Assessment of the impact of the nuclear properties of β⁻-emitting radionuclides on the dosimetry of two radiopharmaceuticals with distinct pharmacokinetics
Bibliographic record
Abstract
Abstract Objective. The aim of this study was to evaluate the impact of the nuclear properties of six β −-emitting radionuclides (47Sc, 67Cu, 111Ag, 161Tb, 177Lu, and 188Re) on the dosimetric outcomes of two tumour-targeting radiopharmaceuticals (RPs), with distinct pharmacokinetics: the peptide DOTA-folate conjugate cm09 and the monoclonal antibody HuM195. The study specifically focused on assessing the radiation-absorbed doses in organs and tumours, as well as comparing the efficacy and safety of the twelve RPs for targeted radionuclide therapy (TRT). Approach. Murine biodistribution data for both RPs were scaled to adult human models to determine biological residence times and the number of disintegrations in source organs and tumours. Dosimetric estimations were performed using OLINDA and MIRDCell software, considering different tumour sizes and organ-specific radiation exposure for both male and female phantoms. Main results. Significant differences in organ and tumour dosimetry were found across the considered radionuclides and tumour-targeting agents, attributable to the nuclear properties of the radionuclides and the RP pharmacokinetics. PFP-HuM195 labelled with 161Tb and 111Ag demonstrated efficient dose delivery to tumour from 1–10 mm, but also higher organ-absorbed doses per unit of injected activity than other labelling radionuclides. Cm09 exhibited less variability in tumour absorbed dose as the labelling radionuclide varied, but also produced much higher kidney absorbed doses than PFP-HuM195. Normalising to the same tumour absorbed dose showed that 177Lu and 161Tb are the safer options for treating small tumours (2.7–12.4 mm) with both RPs. These results demonstrate that the choice of radionuclide has a significant impact on both therapeutic efficacy and organ safety. Significance. This research demonstrates that selecting the appropriate radionuclide for TRT can optimise therapeutic outcomes while minimising radiation exposure to healthy tissues. The findings contribute to advancing personalised TRT approaches by considering RP-specific pharmacokinetics and radionuclide characteristics, paving the way for more effective cancer treatments.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".