Principles of Safety Pharmacology
Bibliographic record
Abstract
Safety Pharmacology is a rapidly developing discipline that uses the basic principles of pharmacology in a regulatory-driven process to generate data to inform risk/benefit assessment. The aim of Safety Pharmacology is to characterize the pharmacodynamic/pharmacokinetic (PK/PD) relationship of a drug's adverse effects using continuously evolving methodology. Unlike toxicology, Safety Pharmacology includes within its remit a regulatory requirement to predict the risk of rare lethal events. This gives Safety Pharmacology its unique character. The key issues for Safety Pharmacology are detection of an adverse effect liability, projection of the data into safety margin calculation and finally clinical safety monitoring. This article sets out to explain the drivers for Safety Pharmacology so that the wider pharmacology community is better placed to understand the discipline. It concludes with a summary of principles that may help inform future resolution of unmet needs (especially establishing model validation for accurate risk assessment). Subsequent articles in this issue of the journal address specific aspects of Safety Pharmacology to explore the issues of model choice, the burden of proof and to highlight areas of intensive activity (such as testing for drug-induced rare event liability, and the challenge of testing the safety of so-called biologics (antibodies, gene therapy and so on.).
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 | 0.026 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
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".