Development and economic trends in cancer therapeutic drugs: a 5-year update 2010–2014
Bibliographic record
Abstract
BACKGROUND: Over the past 20 years, the mechanisms of action, duration of benefits and economic costs of newly licenced cancer drugs have changed significantly; however, summary data on these characteristics are limited. METHODS: In this study, using historical copies of the British National Formulary and relevant contemporary publications, we have documented for each new cancer drug the year of introduction, therapeutic classification, initial indication, median duration of treatment and the cost of treatment at introduction relative to the then current UK GDP per capita. RESULTS: Before 2000, there were 69 cancer treatment drugs available, of which 50 (72.5%) were classical cytotoxic drugs. In the subsequent 15 years, there have been 63 more new cancer treatment drugs added, including 20 kinase inhibitors and 11 monoclonal antibodies. The average median duration of treatment with a new drug has risen from 181 days in 1995-1999 to 263 days in 2010-2014. The average cost of treatment has also risen from £3036.91 (20.6% of UK per capita GDP) in 1995-1999 to £20 233 (89.0%) in 2005-2009 and now to £35 383 (141.7%) in 2010-2014. CONCLUSIONS: The last 5 years has seen 33 new cancer drugs. These drugs deliver significant benefits in patient outcomes and are taken for increasing lengths of time. Alongside these clinical benefits, the direct costs of new treatments have increased significantly over the past decade.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.024 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".