A novel nanoparticle formulation for targeted drug delivery in cardiovascular diseases
Bibliographic record
Abstract
Cardiovascular diseases are the leading cause of mortality across the globe. Of the various cardiovascular diseases, congestive heart failure is the most prevalent. Heart failure has no permanent cure, yet certain treatments and lifestyle changes can help enhance the patients’ quality of life. Congestive heart failure is commonly treated by delivering drugs which lower the blood pressure and improve the heart’s pumping action. However, their use has limitations such as lack of specificity, toxicity, low retention time in the body along with side effects such as hypotension, arrhythmia, nausea, vomiting etc. It is anticipated that the targeted delivery of drugs would help address and overcome these limitations. This thesis focuses on the design and development of a nanoparticle-based formulation for the targeted delivery of the drug, milrinone, for congestive heart failure treatment. The action of milrinone helps in improving the contraction ability and functioning of the failing heart. The nanoparticles were prepared from the protein, human serum albumin, which was surface functionalized to bind the angiotensin II type 1 (AT1) peptide. The peptide-tagged nanoparticles were designed to target the AT1 receptors, found to be overexpressed on the myocardium under heart failure conditions, therefore facilitating higher nanoparticle uptake and drug delivery to the heart. The nanoparticles were spherical with a particle size between 100-200 nm and negative surface charge, indicating high physical stability. The in vitro characterization studies showed that the nanoparticle formulation was target-specific, biodegradable, biocompatible and suitable for use in vivo. The in vivo pharmacokinetics and tissue distribution studies of the targeted nanoparticle formulation revealed superior drug delivery and release, with improvement in the retention time of milrinone compared to the non-targeted drug. The treatment efficacy of this formulation was validated using a rat model of congestive heart failure, where it was found to be safe and effective in improving the cardiac function and contractility. Therefore, this targeted nanoparticle formulation delivering milrinone exhibits immense potential for use in congestive heart failure and related cardiovascular disease
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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) | 0.001 | 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".