Self-Assembling Peptides: Potential Role in Tumor Targeting
Bibliographic record
Abstract
This review focuses on the application of two classes of peptides, i.e., self-assembling peptides (SAPs) and cell-targeting peptides (CTPs), in the development of nanocarrier delivery systems. Self-assembling peptides are emerging in a wide range of biomedical and bioengineering applications and fall into several classes, including peptide amphiphilies, bolaamphiphile peptides, cyclic peptides, and ionic complementary peptides, which can be found naturally or synthesized. The advantage of synthesizing peptides is that their self-assembling properties can be exploited to form desirable structures for various applications. Another, unique property of self-assembling peptides, is stimuli-responsibility in different environments including various pHs, temperatures, ionic strengths, etc. These characteristics make peptides applicable in a wide range of biomaterials in drug discovery. This study reviews the design principles of well-known selfassembling peptides, as well as their physical/chemical properties. In addition, it discusses the therapeutic cancer-targeting peptides and current combinatorial peptide library methods used to identify targeting peptides. Cancer-targeting peptides can target either tumor cell surfaces or tumor vasculature. The RGD peptide is one of the first tumor-targeting peptides that can bind to self-assembling peptides or any other nanocarrier to improve the therapeutic efficiency of targeting drug delivery systems. Keywords: Cell-targeting peptide, combinatorial peptide library, ionic-complementary, phage-display, self-assembling peptide, stimuli-responsive, tumor targeting, vasculature, therapeutic cancer-targeting peptides, therapeutic efficiency, drug delivery systems, nanocarrier delivery systems, metastases, nanotechnology, bolaamphiphile peptides
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".