Launch sequencing of pharmaceuticals with multiple therapeutic indications: evidence from seven countries
Bibliographic record
Abstract
Abstract Background New medicines are increasingly being identified as efficacious across multiple indications. The impact of current pricing and reimbursement policies on launch decisions across these indications remains unclear. Objective This paper, first, maps marketing authorisation and HTA coverage recommendation sequences of multi-indication medicines across Germany, France, England, Scotland, Canada, Australia, and the USA, and, second, evaluates the clinical characteristics, clinical development time and coverage recommendation time of multi-indication medicines, drawing comparisons between the first and subsequent indications of an approved molecule. Methods Medicine approvals by the Food and Drug Administration between 2009–2019 were screened to identify multi-indication products with approved oncology indications. Data on clinical trial characteristics, clinical performance and HTA outcomes were extracted from publicly available regulatory approval and HTA reports. Results Relative to subsequent indications, first indications were more likely to receive conditional marketing authorisation, have an orphan designation, have a single arm phase II pivotal trial and lower MCBS score. Subsequent indications had faster HTA coverage recommendation times in England and Canada. While the majority of first indications received HTA coverage recommendations across all settings, the proportion of subsequent indications with HTA coverage recommendations was lower and uptake varied considerably across settings. Conclusions Discordance in the value of first versus subsequent indications can pose major challenges in systems that define price based on the initial indication. Current pricing and reimbursement systems generate significant fragmentation in the approval and availability of multi-indication products across settings.
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 | 0.039 | 0.122 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".