Physiologically Based Pharmacokinetic (<scp>PBPK</scp>) Modeling: Usefulness and Applications
Bibliographic record
Abstract
Abstract Physiologically based pharmacokinetic (PBPK) models are increasingly being used to describe and define more meaningful parameters that relate to physiology, anatomy, and biochemistry in the prediction of pharmacokinetic (PK) profiles and tissue concentration–time profiles of the parent drug and metabolites and to provide mechanistic insight into drug dynamics. Physiological data (blood flow rates and tissue volumes), physical data (protein binding and tissue partition coefficients), and biochemical data (Michaelis–Menten parameters for transporters and enzymes,Vmax/Km) are the information required for building a PBPK model. The whole‐body PBPK model approach is extremely useful to understand sequential metabolism, the kinetics of metabolites, and examine effects of the transporter and enzyme interplay on the blood and target organ exposures of the drug and its metabolites. The application of PBPK models allows one to make predictions of the exposure in target sites, pharmacological activity, or toxicity and the effects of age, pregnancy, disease states, and drug–drug interactions (DDIs). In this chapter, many cases for the application and usefulness of PBPK models are summarized.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
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".