A Pharmacokinetic Overview of Nanotechnology-Based Drug Delivery Systems: An ADME-Oriented Approach
Bibliographic record
Abstract
With the extensive progress in nanotechnology-based drug delivery systems, pharmacokinetic evaluations have gained much attention from researchers as a central part of the study of these systems. Because the fulfillment of any therapeutic goal(s) by a novel drug delivery system requires that the absorption, distribution, metabolism, and excretion (ADME) be considered from the early stages of the system design to the final clinical evaluations, extensive knowledge of the pharmacokinetic aspects related to ADME is a crucial part of research in this field. The main objectives of the nanotechnology-based drug delivery systems from a pharmacokinetic viewpoint are (1) an improved drug-release profile in vivo, (2) enhanced drug absorption, (3) site-directed drug distribution, (4) a modified drug metabolism pattern, (5) prolonged drug residence time in body (e.g., in blood circulation), and (6) delayed and/or decreased renal excretion of the drug. Accordingly, the purpose of the current review is to present an insightful summary of pharmacokinetic analyses of nanotechnology-based drug delivery systems along with a critical review of recent findings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".