Mechanistic insights into radiopharmaceutical therapies: Spatially-resolved computational model coupling radioligand pharmacokinetics with tumour dynamics
Bibliographic record
Abstract
Abstract Radiopharmaceutical therapies (RPTs) offer targeted radiation delivery to tumour cells, yet treatment outcomes vary substantially across patients while dosing protocols remain largely uniform. Translating mechanistic insight into improved protocols requires models that couple radioligand (RL) pharmacokinetics, spatial tumour biology, and radiation response — a combination that existing models have not yet fully achieved. We coupled a cellular automaton for spatially resolved tumour dynamics to a pharma-cokinetic compartment model that tracks RL from injection through receptor binding and internalization. We estimate the energy deposited by radiation emitted from RL within the tumour and use a linear-quadratic radiobiological survival probability to determine the impact of the treatment. Simulating heterogeneous tumours across a range of conditions, we find that treatment outcome is governed primarily by tumour size and receptor expression levels and is relatively insensitive to the injected amount per cycle but highly dependent on inter-injection time intervals. The model’s uniform RL delivery separates radiobiological resistance from delivery effects — two mechanisms that are spatially correlated in real tumours — and the simulations suggest that compromised RL delivery may play a larger role in hypoxic treatment failure than radioresistance alone. These findings provide a mechanistic basis for patient stratification by receptor expression, yield a new size-based rationale for multi-injection protocols, and demonstrate that spatially resolved modelling can reveal treatment principles inaccessible to non-spatial approaches.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".