Synthesis and structure-activity relationship of Tc/Re 2-arylbenzothiazoles as β-amyloid imaging agents
Bibliographic record
Abstract
1555 Objectives PET tracers for imaging brain β-amyloid (Aβ) plaque have been successfully applied in clinical studies of Alzheimer’s disease (AD). These include 2-arylbenzothiazoles (2-ABTs) [11C]PiB and [18F]Flutemetamol developed at the University of Pittsburgh. In this study, we aim at exploring 99mTc labeled 2-ABTs for imaging Aβ plaques with the less expensive and more accessible imaging modality SPECT. Methods Re 2-ABTs were synthesized via the integrated approach to minimize the overall molecular weight. The binding affinity was measured using synthetic Aβ1-40 fibrils, and the lipophilicity was determined using their HPLC partition coefficients PC18 as an estimation. 99mTc labeling was performed by direct labeling using SnCl2 as the reducing agent. The ability to cross blood-brain barrier was determined by brain uptake in mice at 2 min postinjection (pi). Results By using Re as a surrogate of Tc, a total of 35 neutral and compact Re 2-ABTs with an integrated N3S, N2S2, or N2SO chelator were prepared. These Re 2-ABTs are lipophilic (logPC18= 0.67-3.25) and bind to Aβ1-40 fibrils with a range of affinities (Ki= 29.7-861 nM). Re 2-ABTs with a diaminedithiol or diamine-thiol-phenol chelator integrated at the 3’ and 4’ positions of the phenyl group show better binding affinity to Aβ1-40 fibrils (Ki= 29.7-109 nM). The 99mTc analog of the most potent Re 2-ABT (Ki= 29.7nM; logPC18= 1.65) was prepared in >80 % radiochemical yield and its uptake in mouse brain at 2 min pi was 0.27 %ID/g. Conclusions The 99mTc-labeled 2-ABT reported here is not suitable for imaging Aβ plaques in the brain parenchyma of AD patients due to its limited ability to cross the blood-brain barrier. However, without high background interference from brain parenchyma it might be advantageous to use 99mTc-labeled 2-ABT for imaging Aβ deposition in blood vessels known as cerebral amyloid angiopathy, a common cause of hemorrhagic stroke and dementia in the elderly
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".