Transmembrane pH-Gradient Liposomes To Treat Cardiovascular Drug Intoxication
Bibliographic record
Abstract
Injectable scavenging nanocarriers have been proposed as detoxifying agents when there are no specific antidotes to treat pharmacological overdoses. They act by capturing the drug in situ, thereby restricting distribution in tissues. In the clinic, the only systems used for that purpose are parenteral lipid emulsions, which are relatively inefficient in terms of uptake capacity. In this study, we investigated long-circulating liposomes with a transmembrane pH gradient as treatment for diltiazem intoxication. The unique ion-trapping properties of the vesicles toward ionizable compounds were exploited to sequester the drug in the bloodstream and limit its pharmacological effect. After in vitro optimization of the formulation, the in vivo scavenging properties of the liposomes were demonstrated by examining the drug's pharmacokinetics. The reduced volume of distribution and increased area under the plasma concentration versus time curve in animals treated with liposomes indicated limited tissue distribution. The vesicles exerted a similar but more pronounced effect on deacetyl-diltiazem, the principal active metabolite of the drug. This in vivo uptake of both drug and metabolite altered the overall pharmacological outcome. In rats receiving an intravenous bolus of diltiazem, the liposomes tempered the hypotensive decline and maintained higher average blood pressure for 1 h. The detoxifying action of liposomes was even stronger when the rats received higher doses of the drug via perfusion. In conclusion, the present work provided clear evidence that liposomes with a transmembrane pH gradient are able to change the pharmacokinetics and pharmacodynamics of diltiazem and its metabolite and confirmed their potential as efficient detoxifying nanocarriers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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.
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 teacher head, 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".