Scaling factors to inform <i>in vitro</i> - <i>in vivo</i> extrapolation from preclinical and livestock animals: state of the field and recommendations for development of missing data
Bibliographic record
Abstract
physiologically based pharmacokinetic (IVIVE-PBPK) modeling approaches assists for prediction of first-in animal or human trials. These approaches are underpinned by the scaling factors: microsomal protein per gram (MPPG) and cytosolic protein per gram (CPPG). In addition, IVIVE-PBPK has significant application in the reduction and refinement of live animal models in research. While human scaling factors are well-defined, many preclinical and livestock species remain poorly elucidated or uncharacterized. The MPPG parameter for liver (MPPGL) is the best characterized across all species and is well-defined for mouse, rat, and dog models. The MPPG parameters for Kidney (MPPGK) and intestine (MPPGI), are however; relatively indefinite for most species. Similarly, CPPG scaling factors for liver, kidney, and intestine (CPPGL/CPPGK/CPPGI) are generally sparse in all species. In addition to generation of mathematical values for scaling factors, methodological and animal-specific considerations, such as age, sex, and strain differences, have not yet been comprehensively described. Here, we review the current state-of-the-field for microsomal and cytosolic scaling factors, including highlighting areas that may need further description and development, with the intention of drawing attention to key knowledge gaps. The intention is to promote improved accuracy and precision in IVIVE-PBPK, concordance between laboratories, and stimulate work in underserved, but increasingly vital areas.
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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 | 0.106 | 0.212 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 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".