Contrasting toxicokinetic evaluations and interspecies pharmacokinetic scaling approaches for small molecules and biologics: applicability to biosimilar development
Bibliographic record
Abstract
1. A 2-fold threshold typically used for prediction accuracy of interspecies scaling of clearance (CL) may be too liberal when using pharmacokinetic similarity in animals to advance biosimilar candidate selection for clinical testing. The purpose of this review is to identify interspecies scaling methods for use in de-risking biosimilar development prior to clinical testing. 2. Scaling approaches for predicting macromolecule CL were identified through literature review. Reports that evaluated predicted and observed human CLs for ≥5 individual compounds were considered. Absolute average fold-error (AAFE) was calculated for each method along with the proportion of compounds with individual fold-error values within a tighter threshold of 0.7-1.3. 3. Traditional simple allometry with a minimum of three species and the rule of exponents performed inconsistently with some groups of compounds resulting in a greater than 2-fold error (i.e. AAFE > 2). For monoclonal antibodies (mAbs), simplified allometric approaches employing a single species (monkey) with a fixed exponent of 0.85 consistently resulted in lower AAFEs and a higher proportion of compounds within the tighter range of 0.7-1.3. 4. For macromolecules, and particularly mAbs, employing single-species monkey "simplified" allometric approaches with a fixed exponent of 0.85 may be more appropriate than traditional allometric approaches.
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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 | 0.031 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".