Size-Switching Starch Nanoparticle-based Nanoassemblies for Improving Drug Delivery
Bibliographic record
Abstract
In recent decades, a variety of nanoparticle drug delivery systems (NP DDS) – nanometer-scaled materials physically or covalently interacting with therapeutics – has been developed to overcome biological barriers, improve the half-life, reduce toxicity, and improve the efficacy of conventional drug delivery. However, many NP DDS fail to translate to the clinic. While this is in part due to immense heterogeneity within many disease types across individuals, the conflicting size and surface chemistries required in the “drug delivery pathway” (i.e. to avoid the clearance mechanisms and unintended tissues in the body, then to reach and specifically enter target tissues) also pose a significant challenge. Recent advances in the field of drug delivery have created size- and surface-switching nanoparticles that overcome biological barriers. For example, large (100 – 200 nm) NPs are adequate at evading corporeal defense mechanisms, while small (< 50 nm) NPs can actively enter cancerous tissue. Further, release profiles of drug-loaded NP DDS must be tailored to stay within a narrow therapeutic window to prevent toxic effects. This thesis highlights the synthesis of “nanoassemblies”, an NP DDS that contains small, drug-loaded starch nanoparticles (SNPs) within a larger nanogel matrix. Nanoassemblies are chemically tuned to reach specific targets via different administration routes (notably, cancerous tissues via systemic administration and brain tissue via intranasal administration). Furthermore, therapeutic-loaded SNPs are released under endogenous (pH, redox) or exogenous (ultrasound) stimuli for disease-specific release kinetics, allowing for deeper penetration into tumor cores or through the nose-to-brain pathway as required. Both the physicochemical characterization of these nanoassemblies as well as in vitro and in vivo experiments have been performed to assess the efficacy of nanoassemblies in biological systems and how they may provide performance improvements over non-assembled SNPs. As such, nanoassemblies show great promise in overcoming complex biological barriers to ultimately improve drug delivery in clinical applications.
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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 | 0.001 |
| 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".