A Review of Drug-Drug Interactions for Biologic Drugs Used in the Treatment of Psoriasis
Bibliographic record
Abstract
Biologic drugs are increasingly being prescribed for the treatment of psoriasis. Very little information is available in the literature regarding potential drug interactions with these medications. This paper serves as a guide for prescribers to be aware of possible interactions between biologic drugs approved for the treatment of psoriasis in North America and concomitant therapies. OBJECTIVE: To provide an overview of reported drug interactions between biologic drugs and concomitant therapies. METHODS: Reports of potential drug interactions were compiled through a search of Micromedex, drug monographs (Canadian, American, and European), as well as a PubMed search of each biologic drug with the term "drug interaction." CONCLUSION: Generally, caution should be exercised when multiple immunosuppressive therapies are prescribed due to increased risk of infection. However, this is more the result of a synergistic effect as opposed to a true drug interaction. There have been cases where multiple biologic therapies have been concomitantly used without adverse events, as their mechanisms involved different pathways. The sources used to compile this guide were often comprised of low levels of evidence, reinforcing the idea that further studies are required to better direct prescribers.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".