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Development and Preclinical Evaluation of PET Radiotracers Targeting Adenosine A<sub>1</sub> Receptors

2025· article· en· W4411691100 on OpenAlexaff
Abolghasem Bakhoda, Torben D. Pearson, Zhan-Guo Gao, Kelly A. O’Conor, Seth M. Eisenberg, Andrew Kelleher, Yeona Kang, Jeih‐San Liow, Jun Yong Choi, Woochan Kim, Jinpyo Seo, Michael L. Freaney, Kenneth A. Jacobson, Nora D. Volkow, Sung Won Kim

Bibliographic record

VenueACS Medicinal Chemistry Letters · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdenosine and Purinergic Signaling
Canadian institutionsToronto Metropolitan University
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute on Alcohol Abuse and AlcoholismJess and Mildred Fisher College of Science and Mathematics
KeywordsAdenosineAdenosine receptorReceptorMedicinePharmacologyMedical physicsComputational biologyComputer scienceBiologyInternal medicine

Abstract

fetched live from OpenAlex

Several adenosine A 1 receptor (A 1 R) radiotracers for positron emission tomography (PET) have been developed to study their neuromodulatory functions and role in brain disorders. While two xanthine-based radiotracers ([ 11 C]MPDX and [ 18 F]CPFPX) have been used in humans, we aimed to improve the metabolic stability and specific binding. Guided by structure–activity relationship (SAR) studies, 10 derivatives were synthesized with binding affinities up to 0.12 nM. Three subnanomolar candidates ( 3, 8, 9 ) were radiolabeled with C-11 ( t 1/2 = 20.4 min) for evaluation using in vivo PET imaging and ex vivo rodent brain biodistribution. Although [ 11 C] 8 demonstrated a higher blood–brain barrier (BBB) permeability, negligible in vivo specific binding was observed. Ex vivo studies indicated that all three compounds are substrates for brain efflux pumps. Despite optimized affinity, BBB permeability and in vivo binding specificity remain challenges. These findings inform development of nonxanthine A 1 R radiotracers and highly potent CNS A 1 R drugs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.674

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.284
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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