Targeted therapy using nanotechnology: focus on cancer
Bibliographic record
Abstract
Recent advances in nanotechnology and biotechnology have contributed to the development of engineered nanoscale materials as innovative prototypes to be used for biomedical applications and optimized therapy. Due to their unique features, including a large surface area, structural properties, and a long circulation time in blood compared with small molecules, a plethora of nanomaterials has been developed, with the potential to revolutionize the diagnosis and treatment of several diseases, in particular by improving the sensitivity and recognition ability of imaging contrast agents and by selectively directing bioactive agents to biological targets. Focusing on cancer, promising nanoprototypes have been designed to overcome the lack of specificity of conventional chemotherapeutic agents, as well as for early detection of precancerous and malignant lesions. However, several obstacles, including difficulty in achieving the optimal combination of physicochemical parameters for tumor targeting, evading particle clearance mechanisms, and controlling drug release, prevent the translation of nanomedicines into therapy. In spite of this, recent efforts have been focused on developing functionalized nanoparticles for delivery of therapeutic agents to specific molecular targets overexpressed on different cancer cells. In particular, the combination of targeted and controlled-release polymer nanotechnologies has resulted in a new programmable nanotherapeutic formulation of docetaxel, namely BIND-014, which recently entered Phase II clinical testing for patients with solid tumors. BIND-014 has been developed to overcome the limitations facing delivery of nanoparticles to many neoplasms, and represents a validated example of targeted nanosystems with the optimal biophysicochemical properties needed for successful tumor eradication.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".