Polymer nano-systems for the encapsulation and delivery of active biomacromolecular therapeutic agents
Bibliographic record
Abstract
Biomacromolecular therapeutic agents, particularly proteins, antigens, enzymes, and nucleic acids are emerging as powerful candidates for the treatment of various diseases and the development of the recent vaccine based on mRNA highlights the enormous potential of this class of drugs for future medical applications. However, biomacromolecular therapeutic agents present an enormous delivery challenge compared to traditional small molecules due to both a high molecular weight and a sensitive structure. Hence, the translation of their inherent pharmaceutical capacity into functional therapies is often hindered by the limited performance of conventional delivery vehicles. Polymer drug delivery systems are a modular solution able to address those issues. In this review, we discuss recent developments in the design of polymer delivery systems specifically tailored to the delivery challenges of biomacromolecular therapeutic agents. In the future, only in combination with a multifaceted and highly tunable delivery system, biomacromolecular therapeutic agents will realize their promising potential for the treatment of diseases and for the future of human health.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".