Population Pharmacokinetic Analysis to Assist Dose Selection of the l-Ornithine Salt of Phenylacetic Acid
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: L-Ornithine phenylacetate is an intravenous formulation of the L-ornithine salt of phenylacetic acid under development for the treatment of hepatic encephalopathy. Very limited clinical data in patients are available, with a phase II study in target patients not designed for dose finding, to support phase III dose selection in a global development program. The objective of the present population pharmacokinetic modeling and simulation was to evaluate dose selection for target patient populations with a low body weight, ethnicity, and hepatic impairment in a global clinical study. METHODS: A population pharmacokinetic model was developed based on plasma concentrations of L-ornithine, phenylacetic acid, and phenylacetylglutamine data from four clinical trials in healthy subjects and patients with stable cirrhosis or hospitalized adult patients with liver cirrhosis and hepatic encephalopathy. A covariate analysis was conducted to identify source of variability to support dose selection for global clinical development of L-ornithine phenylacetate. Phenylacetylglutamine formation in the pharmacokinetic model also quantified pharmacodynamic effects measured by ammonia removal. RESULTS: Body weight and hepatic function were significant covariates determining phenylacetic acid exposure. After accounting for body weight, there was no difference between tested Caucasian and Asian populations in phenylacetic acid exposure. Renal dysfunction significantly reduced phenylacetylglutamine excretion. However, renal impairment had no impact on plasma phenylacetic acid and free ammonia levels. Exploratory modeling suggested that L-ornithine might enhance the removal of ammonia. CONCLUSIONS: With a flat dosing algorithm, special consideration must be given to patients with a small body size (i.e., body weight ≤ 50 kg) and severe hepatic impairment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".