Reducing the Kidney Uptake of High Contrast CXCR4 PET Imaging Agents via Linker Modifications
Bibliographic record
Abstract
Purpose: The C-X-C chemokine receptor 4 (CXCR4) is highly expressed in many subtypes of cancers, notably in several kidney-based malignancies. We synthesized, labeled, and assessed a series of radiotracers based on a previous high contrast PET imaging radiopharmaceutical [68Ga]Ga-BL02, with modifications to its linker and metal chelator, in order to improve its tumor-to-kidney contrast ratio. Methods: Based on the design of BL02, a piperidine-based cationic linker (BL06) and several anionic linkers (tri-Aad (BL17); tri-D-Glu (BL20); tri-Asp (BL25); and tri-cysteic acid (BL31)) were substituted for the triglutamate linker. Additionally, the DOTA chelator was swapped for a DOTAGA chelator (BL30). Each radiotracer was labeled with 68Ga and evaluated in CXCR4-expressing Daudi xenograft mice with biodistribution and/or PET imaging studies. Results: Of all the evaluated radiotracers, [68Ga]Ga-BL31 showed the most promising biodistribution profile, with a lower kidney uptake compared to [68Ga]Ga-BL02, while retaining the high imaging contrast capabilities of [68Ga]Ga-BL02. [68Ga]Ga-BL31 also compared favorably to [68Ga]Ga-Pentixafor, with superior imaging contrast in all non-target organs. The other anionic linker-based radiotracers showed either equivocal or worse contrast ratios compared to [68Ga]Ga-BL02; however, [68Ga]Ga-BL25 also showed lower kidney uptake, as compared to that of [68Ga]Ga-BL02. Meanwhile, [68Ga]Ga-BL06 had high non-target organ uptake and relatively lower tumor uptake, while [68Ga]Ga-BL30 showed significantly increased kidney uptake and similar tumor uptake values. Conclusions: [68Ga]Ga-BL31 is an optimized CXCR4-targeting radiopharmaceutical with lower kidney retention that has clinical potential for PET imaging and radioligand therapy.
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 | 0.000 | 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 | 0.000 | 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".