Self-Assembling Phenomena: From Excipients to Drugs
Bibliographic record
Abstract
The universality of self-assembly can be illustrated by an extensive range of examples found in all categories of materials present in Nature.Inspired by its principles, multiple scientists have synthesized new self-assembled materials, many of them having pharmaceutical and biomedical applications.This review tries to bring some significant insights related to how minor modifications at molecular level are translated, via self-assembling phenomena, in major macroscopic changes expanding the range of practical applications.Based on the principle of weak forces interactions which didn't involve chemical modifications, self-assembly is an interdisciplinary approach able to generate complex aggregates, some of them comparable to living structures.The advantages of self-assembly consist in generation of organized systems that cannot be made by other procedures and in understanding the phenomena occurring in living organisms.Various examples of self-assembly phenomena were given for starch and its derivatives with the aim to illustrate how the same chemical entity can generate by minor modifications an extensive range of new compounds with divers features.Based on the principle of weak interactions between building blocks, the self-assembly proved its benefits in preparation of more efficient drug delivery systems and anti-cancer treatments.The review also includes some methods that can be used for characterization of self-assembled compounds discussed.More recently, the approaches based on supramolecular self-assembly processes are also used for fabrication of nanometer-scale objects, one of the major fields of research for the current technology.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".