Incidence and nature of testicular toxicity findings in pharmaceutical development
Bibliographic record
Abstract
BACKGROUND: Testicular toxicity (TT) is a sporadic and challenging issue in pharmaceutical drug development. Efforts to develop TT screening assays or biomarkers have been overshadowed by consortium efforts to predict drug-induced toxicities such as hepatic injury, which are encountered more frequently. METHODS: To gauge the current state of the field and to prioritize future TT activities, the International Life Sciences Institute-Health and Environmental Sciences Institute Developmental and Reproductive Toxicology (DART) Technical Committee sponsored a survey to better understand the incidence and nature of TT findings encountered during drug development. RESULTS: Highlights from the 16 survey respondents include: (1) Although preclinical TT was encountered relatively infrequently, half of the participants observed repeated problems with TT during pharmaceutical development, (2) despite control measures such as use of sexually mature animals to diminish confounding effects of spurious lesions, interpretation of TT remains a challenge, (3) "traditional" evaluation tools such as hormonal monitoring and newer approaches such as -omics are utilized to investigate testicular changes, and (4) an understanding of the risk and relevance of TT findings is achieved through joint consideration of factors such as species specificity, potential mode of action, and safety margins. CONCLUSIONS: TT remains a relatively uncommon but persistent challenge in pharmaceutical development. Although current preclinical TT approaches appear to be effective in limiting the occurrence of pharmaceutical candidate attrition in clinical trials, improved biomarker or screening platforms would allow companies to identify TT at an earlier stage, thus decreasing the time and resources expended on safety evaluation of pharmaceutical candidates.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".