Perspectives and Insights on Anti-Psoriatic Drugs (D05): From Development to New Drug Submission (NDS) Application to Health Canada
Bibliographic record
Abstract
Psoriasis, a chronic autoimmune skin disorder, affects millions in Canada, causing erythematous plaques, reduced quality of life, and comorbidities like psoriatic arthritis. Anti-psoriatic drugs (ATC code D05) encompass topical, oral, and biologic therapies targeting inflammatory pathways, such as IL-23/Th17, to manage moderate-to-severe disease. These include traditional systemic agents, targeted small molecules, and biologics, offering diverse mechanisms to balance efficacy and safety. This article reviews the D05 class’s clinical applications, chemical properties, container closure systems, safety profiles, and emerging technologies, such as nanomedicines and precision medicine. It also outlines the regulatory pathway for New Drug Submission (NDS) in Canada, aligning with the Food and Drugs Act, Health Canada regulations, and International Council for Harmonisation (ICH) guidelines. Recommendations address reimbursement barriers, emphasizing early regulatory engagement, robust pharmacovigilance, and equitable access strategies to ensure patient access to innovative therapies.
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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".