Study of Ligand−Receptor Binding Using SPME: Investigation of Receptor, Free, and Total Ligand Concentrations
Bibliographic record
Abstract
The theoretical background and practical approaches for studying ligand-receptor (protein) binding by solid phase microextraction (SPME) are investigated, along with methods for simultaneous calculation of receptor, free, and total ligand concentrations. With the introduction of new extraction phases (restricted access materials, molecularly imprinted polymers, and immobilized antibodies), SPME allows better separation of small molecules of ligand from larger molecules of receptor, and improved accuracy. This sample preparation method based on nonexhaustive extraction is well suited as a general method to study and quantify systems involving multiple equilibriums, with significant advantages over currently used methods. SPME was used previously for the determination of protein binding constants, but only with conventional extraction phases and in simple cases, with a 1:1 combination ratio between the ligand and the receptor or when negligible depletion conditions were met. The new theoretical approach presented in this study allows the quantification of any binding equilibrium, regardless of the extent of depletion. Restricted-access particles are used as extraction phase, and if the amount of receptor is limited, selected regions of the binding curve may be obtained using a single sample, with a volume as low as 10 muL. The equations developed here are simple and independent of the analytical method used for the quantification of the amount of ligand. Three different practical approaches are presented: the method of multiple standard solutions, the method of successive extractions from the same sample and the method of successive additions to the same sample. The usefulness of this novel approach is demonstrated by using it to determine the binding parameters of some selected drugs to human serum albumin. These parameters are subsequently used to calculate albumin, free drug, and total drug concentrations from unknown mixtures. The results are in good agreement with previously published data. Quantification of the amount of ligand extracted by SPME is done by liquid chromatography coupled with tandem mass spectrometry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".