Meeting Report Session 3: Neurobiomarkers STP 44th Annual Symposium 2025 Development and Utility of Neurobiomarkers in Nonclinical Toxicology Studies
Bibliographic record
Abstract
The objective of the third session (Neurobiomarkers) in the 2025 Society of Toxicologic Pathology (STP) symposium was to provide an overview of different types of existing neural biomarkers, highlight the value of novel biomarkers to detect and/or predict nervous system changes in preclinical species, and provide perspectives on their translational potential. These biomarkers can also help evaluate the efficacy of new drugs, monitor neurological diseases, and better characterize neuropathology findings. The lectures in this session featured distinguished experts in their respective scientific disciplines such as electrophysiology, molecular pathology tools to characterize pathology lesions in the visual pathways, fluid-based biomarkers, the use of MRI imaging to visualize test article delivery to the CNS of monkeys, and quantifying DRG changes using techniques like stereology, micro-CT, and nano-CT. In this session, there was also a presentation about immunohistochemical stains performed to evaluate ketamine-induced neurotoxicity in neonatal Sprague Dawley rats as a model for pediatric anesthesia. The integration of neural biomarkers in nonclinical studies in conjunction with a dedicated histopathology evaluation provides the necessary scientific data to better predict and derisk neurotoxicity in clinical studies.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.097 | 0.046 |
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".