Electrochemical Quantitation of Supramolecular Excipient@Drug Complexation: A General Assay Strategy Based on Competitive Host Binding with Surface-Immobilized Redox Guest
Bibliographic record
Abstract
The macrocyclic cucurbit[7]uril (CB[7]) host has exhibited great application potential as a pharmaceutical excipient due to its versatile abilities to modulate the chemical/physical properties of drug molecules (guests) and to control their in vivo delivery and release (upon complexation). The formation of stable CB[7]@drug complexes is the prerequisite for these promising applications; we report herein a general assay strategy to quantitate the complexation based on competitive binding with surface-immobilized redox guests in conjunction with conventional electrochemical techniques (e.g., cyclic voltammetry). Particularly, by incubating a mixture of CB[7] and a drug molecule with ferrocene (Fc)-terminated self-assembled monolayers (SAMs) on gold, the competitive host@guest binding between the CB[7]@drug complex formed in solution and the CB[7]@Fc complex formed on surface can be quantified with direct cyclic voltammetry measurements. On the basis of the known concentrations of CB[7]/drug and electrochemically determined surface densities of free/complexed Fc groups, the formation constant of CB[7]@drug complex can be determined. With several drug molecules as examples, we have demonstrated the capability of this method for quantitative studies of the formation of supramolecular excipient@drug complexes that are of interest in pharmaceutical and biomedical sciences. More importantly, this work promises a general assay strategy that allows electrochemical quantitation of a wide range of electro-inactive analytes based on the competitive supramolecular host@guest binding at redox-tagged molecular interfaces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".