Elucidating the role of surface chemistry on cationic phosphorus dendrimer–siRNA complexation
Bibliographic record
Abstract
In the field of dendrimers targeting small interfering RNA (siRNA) delivery, dendrimer structural properties, such as the flexibility/rigidity ratio, play a crucial role in the efficiency of complexation. However, advances in organic chemistry have enabled the development of dendrimers that differ only by a single atom on their surface terminals. This is the case for cationic phosphorus dendrimers functionalized with either pyrrolidinium (DP) or morpholinium (DM) terminal groups. This small change was shown to strongly affect the dendrimer-siRNA complexation, leading to more efficient anti-inflammatory effects in the case of DP. Reasons for this different behavior can hardly be inferred only by biological in vitro and in vivo experiments due to the high number of variables and complexity of the investigated biological system. However, an understanding of how small chemical surface changes may completely modify the overall dendrimer-siRNA complexation is a significant breakthrough towards the design of efficient dendrimers for nucleic acid delivery. Herein, we present experimental and computational approaches based on isothermal titration calorimetry and molecular dynamics simulations to elucidate the molecular reasons behind different efficiencies and activities of DP and DM. Results of the present research highlight how chemical surface modifications may drive the overall dendrimer-siRNA affinity by influencing enthalpic and entropic contributions of binding free energy. Moreover, this study elucidates molecular reasons related to complexation stoichiometry that may be crucial in determining the dendrimer complexation efficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".