Translational Challenges From Nonclinical to Clinical Program: Case Study Examples
Bibliographic record
Abstract
Drug discovery and development is a complex, lengthy, and expensive process that takes on average 10-15 years and approximately $1-2 billion USD for approval of a new drug. While the studies needed to support clinical development are generally outlined in guidance documents, there is much less guidance on how to translate the nonclinical data into clinical designs. Nonclinical studies are performed to conduct the First-in-Human clinical trial, which is the first major milestone to advance new promising drug candidates, and are conducted primarily to determine the safe dose range for clinical development. Resolving how to move forward, and even when to move forward, requires significant cross-functional collaboration with pathologists, ADME scientists, biologists, and clinical staff. There are many reasons why drug candidates may fail; these could be as simple as insufficient understanding of the nature of the translational process, failure to effectively integrate the data from different pharmacologically relevant species, or erosion of the margin of safety during chronic toxicology studies. The case studies described here were designed to help participants in the 2024 American College of Toxicology (ACT) Continuing Education course "Translational Challenges from Nonclinical to Clinical Program: Case Study Examples" to improve their skills in managing translational challenges from nonclinical to clinical program encountered during drug development.
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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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".