Preliminary PET imaging of [<sup>11</sup>C]evobrutinib in mouse models of colorectal cancer, SARS‐CoV‐2, and lung damage: Radiosynthesis via base‐aided palladium‐NiXantphos‐mediated <sup>11</sup>C‐carbonylation
Bibliographic record
Abstract
Evobrutinib is a second‐generation, highly selective, irreversible Bruton's tyrosine kinase (BTK) inhibitor that has shown efficacy in the autoimmune diseases arthritis and multiple sclerosis. Its development as a positron emission tomography (PET) radiotracer has potential for in vivo imaging of BTK in various disease models including several cancers, severe acute respiratory syndrome‐coronavirus‐2 (SARS‐CoV‐2), and lipopolysaccharide (LPS)‐induced lung damage. Herein, we report the automated radiosynthesis of [11C]evobrutinib using a base‐aided palladium‐NiXantphos‐mediated 11C‐carbonylation reaction. [11C]Evobrutinib was reliably formulated in radiochemical yields of 5.5 ± 1.5% and a molar activity of 34.5 ± 17.3 GBq/μmol (n = 12) with 99% radiochemical purity. Ex vivo autoradiography studies showed high specific binding of [11C]evobrutinib in HT‐29 colorectal cancer mouse xenograft tissues (51.1 ± 7.1%). However, in vivo PET/computed tomography (CT) imaging with [11C]evobrutinib showed minimal visualization of HT‐29 colorectal cancer xenografts and only a slight increase in radioactivity accumulation in the associated time‐activity curves. In preliminary PET/CT studies, [11C]evobrutinib failed to visualize either SARS‐CoV‐2 pseudovirus infection or LPS‐induced injury in mouse models. In conclusion, [11C]evobrutinib was successfully synthesized by 11C‐carbonylation and based on our preliminary studies does not appear to be a promising BTK‐targeted PET radiotracer in the rodent disease models studied herein.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".