Clinical trial risk reduction in non-small cell lung cancer though the use of biomarkers and receptor-targeted therapies.
Bibliographic record
Abstract
8040 Background: We analyzed the risk of clinical trial failure duringnon-small cell lung cancer (NSCLC) drug development between 1998 and 2012. Methods: NSCLC drug development was investigated using trial disclosures from publically available resources. Compounds were excluded from the analysis if they began phase I clinical testing before 1998 and if they did not use treatment relevant endpoints. Analysis was conducted in regards to treatment indication, compound classification and mechanism of action. Costs of clinical drug development for advanced NSCLC were calculated using industry data and assumptions, a 9% yearly discount rate and assuming a clinical trial length of 2.5 years for phase I trials, 4 years for phase II trials, 5 years for phase III trials and an average of 5 phase I trials, 7 phase II trials, and 4 phase III trials per approved drug. All funding costs are in US dollars (USD). Results: 2,407 clinical trials met search criteria. 676 trials and 199 unique compounds met our inclusion criteria. The likelihood, or cumulative clinical trial success rate, that a new drug would pass all phases of clinical testing and be approved was found to be 11%, which is less than the expected industry aggregate rates (16.5%). The success of phase III trials was found to be the biggest obstacle for drug approval with a success rate of only 28%. Biomarker-guided targeted therapies (with a success rate of 62%) and receptor targeted therapies (with a success rate of 31%) were found to have the highest likelihood of success in clinical trials. The risk-adjusted cost for NSCLC clinical drug development was calculated to be 1.89 billion US dollars. Use of biomarkers decreased drug development cost by 26% to 1.4 billion US dollars. Potential savings may be even higher if fewer clinical trials are required for successful development. Conclusions: Physicians that enroll patients in NSCLC trials should prioritize their participation in clinical trial programs that involve either a biomarker or receptor targeted therapy, which appear to carry the best chances for a successful treatment response. Given the high adjusted cost of clinical testing alone in NSCLC, efforts to mitigate the risk of trial failure need to explore these factors more fully.
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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 | 0.085 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".