Nanostructured Systems in Advanced Drug Targeting for the Cancer Treatment: Recent Patents
Bibliographic record
Abstract
BACKGROUND: Cancer is one of the leading causes of death in the world and it is necessary to develop new strategies for its treatment because most therapies have limited access to many types of tumors, as well as low therapeutic efficacy and high toxicity. OBJECTIVE: The present research aims to identify recent patents of drug delivery nanostructured systems that may have application in improving cancer treatment. METHODS: Recent patents regarding the drug delivery nanostructured systems for cancer treatment were obtained from the patent databases of the six main patent offices of the world: United States Patent and Trademark Office, European Patent Office, World Intellectual Property Organization, Japan Patent Office, State Intellectual Property Office of China and Korean Intellectual Property Office. RESULTS: A total of 1710 patent documents from 1998 to 2017 including "drug delivery nanostructured systems for cancer treatment" were retrieved. The top five countries in patent share were USA, China, South Korea, Canada and Germany. The universities and enterprises of USA had the highest amount of patents followed by institutions from China. CONCLUSION: There is a strong tendency for the development of new nanostructured systems for the release of drugs; particularly, in recent years, the development of nanoparticles has focused on nanodiscs, gold nanoparticles and immunoliposomes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".