Safety pharmacology 2023 and implementation of the ICH E14/S7B Q&A guidance document
Bibliographic record
Abstract
This editorial prefaces the annual themed issue on safety pharmacology (SP) methods published since 2004 in the Journal of Pharmacological and Toxicological Methods (JPTM). We highlight here the content derived from the recent 2022 Safety Pharmacology Society (SPS) and Canadian Society of Pharmacology and Therapeutics (CSPT) joint meeting held in Montreal, Quebec, Canada. The meeting also generated 179 abstracts (reproduced in the current volume of JPTM). As in previous years the manuscripts reflect various areas of innovation in SP including a comparison of the sensitivity of cross-over and parallel study designs for QTc assessment, use of human-induced pluripotent stem cell (hi-PSC) neuronal cell preparations for use in neuropharmacological safety screening, and hiPSC derived cardiac myocytes in assessing inotropic adversity. With respect to the latter, we anticipate the emergence of a large data set of positive and negative controls that will test whether the imperative to miniaturize, humanize and create a high throughput process is offset by any loss of precision and accuracy.
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 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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 0.025 |
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".