Species selection for nonclinical safety assessment of drug candidates: Examples of current industry practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it