Selecting the Best Drugs for Phase I Clinical Development and Beyond
Bibliographic record
Abstract
Attrition rates of drugs from human entry to regulatory approval are far higher in anticancer drugs than those for nononcology indications. In the era of molecular therapeutics that results from a deeper understanding in cancer biology and advancing technologies, the number of compounds available for clinical testing is likely to continue to increase. Although the main objectives of phase I trials are to characterize toxicities of new agents and to determine the recommended dose for phase II development, most phase I studies are now designed to provide some early signal on preliminary efficacy as secondary objectives. The "go-no-go" decision to further develop a drug, or not, is now often pushed forward to the phase I setting. Thus, there is a need for investigators to be able to critically review the preclinical data available in order to determine which drugs should advance on the developmental path. This review highlights the intrinsic characteristics of a drug and the relevant data to be collected during its preclinical assessment, which may maximize the chances of success in clinical testing and eventual regulatory approval.
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 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 | 0.037 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".