The Challenges of Switching Therapies in an Evolving Multiple Biosimilars Landscape: A Narrative Review of Current Evidence
Bibliographic record
Abstract
With the increasing availability of biosimilars, the practice of switching therapies for non-medical reasons between an originator biologic and an analogous biosimilar has become more common. The evidence to support this practice mostly comes from single-switch randomized controlled trials (RCTs) and real-world (RW) evidence studies. However, as more biosimilars of the same originator enter the market, multiple switching events between originators and biosimilars is becoming a reality, despite limited evidence to support the efficacy and safety of such practice. Some countries have established guidelines, policies, or laws related to interchangeability and/or automatic substitution, whereas others have left these practices unregulated or controlled by other components of the healthcare system. Collectively, guidelines on single non-medical switching are often vague, with even less focus given to multiple non-medical switching, leaving this practice mostly unregulated. This narrative review will first discuss the current regulatory perspectives on non-medical switching and challenges associated with switching therapies, particularly with the availability of multiple biosimilars. We will then review the current evidence from RCTs and RW studies in the light of three different multiple-switch scenarios currently taking place in clinical practice: switching between an originator and a single biosimilar, switching between biosimilars of the same originator, and the clinical practice of switching back to the originator (i.e., switchbacks) after a failure of the initial non-medical switch to the analogous biosimilar.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".