Personalized metronomic radiopharmaceutical therapy through injection profile optimization via physiologically based pharmacokinetic (PBPK) modeling
Bibliographic record
Abstract
Each treatment cycle of radiopharmaceutical therapy (RPT) is administered as a single dose. We aimed to investigate a personalized metronomic RPT paradigm, employing multiple lower-dose administrations, to evaluate its effect on delivering radiopharmaceuticals to tumors. We developed a physiologically-based pharmacokinetic (PBPK) model applied to metastatic castration-resistant prostate cancer patients to analyze the impact of metronomic framework and various infusion durations (1–4 h) on absorbed doses (ADs) in tumors and organ-at-risk (OAR). We designed a treatment algorithm to select optimal regimens with high AD, while investigating what we term radiopharmaceutical delivery payload (RDP). This metric evaluates the efficiency of radiopharmaceutical delivery by quantifying the proportion of the administered dose that successfully reaches the target tissue. The goal is to optimize trade-offs between RDP and tumors-AD among injection profiles, amongst varying radioactivity (1-22GBq), total radiopharmaceutical mass (2 5 -2 10 nmol), number of injections (2–6), and time intervals (12–36 h) between injections. Our framework applied to five patients led to increased AD between 2 and 358 Gy (between 2 and 146%) higher than normally administered to patients, safeguarding OARs. Using single-dose scenarios to match ADs in metronomic approach, led to significant increase in injected activities, requiring injection of 0 to 9GBq additional activity (reducing RDP by 3–75%). Maintaining total administered radioactivity within clinically therapeutic levels, increasing frequency, time interval, and infusion duration increases tumors and OARs AD by 0.05-73%, while it decreased tumors-to-OARs AD ratios by 0.1–30%. Based on the PBPK modeling approach, metronomic RPT appears to improve efficacy (RDP) in delivered doses to tumors for a given total injected radioactivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".