Progress in physiologically based pharmacokinetic-pharmacodynamic models of amino acids in humans
Bibliographic record
Abstract
PURPOSE OF REVIEW: Amino acids are critical to health, serving both as constituents of proteins and in signaling and metabolism. Amino acids are consumed as nutrients, supplements, and nutraceuticals. Much remains to be learned about amino acid function. Physiologically based pharmacokinetic and pharmacodynamic (PBPK-PD) modeling is an emerging tool for studying their complex biology. This review highlights recent PBPK-PD models developed to study amino acid physiology and metabolism and discusses their potential for addressing unresolved questions in the field. RECENT FINDINGS: PBPK-PD models provided several insights. They revealed the interplay between the mechanisms by which leucine governs skeletal muscle protein metabolism in healthy adults. The models also identified optimal dosing regimens of amino acid supplementation to treat sickle-cell disease and recurrent hypoglycemia, and to minimize drug side effects in seizure disorders. Additionally, they characterized the effects of novel anticancer drugs that seek to deprive cancer cells of amino acids. Future models may inform treatment strategies for sarcopenia, characterize distinctions between animal- and plant-based nutrition, and inform nutrient-drug interactions in Parkinson's disease. SUMMARY: PBPK-PD models are powerful tools for studying amino acid physiology and metabolism, with applications to nutrition, pharmacology, and their interplay.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".