Comparative Study of Three Methods for Affinity Measurements: Capillary Electrophoresis Coupled with UV Detection and Mass Spectrometry, and Direct Infusion Mass Spectrometry
Bibliographic record
Abstract
We present affinity capillary electrophoresis and mass spectrometry (ACE-MS) as a comprehensive separation technique for label-free solution-based affinity analysis. The application of ACE-MS for measuring affinity constants between eight small molecule drugs [ibuprofen, s-flurbiprofen, diclofenac, phenylbutazone, naproxen, folic acid, resveratrol, and 4,4'-(propane-1,3-diyl) dibenzoic acid] and β-cyclodextrin is described. We couple on-line ACE with MS to combine the separation and kinetic capability of ACE together with the molecular weight and structural elucidation of MS in one system. To understand the full potential of ACE-MS, we compare it with two other methods: Direct infusion mass spectrometry (DIMS) and ACE with UV detection (ACE-UV). After the evaluation, DIMS provides less reliable equilibrium dissociation constants than separation-based ACE-UV and ACE-MS, and cannot be used solely for the study of noncovalent interactions. ACE-MS determines apparent dissociation constants for all reacting small molecules in a mixture, even in cases when drugs overlap with each other during separation. The ability of ACE-MS to interact, separate, and rapidly scan through m/z can facilitate the simultaneous affinity analysis of multiple interacting pairs, potentially leading to the high-throughput screening of drug candidates.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".