Targeted and Non-Targeted Mechanisms for Killing Hypoxic Tumour Cells—Are There New Avenues for Treatment?
Bibliographic record
Abstract
PURPOSE: A major issue in radiotherapy is the relative resistance of hypoxic cells to radiation. Historic approaches to this problem include the use of oxygen mimetic compounds to sensitize tumour cells, which were unsuccessful. This review looks at modern approaches aimed at increasing the efficacy of targeting and radiosensitizing hypoxic tumour microenvironments relative to normal tissues and asks the question of whether non-targeted effects in radiobiology may provide a new "target". Novel techniques involve the integration of recent technological advancements such as nanotechnology, cell manipulation, and medical imaging. Particularly, the major areas of research discussed in this review include tumour hypoxia imaging through PET imaging to guide carbogen breathing, gold nanoparticles, macrophage-mediated drug delivery systems used for hypoxia-activate prodrugs, and autophagy inhibitors. Furthermore, this review outlines several features of these methods, including the mechanisms of action to induce radiosensitization, the increased accuracy in targeting hypoxic tumour microenvironments relative to normal tissue, preclinical/clinical trials, and future considerations. CONCLUSIONS: This review suggests that the four novel tumour hypoxia therapeutics demonstrate compelling evidence that these techniques can serve as powerful tools to increase targeting efficacy and radiosensitizing hypoxic tumour microenvironments relative to normal tissue. Each technique uses a different way to manipulate the therapeutic ratio, which we have labelled "oxygenate, target, use, and digest". In addition, by focusing on emerging non-targeted and out-of-field effects, new umbrella targets are identified, which instead of sensitizing hypoxic cells, seek to reduce the radiosensitivity of normal tissues.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".