Liposomal Drug Delivery: Recent Patents and Emerging Opportunities
Bibliographic record
Abstract
It is challenging to develop innovative, as well as commercially viable, lipid-based drug delivery systems for the treatment of cancer because of the breadth of existing intellectual property that limits freedom-to-operate. For example, novel compositions can be described in which a new chemical entity is associated with a lipid based carrier, but if the loading method or components of the lipid compositions are proprietary then the ability to develop novel compositions will require access to the appropriate intellectual property. We believe it is useful to present a review of the patent literature describing novel liposomal drug delivery systems given by parenteral administration to humans for the treatment of serious medical conditions such as cancer. This review is intended to: (i) identify and describe novel approaches that have recently been protected by US or international patents and patent applications, and; (ii) identify founding technology in the field which is recently off-patent, thus presenting emerging opportunities for the development of new therapeutic options for patients. Issued patents, and selected patent applications, having publication dates in 2005 or 2006 were retrieved from searches of the US, European, German, Japanese, INPADOC and WIPO PCT databases. Liposomal delivery systems patented for systemic administration in the treatment of human medical conditions were reviewed in detail.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".