Design and fabrication of drug‐delivery systems toward adjustable release profiles for personalized treatment
Bibliographic record
Abstract
Abstract Advanced polymeric controlled delivery systems are designed to effectively treat chronic diseases by adjusting the temporal profile of drug release. Most conventional controlled‐release carriers provide a constant and sustained‐release profile of therapeutics for an extended time. Although these systems have improved the patients’ compliance and adherence and have reduced the administration frequency, they cannot be used for optimal treatment of diseases that require variable patterns of drug release in the treatment regimen. These patterns and the specific rhythms of medical conditions determined by both the body's internal biological clock cycles (i.e., circadian rhythm) and each patient's characteristics call for patient‐specific controlled drug‐delivery systems that can provide adjustable drug release profiles. The importance of individualized therapy and the variety of biodegradable polymers with tunable physicochemical properties promote the design and manufacturing of polymeric delivery systems that release therapeutics at controllable rates. In the past two decades, novel biomaterials and fabrication methods have been utilized to improve the traditional drug‐delivery design and manufacturing technologies. This review article provides a critical discussion of emerging polymeric controlled‐release systems and the mechanisms through which they release their therapeutic agents. Advances and challenges in the design and the fabrication processes of polymeric drug‐delivery systems, particularly solid oral dosage forms and implantable microchips, with controllable release profiles of drugs, are reviewed, focusing on the application of microtechnology and 3D printing techniques in their manufacturing.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".