Species selection for nonclinical safety assessment of drug candidates: Examples of current industry practice
Bibliographic record
Abstract
In drug development, nonclinical safety assessment is pivotal for human risk assessment and support of clinical development. Selecting the relevant/appropriate animal species for toxicity testing increases the likelihood of detecting potential effects in humans, and although recent regulatory guidelines state the need to justify or dis-qualify animal species for toxicity testing, individual companies have developed decision-processes most appropriate for their molecules, experience and 3Rs policies. These generally revolve around similarity of metabolic profiles between toxicology species/humans and relevant pharmacological activity in at least one species for New Chemical Entities (NCEs), whilst for large molecules (biologics) the key aspect is similarity/presence of the intended human target epitope. To explore current industry practice, a questionnaire was developed to capture relevant information around process, documentation and tools/factors used for species selection. Collated results from 14 companies (Contract Research Organisations and pharmaceutical companies) are presented, along with some case-examples or over-riding principles from individual companies. As the process and justification of species selection is expected to be a topic for continued emphasis, this information could be adapted towards a harmonized approach or best practice for industry consideration.
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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 | 0.120 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".