Radiosynthesis of [ <sup>18</sup> F]brequinar for <i>in vivo</i> PET imaging of hDHODH for potential studies of acute myeloid leukemia and cancers
Bibliographic record
Abstract
Dihydroorotate dehydrogenase (DHODH), an enzyme that plays a critical role in the de-novo pyrimidine biosynthesis, has been recognized as a promising target for the treatment of diseases that involve cellular proliferation, such as autoimmune diseases and cancers. Pharmacological inhibition of human DHODH (hDHODH) that offers a potential therapeutic strategy for the treatment in adult subjects with acute myeloid leukemia (AML) has recently been supported by Phase I/II clinical trials for the treatment of patients with relapsed/refractory AML. To facilitate the development of optimized hDHODH inhibitors, the presence of an in vivo imaging probe that is able to demonstrate in vivo target engagement is critical and desirable. Brequinar is one of the most potent hDHODH inhibitors so far discovered. In this work, we compare the chemical design and radiosynthesis starting from either pinacole boronate p-nitrobenzyl ester (4) or tributylstannate (tin) p-nitrobenzyl ester (5), chosen for their suitability as a precursor to [18F]brequinar. We report here the design, synthesis, radiolabeling and characterization of [18F]brequinar, and a preliminary PET imaging study of DHODH in vivo. This study provides the strategies to create [18F]brequinar, the first hDHODH inhibitor PET radiotracer, which will facilitate its use as a tool (theranostics) for hDHODH drug development and for diagnosis and monitoring therapeutic efficacy in AML and cancers.
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 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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".