Introducing the Molecular Pharmaceutics Special Issue on “Tiny Things, Big Impact: Nanomedicine in Canada”
Bibliographic record
Abstract
RecommendationsN anomedicine refers to the application of nanotechnology and nanomaterials for medical purposes and as such holds tremendous potential to revolutionize how we diagnose and treat diseases.Its application seems limitless ranging from facilitated and targeted drug delivery, to improved bioavailability of drugs, reduced side effects, protection of labile cargo, or to leveraging distinct optical or structural properties for imaging purposes.Canada's leading position in nanomedicine innovation and translation is evident.A total of 6 out of 12 nanomedicine products approved by the U.S. Food and Drug Administration and/or European Medicine Agency were originally developed in Canada.These include Visudyne (liposomal verteporfin for classic subfoveal choroidal neovascularization) in 2000, Myocet (liposomal doxorubicin for metastatic breast cancer) in 2000, Marqibo (liposomal vincristine for relapsed acute lymphoblastic leukemia) in 2012, Vyxeos (liposomal daunorubicin and cytarabine for acute myeloid leukemia) in 2017, Onpattro (lipid nanoparticle siRNA for polyneuropathy in patients with hereditary transthyretin-mediated amyloidosis) in 2018, and Comirnaty (lipid nanoparticle mRNA vaccine for COVID-19) in 2021.Many Canadian scientists contributed to the invention of key technologies in the field that led to the launch of these products.Dr. Terry Allen at the University of Alberta pioneered work on long-circulating liposomes, and Dr. Jean-Christophe Leroux at the University of Montreal is known for his achievements in the area of the stimuli-responsive nanoformulations.Drs.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.070 | 0.024 |
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".