Noteworthy idiosyncrasies of 1α,25‐dihydroxyvitamin D<sub>3</sub> kinetics for extrapolation from mouse to man: Commentary
Bibliographic record
Abstract
Abstract Calcitriol or 1,25‐dihydroxyvitamin D3 [1,25(OH)2D3] is the active ligand of the vitamin D receptor (VDR) that plays a vital role in health and disease. Vitamin D is converted to the relatively inactive metabolite, 25‐hydroxyvitamin D3 [25(OH)D3], by CYP27A1 and CYP2R1 in the liver, then to 1,25(OH)2D3 by a specific, mitochondrial enzyme, CYP27B1 (1α‐hydroxylase) that is present primarily in the kidney. The degradation of both metabolites is mostly carried out by the more ubiquitous mitochondrial enzyme, CYP24A1. Despite the fact that calcitriol inhibits its formation and degradation, allometric scaling revealed strong interspecies correlation of the net calcitriol clearance (CL estimated from dose/AUC∞), production rate (PR), and basal, plasma calcitriol concentration with body weight (BW). PBPK‐PD (physiologically based pharmacokinetic‐pharmacodynamic) modeling confirmed the dynamic interactions between calcitriol and Cyp27b1/Cyp24a1 on the decrease in the PR and increase in CL in mice. Close scrutiny of the literature revealed that basal levels of calcitriol had not been taken into consideration for estimating the correct AUC∞ and CL after exogenous calcitriol dosing in both animals and humans, leading to an overestimation of AUC∞ and underestimation of the plasma CL. In humans, CL was decreased in chronic kidney disease but increased in cancer. Collectively, careful pharmacokinetic data analysis and improved definition are achieved with PBPK‐PD modeling, which embellishes the complexity of dose, enzyme regulation, and disease conditions. Allometric scaling and PBPK‐PD modeling were applied successfully to extend the PBPK model to predict calcitriol kinetics in cancer patients.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.007 | 0.014 |
| 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".