Challenges for switching central nervous system and psychiatric medication products: A review of the literature
Bibliographic record
Abstract
BACKGROUND: Switching between versions of medication products happens commonly despite challenges in achieving bioequivalence and therapeutic equivalence. Central nervous system and psychiatric drugs, especially those that are technically demanding to manufacture and have complex pharmacokinetic properties, such as long-acting injectables (LAIs), pose particular challenges to bioequivalence and safe and efficacious drug switching. AIMS: To assess whether drugs deemed "bioequivalent" are truly interchangeable in drug switching. METHODS: We assessed the published literature from January 2017 through June 2023 on PubMed using the MeSH terms "drugs, generic" OR "equivalency, generic" combined with terms for different psychiatric drug classes. RESULTS: While most of the published studies returned in the search found that switching drug products was safe and clinically comparable, data on most drug classes other than those primarily indicated in the treatment of seizure disorder were sparse. Some studies also provided evidence that real-world outcomes such as adherence and hospitalizations may also be affected by switching. In addition, a review of bioequivalence testing guidance showed inconsistency across agencies and a lack of product-specific guidance from Health Canada, which raises questions about potential claims of bioequivalence for more complex products such as LAIs. CONCLUSIONS: Overall, given the difficulty in treating mental health disorders, prescribers should be cautious when switching products and formulations in a patient who has been stabilized on a drug.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".