Combination of Radioligand Therapy and Immunotherapy: How to Make It Work in Clinic?
Bibliographic record
Abstract
Rubin Jiao, Ekaterina Dadachova College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, SK, CanadaCorrespondence: Ekaterina Dadachova, College of Pharmacy and Nutrition, University of Saskatchewan, 107 Wiggins Road, Health Sciences Bld, Room B18, Saskatoon, SK, S7N 5E5, Canada, Tel +1-306-966-5163, Email ekaterina.dadachova@usask.caAbstract: Combining external beam radiation therapy (EBRT) with immunotherapy showed promise pre-clinically by inducing immunogenic cell death (ICD) thus releasing damage-associated molecular patterns (DAMPs) and cytokines to activate the immune system. Clinical results, however, have often been disappointing. Radioligand therapy (RL), which uses targeted radionuclides to deliver cytotoxic radiation, offers advantages over EBRT by treating multiple tumors simultaneously. Combining RL with immunotherapy faces challenges, as prolonged radiation exposure can damage immune cells, and the “cross-fire” and “bystander” effects may harm incoming effector cells. Current RL therapies require multiple doses, further complicating immune cell viability. To optimize RL-immunotherapy combinations, timing is critical. Administering immunotherapy weeks after RL therapy may reduce radiation-induced immune cell damage. Additionally, selecting radionuclides with shorter half-lives could minimize immune cell toxicity while maintaining tumor-killing efficacy. Future RL therapies should prioritize radionuclides with optimal emission profiles and half-lives to enhance synergy with immunotherapy and improve clinical outcomes.Keywords: immunotherapy, radioligand therapy, combination therapy, clinical outcomes, radiobiology
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
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".