NANOTECHNOLOGY IN CANCER TREATMENT: ADVANCEMENTS AND FUTURE DIRECTIONS – ANALYZING THE IMPACT OF NANO-BASED DRUG DELIVERY IN ONCOLOGY- SYSTEMATIC REVIEW
Bibliographic record
Abstract
Background: Nanotechnology has emerged as a transformative tool in cancer therapy, offering precision drug delivery, improved pharmacokinetics, and reduced systemic toxicity. Traditional cancer treatments often suffer from non-specific targeting and adverse side effects. Despite a growing body of literature on nano-based drug delivery, a comprehensive synthesis of current evidence and its translational potential remains limited, highlighting the need for an updated systematic review. Objective: This systematic review aims to evaluate the clinical effectiveness, safety, and translational significance of nanotechnology-based drug delivery systems in oncology. Methods: A systematic review was conducted in accordance with PRISMA guidelines. Literature searches were performed across PubMed, Scopus, Web of Science, and the Cochrane Library using keywords related to “nanotechnology,” “cancer,” and “drug delivery.” Studies published between 2020 and 2025 were screened based on predefined inclusion and exclusion criteria, focusing on human studies involving nano-based therapeutics in oncology. Data extraction followed a standardized protocol, and risk of bias was assessed using the Cochrane Risk of Bias Tool and Newcastle-Ottawa Scale, as appropriate. Results: Eight studies met the eligibility criteria and were included in the final review. The findings consistently demonstrated that nanoformulations such as liposomes, polymeric nanoparticles, and RNA-loaded nanocarriers enhanced tumor-specific drug delivery, reduced systemic toxicity, and showed promise in overcoming drug resistance. Although clinical data were limited, preclinical and early-phase evidence suggests high therapeutic potential with favorable safety profiles. Heterogeneity in study designs and reporting limited the feasibility of a meta-analysis. Conclusion: Nano-based drug delivery systems represent a significant advancement in oncology, offering enhanced efficacy and safety over traditional therapies. However, the current evidence is predominantly preclinical, necessitating large-scale clinical trials to confirm therapeutic benefits and guide clinical implementation.
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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 | 0.020 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".