A Patent Analysis on Nano Drug Delivery Systems
Bibliographic record
Abstract
BACKGROUND: A nano drug delivery system is an effective tool for drug delivery and controlled release, which is used for a variety of medical applications. In recent decades, nano drug delivery systems have been significantly developed with the emergence of new nanomaterials and nanotechnologies. OBJECTIVE: This article aimed to provide insight into the technological development of nano drug delivery systems through patent analysis. METHODS: 3708 patent documents were used for patent analysis after retrieval from the Incopat patent database. RESULTS: The number of patents on nano drug delivery systems has shown a rapid growth trend in the past two decades. At present, China and the United States have obvious contributions to the number of patents. According to the patent data, the nanomaterials used in nano drug delivery system are mainly inorganic nanomaterials, lipid-based nanomaterials, and macromolecules. In recent years, the highly cited patents (≥14) for nano drug delivery systems mainly involve lipid-based nanomaterials, indicating that their technology is mature and widely used. The inorganic nanomaterials in drug delivery have received increasing attention, and the number of related patents has increased significantly after 2016. The number of highly cited patents in the United States is 250, which is much higher than in other countries. CONCLUSION: Even after decades of development, nano drug delivery systems remain a hot topic for researchers. The significant increase in patents since 2016 can be attributed to the large number of new patents from China. However, according to the proportion of highly cited patents in total, China's patented technologies in nano drug delivery systems are not advanced enough compared to developed countries, including the United States, Canada, Germany, and France. In the future, research on emerging nanomaterials for nano drug delivery systems, such as inorganic nanomaterials, may focus on developing new materials and optimising their properties. The lipid-based and polymer- based nanomaterials can be continuously improved for the development of new nanomedicines.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".