Clinical Trial Risk in Type-2 Diabetes: Importance of Patient History
Bibliographic record
Abstract
PURPOSE: To determine the risk of clinical trial failure for drugs developed for type-2 diabetes. METHODS: Drugs were investigated by reviewing phase I to phase III studies that were conducted between 1998 and February 2013. The clinical trial success rates were calculated and compared to the industry standard. The drugs were classified into GLP-1 receptor agonists, DPP-4 inhibitors, SGLT-2 inhibitors and "Other". The exclusion criteria for drugs in this study: Drugs that were started in phase I studies prior to January 1998 for this indication and drugs whose primary indications were not for the control of blood glucose levels. RESULTS: Data was extracted from clinicaltrials.gov; there were a total of 131 drug candidates that fit our specified criteria, of which 8 received FDA approval. The cumulative success rate for molecules developed for type-2 diabetes is 10%. Small molecules were more successful than biologics. A strong disparity was observed in phase III, with studies that utilised treatment naïve patients having a 40% success rate, compared to an 83% success rate in patients who have had previous anti-hyperglycemic exposure. CONCLUSIONS: 1 in 10 drugs that enter clinical testing in this disease will be approved. The DPP-4 inhibitor class of drugs had the highest success rate of all drug classes with a 63% cumulative success rate; while treatment naïve patients carried the greatest clinical trial risk.
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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 | 0.053 | 0.236 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".