Comparison of pharmacokinetics of L-carnitine, Acetyl-L-carnitine and Propionyl-Lcarnitine after single oral administration of L-carnitine in healthy volunteers
Bibliographic record
Abstract
PURPOSE: To investigate the pharmacokinetics of L-carnitine (LC) and its analogues, acetyl-L-carnitine (ALC) and propionyl-L-carnitine (PLC) in healthy volunteers after single L-carnitine administration. METHODS: Liquid L-carnitine (2.0 g) was administered orally as a single dose in 12 healthy subjects. Plasma and urine concentrations of L-carnitine, ALC and PLC were detected by HPLC. RESULTS: The maximum plasma concentration (Cmax) and area under the curve (AUC 0-infinity) of L-carnitine was 84.7+/-25.2 micromol x L(-1) x h and 2676.4+/-708.3 micromol x L(-1) x h, respectively. The elimination half-life of L-carnitine and the time required to reach the Cmax (Tmax) was 60.3+/-15.0 and 3.4+/-0.46 h, respectively. The Cmax of ALC (12.9+/-5.5 micromol x L(-1)) and PLC (5.08+/-3.08 micromol x L(-1)) was lower than L-carnitine (P < 0.01), so as the AUC 0-infinity (166.2+/-77.4 and 155.6+/-264.2 micromol x L(-1) x h, respectively, P < 0.01). The half-life of ALC (35.9+/-28.9h) and PLC (25.7+/-30.3 h) was also shorter than L-carnitine (P < 0.01). The 24h accumulated urinary excretion of L-carnitine, ALC and PLC were 613.5+/-161.7, 368.3+/-134.8 and 61.3+/-37.8 micromol, respectively. CONCLUSION: L-carnitine has a greater maximum plasma concentration than ALC and PLC. L-carnitine also has a longer half-life than ALC and PLC. These data may have important implications in the designing of dosing regimens for L-carnitine or its analogues, such as ALC or PLC.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".