Have physiologically-based pharmacokinetic models delivered?
Bibliographic record
Abstract
The application of in silico methods for predicting internal dosimetry of a compound has gained attention in the past few years from academia, government and industry. One such method based on both compound- and organism-specific information is physiologically-based pharmacokinetic (PBPK) modeling. Numerous promises surrounding the potential of PBPK models to guide drug development (DD) and human health risk assessment (HHRA) have been made with primary areas of application being incorporation of in vitro data for pharmacokinetic prediction in early drug development, interspecies scaling, intra-human scaling and, of special interest, prediction of drug-drug interaction potential. This article addresses the question 'Have physiologically-based pharmacokinetic models delivered?' through analysis of its promises and accomplishments in real-world situations. Progress on PBPK model use in DD and HHRA has been demonstrated, especially in the area of interspecies and adult-to-children scaling, although its actual application is not reflected in the number of published works. Future advances will depend on continued model development as well as integration of PBPK models with models of response and/or disease. More importantly, increased training along with managerial and regulatory support is imperative to the continued integration of PBPK modeling in both HHRA and DD.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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; both teacher heads agree on what is shown here.
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".