How innovative are new drugs launched in the UK? A retrospective study of new drugs listed in the British National Formulary (BNF) 2001–2012
Bibliographic record
Abstract
OBJECTIVES: Innovative new drugs offer potential benefits to patients, healthcare systems, governments and the pharmaceutical industry. Recent data suggest annual numbers of new drugs launched in the UK have increased in recent years, and we sought to understand whether this represents increasing numbers of highly innovative drugs being made available or the introduction of increasing numbers of drugs with limited additional therapeutic value. DESIGN AND SETTING: Retrospective observational study of new drug entries in the British National Formulary (BNF). PRIMARY AND SECONDARY OUTCOME MEASURES: Number of new drugs launched in the UK each year (based on first appearance in the BNF) from 2001 to 2012, including new chemical entities and new biological drugs, categorised by degree of innovativeness according to published criteria that incorporate both clinical usefulness and the nature of the innovation. RESULTS: Highly innovative, moderately innovative and slightly innovative drugs made up 26%, 18% and 56% of all newly launched drugs, respectively, for the study period (n=290). There was an upward trend in annual numbers of slightly innovative drugs from 2004 onwards (R(2)=0.44), which aligned closely with the recovery in total numbers of new drugs launched each year since that time. There were no discernible time trends in the highly or moderately innovative categories. New drugs for malignancy and skin disease were most likely to be characterised as highly innovative (44% and 57%, respectively). CONCLUSIONS: Highly innovative new drugs comprise only around a quarter of all new drug launches in the UK. In contrast, drugs categorised as only slightly innovative comprised well over half of all new drugs and annual numbers in this category are increasing. Current policy initiatives that seek to increase the supply of innovative new drugs have long-lead times to impact, and will need careful assessment to ensure they deliver their aims without unintended consequences.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".