Diversity-oriented synthesis of second generation guanidinium-rich transporters toward cell-selective penetration
Bibliographic record
Abstract
Cell-penetrating peptides (CPPs) hold significant promise for intracellular delivery of various cargo molecules such as therapeutics. However, the lack of selectivity remains a critical challenge and limits the clinical application of CPPs. Using an automated peptide synthesizer, we generated a diversity-oriented library of 256 peptidomimetics containing four modified peptoid guanidine-bearing monomers incorporated alternatively with four α-amino acids. These α-amino acids were chosen to enhance lipophilic interactions with the cell membrane (Phe, 2Nal) or to bear pH-sensitive properties (His), which could enhance cancer cell selectivity. The synthesized library exhibits selective internalization, with an average selectivity index (SI) of 1.49 for HeLa cells in comparison to non-cancerous HEK293 cells. Compounds 155 and 187, containing three His residues and either Phe or 2Nal, show high cellular uptake in HeLa cells (64.6% and 75.7%, respectively) and possess an SI of 2.7 and 2.9, respectively, at the tested dose of 5 μM. Altogether, these findings highlight the use of diversity-oriented library synthesis to identify cell-permeable candidates as well as their potential for targeted cellular delivery and enhanced specificity.
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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".