Mechanisms and Barriers in Nanomedicine: Progress in the Field and Future Directions
Bibliographic record
Abstract
In recent years, steady progress has been made in synthesizing and characterizing engineered nanoparticles, resulting in several approved drugs and multiple promising candidates in clinical trials. Regulatory agencies such as the Food and Drug Administration and the European Medicines Agency released important guidance documents facilitating nanoparticle-based drug product development, particularly in the context of liposomes and lipid-based carriers. Even with the progress achieved, it is clear that many barriers must still be overcome to accelerate translation into the clinic. At the recent conference workshop "Mechanisms and Barriers in Nanomedicine" in May 2023 in Colorado, U.S.A., leading experts discussed the formulation, physiological, immunological, regulatory, clinical, and educational barriers. This position paper invites open, unrestricted, nonproprietary discussion among senior faculty, young investigators, and students to trigger ideas and concepts to move the field forward.
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 | 0.015 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| 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".