IV‐V, with intermediate stages showing very low concordance (Figure 2). The Bland‐Altman analysis identified wide limits of agreement, suggesting high variability and high tracer‐specific differences (Figure 3). On the other hand, it also identified that mean differences between tracers were small, indicating minimal systematic bias.ConclusionThese preliminary findings reveal discrepancies in Braak staging when comparing Flortaucipir, MK6240, PI2620 and RO948. These findings suggest that while the tracers provide comparable stages on average, they may not be fully interchangeable in individual cases.

BackgroundThe Alzheimer's disease (AD) Braak staging is a key framework for classifying tau pathology progression in AD based on histopathological post‐mortem brain examinations. However, adapting it to PET imaging can be challenging due to differences in tracer uptake patterns and binding properties, which affect sensitivity, specificity, and regional staging. This study compares Braak staging across four tau PET tracers: Flortaucipir, MK6240, PI2620, and RO948.MethodsWe assessed 90 participants across the AD spectrum (46 CU, 31 MCI, 13 dementia; mean age 66.1 ± 7.8) using Aβ PET and four tau PET tracers: (Flortaucipir, MK6240, PI2620, and RO948). Braak positivity was defined based on Aβ− CU individuals (mean +2.5 SD, SUVR). To evaluate systematic bias and agreement between tracers, we computed pairwise differences at corresponding Braak stage estimates and applied the Bland‐Altman method to assess mean bias and limits of agreement. Additionally, Tau PET Braak region trajectories were modeled as functions of Aβ burden (Centiloid scale) using the Lowess method.ResultsBraak stage trajectories as a function of Aβ differ depending on the tracer and the sequential order of abnormality is highly variable. For instance, while for MK6240, RO948 and PI2620, Braak I is the first region to became abnormal, for Flortaucipir the earliest region to became abnormal is Braak IV (Figure 1). This variable pattern of abnormality impacts in the concordance of Braak staging between tracers with the highest Braak staging agreement resulting in concordance levels of approximately 70%. The highest levels of agreement between tracers usually happen at Braak 0 or Braak IV‐V, with intermediate stages showing very low concordance (Figure 2). The Bland‐Altman analysis identified wide limits of agreement, suggesting high variability and high tracer‐specific differences (Figure 3). On the other hand, it also identified that mean differences between tracers were small, indicating minimal systematic bias.ConclusionThese preliminary findings reveal discrepancies in Braak staging when comparing Flortaucipir, MK6240, PI2620 and RO948. These findings suggest that while the tracers provide comparable stages on average, they may not be fully interchangeable in individual cases.