Preclinical Pharmacology and Pharmacokinetics of Inhaled Hexadecyl-Treprostinil (C16TR), a Pulmonary Vasodilator Prodrug
Bibliographic record
Abstract
This article describes the preclinical pharmacology and pharmacokinetics (PK) of hexadecyl-treprostinil (C16TR), a prodrug of treprostinil (TRE), formulated in a lipid nanoparticle (LNP) for inhalation as a pulmonary vasodilator. C16TR showed no activity (>10 µ M) in receptor binding and enzyme inhibition assays, including binding to prostaglandin E 2 receptor 2, prostaglandin D 2 receptor 1, prostaglandin I 2 receptor, and prostaglandin E 2 receptor 4; TRE potently bound to each of these prostanoid receptors. C16TR had no effect (up to 200 nM) on platelet aggregation induced by ADP in rat blood. In hypoxia-challenged rats, inhaled C16TR-LNP produced dose-dependent (0.06–6 µ g/kg), sustained pulmonary vasodilation over 3 hours; inhaled TRE (6 µ g/kg) was active at earlier times but lost its effect by 3 hours. Single- and multiple-dose PK studies of inhaled C16TR-LNP in rats showed proportionate dose-dependent increases in TRE C max and area under the curve (AUC) for both plasma and lung; similar results were observed for dog plasma levels in single-dose PK studies. In both species, inhaled C16TR-LNP yielded prolonged plasma TRE levels and a lower plasma TRE C max compared with inhaled TRE. Inhaled C16TR-LNP was well tolerated in rats and dogs; TRE-related side effects included cough, respiratory tract irritation, and emesis and were seen only after high inhaled doses of C16TR-LNP in dogs. In guinea pigs, inhaled TRE (30 µ g/ml) consistently produced cough, but C16TR-LNP (30 µ g/ml) elicited no effect. These results demonstrate that C16TR-LNP provides long-acting pulmonary vasodilation, is well tolerated in animal studies, and may necessitate less frequent dosing than inhaled TRE with possibly fewer side effects.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.001 |
| 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".