Manganese-52: applications in cell radiolabelling and liposomal nanomedicine PET imaging using oxine (8-hydroxyquinoline) as an ionophore
Bibliographic record
Abstract
The ionophore 8-hydroxyquinoline (oxine) has been used to radiolabel cells and liposomal medicines with 111In and, more recently, 89Zr, for medical nuclear imaging applications. Oxine has also shown promising ionophore activity for the positron-emitting radionuclide 52Mn that should allow imaging of labelled cells and nanomedicines for long periods of time (>14 days). However, to date, the radiometal complex formed and its full labelling capabilities have not been fully characterised. Here, we provide supporting evidence of the formation of [52Mn]Mn(oxinate)2 as the metastable complex responsible for its ionophore activity. The cell labelling properties of [52Mn]Mn(oxinate)2 were investigated with various cell lines. The liposomal nanomedicine, DOXIL® (Caelyx) was also labelled with [52Mn]Mn(oxinate)2 and imaged in vivo using PET imaging. [52Mn]Mn(oxinate)2 was able to label various cell lines with moderate efficiency (15-53%), however low cellular retention of 52Mn (21-25% after 24 h) was observed which was shown not to be due to cell death. PET imaging of [52Mn]Mn-DOXIL at 1 h and 24 h post-injection showed the expected pharmacokinetics and biodistribution of this stealth liposome, but at 72 h post-injection showed a profile matching that of free 52Mn, consistent with drug release. We conclude that oxine is an effective ionophore for 52Mn, but high cellular efflux of the isotope limits its use for prolonged cell tracking. [52Mn]Mn(oxinate)2 is effective for labelling and tracking DOXIL in vivo. The release of free radionuclide after liposome extravasation could provide a non-invasive method to monitor drug release in vivo.
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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 | 0.001 | 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".