Tracer kinetic analysis of [18F]DCFPyL, a promising PSMA imaging agent, in mouse xenografts of prostate cancer
Bibliographic record
Abstract
64 Objectives Prostate-specific membrane antigen (PSMA) is frequently over-expressed in prostate cancer. To date, various 18F- and 68Ga-labeled radiotracers for PET imaging of PSMA have been developed and entered clinical trials. Here we describe dynamic PET studies with [18F]DCFPyL, a radiotracer developed by Pomper et al. at John Hopkins Medical School [1], in mouse xenografts of prostate cancer to gain more insights into pharmacokinetics. Methods [18F]DCFPyL was synthesized via direct nucleophilic aromatic substitution reaction in an automated radiosynthesis unit. Dynamic PET experiments were performed in LNCaP (PSMA+) and PC3 (PSMA-) tumor-bearing BALB/c nude mice. Reversible 1- and 2-tissue compartment model analysis were applied for kinetic analysis. Results Automated radiosynthesis of [18F]DCFPyL afforded good radiochemical yields (>50%) and high specific activity. Dynamic PET analysis revealed rapid and high uptake of radioactivity (SUV5min 0.85) in LNCaP tumors which slightly increased over time (SUV60min 0.91). Muscle tissue showed rapid and continuous clearance over time (SUV60min 0.06). Fast blood clearance of radioactivity resulted in tumor-blood-ratios of 1.0 after 10 min and 8.3 after 60 min. PC3 tumors also showed continuous clearance of radioactivity over time (SUV60min 0.11). Kinetic analysis of PET data revealed the 2-tissue compartment model as best fit with K1 0.12 min-1, k2 0.17 min-1, k3 0.08 min-1 and k4 0.003 min-1 confirming molecular trapping of [18F]DCFPyL in PSMA expressing tumor cells. Conclusions [18F]DCFPyL displays high PSMA-associated tumor uptake combined with superior clearance parameters. Compartment model analysis points to a two-step molecular trapping process, possibly based on binding and internalization following retention of radioactivity in PSMA-expressing tumor cells.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".