Inhalable nanoparticle-based drug delivery system for non-small cell lung cancer therapy: promises and challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Lung cancer remains the leading cause of cancer-related deaths worldwide, with non-small cell lung cancer (NSCLC) accounting for approximately 85% of cases. Conventional therapies, including surgery, chemotherapy, and targeted treatments, often show limited efficacy, especially in advanced stages. Pulmonary drug delivery systems (PDDSs) have gained attention as an innovative approach for NSCLC management by enabling direct drug administration to the lungs. This method improves therapeutic outcomes while minimizing systemic side effects. Advances in nanotechnology have further enhanced the potential of inhalable chemotherapy, with nanoparticles offering notable benefits such as improved drug solubility, stability, and protection from premature degradation. Additionally, their small size and modifiable surfaces allow for targeted delivery to tumor cells, increasing treatment efficacy and reducing harm to healthy tissues. This review outlines the formulations used in PDDSs for NSCLC, evaluates suitable inhalation devices, and examines the physicochemical characteristics of nanoparticles critical for pulmonary delivery. It also discusses both passive and active targeting mechanisms and explores current challenges in inhalable nanomedicine. Emphasis is placed on recent innovations in inhalable nanomaterials, providing a comprehensive and current perspective on their application in NSCLC therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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 it