Therapeutic uses of furin and its inhibitors: a patent review
Bibliographic record
Abstract
INTRODUCTION: Since the discovery of furin, numerous reports have studied its role in health and diseases, including cancer, inflammatory and infectious diseases. This interest has led to the development of both large protein- and peptide-based inhibitors aiming to control furin activity to treat these disorders. The most recent advances include the development of potent peptidomimetic furin inhibitors, considerably expanding the field of therapeutic applications. AREA COVERED: In this review, the use of furin or its inhibitors for therapeutic conditions is described through the patent literature since 1994. Only compounds with biological efficacy or augmented properties demonstrated within the patent literature or the associated publications concerning their claimed uses are discussed. EXPERT OPINION: Considering the diseases that may benefit from furin inhibition, several patents detail the use of the restricted number of furin inhibitors. However, there have been recent reports of new scaffolds, and even the use of furin itself, as a therapeutic agent. Despite considerable evidence of in vivo efficacy, limited confirmation from clinical trials supports or refutes the further use of these compounds in a therapeutic context. The most advanced application is the use of furin knockdown in the generation of an autologous cancer vaccine, which has initiated clinical trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".