The Success Rate of New Drug Development in Clinical Trials: Crohn’s Disease
Bibliographic record
Abstract
PURPOSE: To determine the risk of drug failure during clinical trial testing in Crohn's disease and determine what steps can be taken to improve outcomes. This is the first study to quantify such risk for a single disease. METHODS: Moderate to severe Crohn's disease was investigated by reviewing press releases from 1998 to June 2008. Clinical trial failure causes were classified as commercial or clinical and compared with industry expectations. The risk of failure was also reviewed based on whether the compound was a small molecule drug or a biologic. Lastly, the role of the sponsor was examined, in determining whether the size of the firm involved in a drug program was predictive of the outcome of the study. RESULTS: More than a 120 press releases were reviewed yielding 37 drugs that met our search criteria. The cumulative success rate for drug development in Crohn's disease is 19%, from start to finish of clinical trial testing. New drug approvals are dominated by protein based therapeutics in this indication. Commercial and clinical failures both contributed substantially to the failure rates of new drugs. Phase I clinical testing appeared to offer little risk mitigation with pass rates at 95%. CONCLUSIONS: Funding intended to advance Crohn's disease must take into account the disease specific historical failure rate of drug development in forecasting any reasonable expectation of producing new therapies. As it currently stands, one in five drugs will be successfully approved that enter clinical trial testing in this indication. To manage this risk continued development of biologics over small molecule drugs may be warranted in this disease.
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 | 0.111 | 0.321 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".