Continuous Flow Immobilized Enzyme Reactor–Tandem Mass Spectrometry for Screening of AChE Inhibitors in Complex Mixtures
Bibliographic record
Abstract
A method is described for identifying bioactive compounds in complex mixtures based on the use of capillary-scale monolithic enzyme-reactor columns for rapid screening of enzyme activity. A two-channel nanoLC system was used to continuously infuse substrate coupled with automated injections of substrate/small molecule mixtures, optionally containing the chromogenic Ellman reagent, through sol-gel derived acetylcholinesterase (AChE) doped monolithic columns. This is the first report of AChE encapsulated in monolithic silica for use as an immobilized enzyme reactor (IMER), and the first use of such IMERs for mixture screening. AChE IMER columns were optimized to allow rapid functional screening of compound mixtures based on changes in the product absorbance or the ratio of mass spectrometric peaks for product and substrate ions in the eluent. The assay had robust performance and produced a Z' factor of 0.77 in the presence of 2% (v/v) DMSO. A series of 52 mixtures consisting of 1040 compounds from the Canadian Compound Collection of bioactives was screened and two known inhibitors, physostigmine and 9-aminoacridine, were identified from active mixtures by manual deconvolution. The activity of the compounds was confirmed using the enzyme reactor format, which allowed determination of both IC(50) and K(I) values. Screening results were found to correlate well with a recently published fluorescence-based microarray screening assay for AChE inhibitors.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 | 0.002 |
| 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".