Improving the Efficacy of Common Cancer Treatments via Targeted Therapeutics towards the Tumour and Its Microenvironment
Bibliographic record
Abstract
Cancer is defined as the uncontrolled proliferation of heterogeneous cell cultures in the body that develop abnormalities and mutations, leading to their resistance to many forms of treatment. Left untreated, these abnormal cell growths can lead to detrimental and even fatal complications for patients. Radiation therapy is involved in around 50% of cancer treatment workflows; however, it presents significant recurrence rates and normal tissue toxicity, given the inevitable deposition of the dose to the surrounding healthy tissue. Chemotherapy is another treatment modality with excessive normal tissue toxicity that significantly affects patients' quality of life. To improve the therapeutic efficacy of radiotherapy and chemotherapy, multiple conjunctive modalities have been proposed, which include the targeting of components of the tumour microenvironment inhibiting tumour spread and anti-therapeutic pathways, increasing the oxygen content within the tumour to revert the hypoxic nature of the malignancy, improving the local dose deposition with metal nanoparticles, and the restriction of the cell cycle within radiosensitive phases. The tumour microenvironment is largely responsible for inhibiting nanoparticle capture within the tumour itself and improving resistance to various forms of cancer therapy. In this review, we discuss the current literature surrounding the administration of molecular and nanoparticle therapeutics, their pharmacokinetics, and contrasting mechanisms of action. The review aims to demonstrate the advancements in the field of conjugated nanomaterials and radiotherapeutics targeting, inhibiting, or bypassing the tumour microenvironment to promote further research that can improve treatment outcomes and toxicity rates.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".