Current Trends of Practices in Nonclinical Toxicology: An Industry Survey
Bibliographic record
Abstract
The growth in drug development over the past years reflects significant advancements in basic sciences and a greater understanding of molecular pathways of disease. Benchmarking industry practices has been important to enable a critical reflection on the path to evolve pharmaceutical testing, and the outcome of past industry surveys has had some impact on best practices in testing. A survey was provided to members of SPS, ACT, and STP. The survey consisted of 37 questions and was provided to 2550 participants with a response rate of 24%. Most respondents (∼75%) came from the US and Europe. The survey encompassed multiple topics encountered in nonclinical testing of pharmaceuticals. The most frequent target indications were oncology (69%), inflammation (55%), neurology/psychiatry/pain (46%), cardiovascular (44%), and metabolic diseases (39%). The most frequent drug-induced toxicology issues confronted were hepatic, hematopoietic, and gastrointestinal. Toxicological effects that impacted the no observed adverse effect level (NOAEL) were most frequently based on histopathology findings. The survey comprised topics encountered in the use of biomarkers in nonclinical safety assessment, most commonly those used to assess inflammation, cardiac/vascular, renal, and hepatic toxicity as well as common practices related to the assessment of endocrine effects, carcinogenicity, genotoxicity, juvenile and male-mediated developmental and female reproductive toxicity. The survey explored the impact of regulatory meetings on program design, application of the 3 Rs, and reasons for program delays. Overall, the survey results provide a broad perspective of current practices based on the experience of the scientific community engaged in nonclinical safety assessment.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".