The Effect of Biomarkers on Clinical Trial Risk in Gastric Cancer
Bibliographic record
Abstract
OBJECTIVES: This study examined clinical trial success rates for new drug developments in gastric cancer since 1998. We also examined the clinical trial design features that may mitigate the risk of clinical trial failure. MATERIALS AND METHODS: Clinical trial data was obtained from clinicaltrials.gov. Drugs were included if they entered testing between January 1, 1998 and January 1, 2022 and were excluded if they did not have a completed phase I trial or treated secondary effects of gastric cancer. Transition probabilities were calculated for each phase and compared with industry averages. Success rates were determined based on biomarker usage, drug target, type of therapy, and drug chemistry. RESULTS: Upon screening 1990 trials, 219 drugs met our inclusion criteria. The probability of a drug completing all phases of testing and obtaining FDA approval was 7%, which is below the 11% industry average. The use of biomarkers in clinical development resulted in nearly a 2-fold increase in the cumulative success rate. Biologics also exhibited higher success rates (17%) as opposed to small molecules (1%). This was true even when we compared both drug types that shared the same target. When comparing only receptor-targeted therapies, biologics (62%) continued to outperform small molecules (18%). Similarly, when narrowed down to drugs targeting solely HER2 receptors, biologics continued to prevail (64% vs. 24%). CONCLUSIONS: Implementing biomarkers, receptor-targeted therapies, and biologics in clinical development improves clinical trial success rates in gastric cancer. Thus, physicians should prioritize the enrollment of gastric cancer patients in clinical trials that incorporate the aforementioned features.
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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 | 0.325 | 0.614 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".