Dual enhancement in the radiosensitivity of prostate cancer through nanoparticles and chemotherapeutics
Bibliographic record
Abstract
Background: Radiotherapy (RT) is an essential component in the treatment regimens for many cancer patients. However, the dose escalation required to improve curative results is hindered due to the normal tissue toxicity that is induced. The introduction of radiosensitizers to RT treatment is an avenue that is currently being explored to overcome this issue. By introducing radiosensitizers into tumor sites, it is possible to preferentially enhance the local dose deposited. Gold nanoparticles (GNPs) are a potential candidate that have shown great promise in increasing the radiosensitivity of cancer cells through an enhancement in DNA damage. Furthermore, docetaxel (DTX) is a chemotherapeutic agent that arrests cells in the G2/M phase of the cell cycle, the phase most sensitive to radiation damage. We hypothesized that by incorporating DTX to GNP-enhanced radiotherapy treatment, we could further improve the radiosensitization experienced by cancer cells. To assess this strategy, we analyzed the radiotherapeutic effects on monolayer cell cultures in vitro, as well as on a mice prostate xenograft model in vivo while using clinically feasible concentrations for both GNPs and DTX. Results: The introduction of DTX to GNP-enhanced radiotherapy further increased the radiotherapeutic effects experienced by cancer cells. A 38% increase in DNA double-strand breaks was observed with the combination of GNP/DTX vs GNP alone after a dose of 2 Gy was administered. In vivo results displayed significant reduction in tumor growth over a 30-day observation period with the treatment of GNP/DTX/RT when compared to GNP/RT after a single 5 Gy dose was given to mice. The treatment strategy also resulted in 100% mice survival, which was not observed for other treatment conditions. Conclusions: Incorporating DTX to work in unison with GNPs and RT can increase the efficacy of RT treatment. Our study suggests that the treatment strategy could improve tumor control through local dose enhancement. As the concentrations used in this study are clinically feasible, there is potential for this strategy to be translated into clinical settings. Supplementary Information: The online version contains supplementary material available at 10.1186/s12645-023-00228-0.
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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 | 0.000 |
| 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".