Targeted Nanomedicines for Cancer Therapy, From Basics to Clinical Trials
Bibliographic record
Abstract
Traditional systemic chemotherapy involves the wide distribution of drug molecules in the body, causing toxic side effects in the healthy tissues and limiting the therapeutic dose required at the site of drug action. In order to decrease side effects and increase the drug efficacy, recent research on chemotherapy focuses on drug targeting. Targeted therapy can be achieved by several mechanisms including; 1) using an antibody as a drug that is specific to a disease biomarker, 2) using an antibody (or peptide) as a targeting agent conjugated to the drug molecule, 3) delivering the drug molecules to the target tissue in a nano-carrier with or without the targeting agent attached on its surface. The third approach involves the nanomedicines that can be targeted to diseased tissues by both passive (extravasating at diseased sites due to leaky vasculature) and active (specific interaction of the targeting agent with disease biomarker) targeting mechanisms. In this review we will cover the passively targeted nanomedicines prepared using nano drug carriers. Ideally the carrier particle should be in the right size (1-100nm), stable enough to prevent drug leakage during circulation, and safe not to cause any damage to healthy tissues. Competition for all these properties generated many different types of materials to be used as nanodrug delivery systems. After a brief review of most commonly used drug carriers, we discuss the clinical use of the targeted nanomedicines with regard to their pharmacokinetic and pharmacodynamics properties, and how these properties vary from conventional formulations providing free drugs in the circulation after administration.
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How this classification was reachedexpand
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 | 0.021 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".