The characterization of preclinical evidence for agents entering clinical development
Bibliographic record
Abstract
The translation of basic biological discoveries into clinical applications that improve human health remains a slow, expensive, and failure-prone endeavor despite tremendous advances in genetics and molecular biology. Drug development failures have immense costs, including the burden of subjecting patient-volunteers to needless harms and the squandering of scarce resources that could be applied to more promising areas of research. To better understand why so many drugs that appear promising in preclinical studies perform so poorly in later trials, this thesis first developed a search method to capture a cohort of 371 novel agents entering clinical translation between 2000-2003 and then sought to determine the accessibility of preclinical in vivo efficacy evidence for these interventions. In general, there was a large volume of animal evidence available in the published literature (n=2735; 55/agent). However, the number of studies published prior to the first published account of human testing was modest (7/agent). This was especially true for agents that went on to receive regulatory approval; licensed drugs had, on average, half as many animal studies published prior to initial clinical trial publication (per agent) than unlicensed drugs. Furthermore, 16% of our sample of novel interventions had no antecedent animal data available whatsoever. This suggests that data withholding might take place earlier in development â perhaps especially where clinical promise is greatest. However, our ability to obtain a large volume of preclinical studies suggests the feasibility of synthesizing preclinical evidence to better understand why some drugs fail in clinical development.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".