Launch sequencing of pharmaceuticals with multiple therapeutic indications: evidence from seven countries
Bibliographic record
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".