Novel Milrinone Nanoformulation for Use in Cardiovascular Diseases: Preparation and <i>in Vitro</i> Characterization
Bibliographic record
Abstract
Cardiovascular diseases are the leading causes of mortality across the globe. Over the years, various drug formulations and delivery methods have been tested for cardiac repair. Milrinone (MRN) is a widely known cardiac inotrope drug used for the treatment of congestive heart failure in patients, however, its efficacy is limited. This study is the first to report the design of a novel MRN-nanoformulation using human serum albumin nanoparticles (HSA-NPs). The HSA-NPs exhibit promising drug delivery characteristics, such as target specificity, nonimmunogenicity, biocompatibility, and enhanced bioavailability. This article describes a MRN-nanoformulation design for in vitro drug release, cellular uptake, biocompatibility, and other features. The MRN-nanoformulation was prepared by the ethanol desolvation technique and key parameters were optimized to obtain a desired particle size of 154.2 ± 5.8 nm, zeta potential of -29.5 ± 2.9 mV, and a drug encapsulation efficiency of 41.1 ± 1.7%. Molecular docking studies have revealed that MRN binds in the hydrophobic cavity of HSA, which has also been indicated by circular dichroism and enzyme-mediated drug release studies in the presence of trypsin, pepsin, proteinase K, protease, and cathepsin D. The intracellular uptake of fluorescently tagged MRN-HSA-NPs using HUVEC and H9c2 cells was evaluated by flow cytometry. The nanoparticle toxicity results indicated that MRN-HSA-NPs show significantly lower cytotoxicity and higher cell viability ( P < 0.0001) as compared to the MRN-lactate drug in HUVEC (61.6 ± 3.7% vs 36.2 ± 2.9%) and H9c2 (58.8 ± 5.7% vs 18.8 ± 4.9%) cells. These studies indicate that the novel MRN-nanoformulation offers better drug delivery procedures than currently used methods and has potential in treatment of congestive heart failure and other cardiovascular diseases.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".