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Noteworthy Idiosyncrasies of 1α,25‐Dihydroxyvitamin D <sub>3</sub> Kinetics for Extrapolation from Mouse to Man

2020· article· en· W3016742862 on OpenAlexaff
K. Sandy Pang, Keumhan Noh, Qi Joy Yang, Lavtej Sekhon, Holly P. Quach, Edwin C.Y. Chow

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCalcitriolCYP27A1EndocrinologyCalcitriol receptorInternal medicineVitamin D and neurologyMetaboliteCYP24A1ChemistryCholecalciferolBiologyMetabolismMedicine

Abstract

fetched live from OpenAlex

Calcitriol or 1α,25‐dihydroxyvitamin D 3 [1,25(OH) 2 D 3 ] 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 D 3 [25(OH)D 3 ] by CYP27A1 and CYP2R1 in the liver, then to 1,25(OH) 2 D 3 by a specific, mitochondrial enzyme, CYP27B1 (1α‐hydroxylase) that is present primarily in the kidney. Degradation of both metabolites is mostly carried out by the more ubiquitous mitochondrial enzyme, CYP24A1. Despite that calcitriol inhibits its formation and degradation, allometric scaling revealed strong interspecies (mouse, rat, dog and human) correlations of the net calcitriol clearance (CL from dose/AUC ∞ ), production rate (PR), and basal, plasma calcitriol concentration (C BL ) with body weight (BW). A closer scrutiny of the collated literature data revealed that the basal plasma concentration of calcitriol was not taken into consideration in the estimation of AUC ∞ (with subtraction of C BL ) and CL after exogenous calcitriol dosing in both animals and humans. This led to an overestimation of AUC ∞ and underestimation of CL. After correction, the allometric equations [plots of log(CL) or log(PR) versus log(body weight) or log(BW)] were: CL=0.654xBW 0.807 and PR=36.6xBW 0.718 . The CL of calcitriol was further influenced by diseased states, and was found to be reduced in chronic kidney disease but increased in cancer when compared to healthy humans. These changes, however, were not discerned with allometric scaling since all clinical data appeared as a cluster of closely similar values on the allometric plot. PBPK‐PD (physiologically‐based pharmacokinetic‐pharmacodynamic) modeling (Ramakrishnan et al 2016), however, confirmed the dynamic interactions between calcitriol and Cyp27b1/Cyp24a1, showing the increase in CL as well as decrease in PR in a dose‐dependent fashion for the intravenous, murine data of Quach et al (2015). The PBPK model was successful in predicting data from the escalating oral and intravenous doses of calcitriol given to cancer patients upon scale‐up.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.002

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.

Opus teacher head0.035
GPT teacher head0.287
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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