Recent advances in "smart" delivery systems for extended drug release in cancer therapy
Bibliographic record
Abstract
Regina-Veronicka Kalaydina,1,* Komal Bajwa,2,* Bessi Qorri,1,* Alexandria Decarlo,3 Myron R Szewczuk1 1Department of Biomedical and Molecular Sciences, Queen’s University, 2Postgraduate Medical Education, Graduate Diploma and Professional Master in Medical Sciences, School of Medicine, Queen’s University, 3Department of Biology, Queen’s University, Kingston, ON, Canada *These authors contributed equally to this work Abstract: Advances in nanomedicine have become indispensable for targeted drug delivery, early detection, and increasingly personalized approaches to cancer treatment. Nanoparticle-based drug-delivery systems have overcome some of the limitations associated with traditional cancer-therapy administration, such as reduced drug solubility, chemoresistance, systemic toxicity, narrow therapeutic indices, and poor oral bioavailability. Advances in the field of nanomedicine include “smart” drug delivery, or multiple levels of targeting, and extended-release drug-delivery systems that provide additional methods of overcoming these limitations. More recently, the idea of combining smart drug delivery with extended-release has emerged in hopes of developing highly efficient nanoparticles with improved delivery, bioavailability, and safety profiles. Although functionalized and extended-release drug-delivery systems have been studied extensively, there remain gaps in the literature concerning their application in cancer treatment. We aim to provide an overview of smart and extended-release drug-delivery systems for the delivery of cancer therapies, as well as to introduce innovative advancements in nanoparticle design incorporating these principles. With the growing need for increasingly personalized medicine in cancer treatment, smart extended-release nanoparticles have the potential to enhance chemotherapy delivery, patient adherence, and treatment outcomes in cancer patients. Keywords: nanomedicine, smart delivery systems, extended drug release, personalized medicine
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".