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Record W1991426846 · doi:10.1021/ac050761q

Inhibitor Screening Using Immobilized Enzyme Reactor Chromatography/Mass Spectrometry

2005· article· en· W1991426846 on OpenAlexafffund
Richard Hodgson, Travis R. Besanger, Michael A. Brook, John D. Brennan

Bibliographic record

VenueAnalytical Chemistry · 2005
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsChemistryChromatographySubstrate (aquarium)AnalyteElutionMass spectrometryTandem mass spectrometryInosineMonolithic HPLC columnEnzymeHigh-performance liquid chromatographyBiochemistry

Abstract

fetched live from OpenAlex

We describe the coupling of capillary-scale monolithic enzyme reactor columns directly to a tandem mass spectrometer for screening of enzyme inhibitors. A two-channel nanoLC system is used to continuously infuse substrate or substrate/inhibitor mixtures through the column, allowing continuous variation of inhibitor concentration by simply altering the ratio of flow from the two pumps. In the absence of inhibitor, infusion of substrate leads to formation of product, and both substrate and product ions can be simultaneously monitored in a quantitative manner by MS/MS. The presence of inhibitor leads to a decrease in product and an increase in substrate concentration in the column eluent. Knowing the product/substrate ratio and the total analyte concentration (P + S), the concentration of product eluting, and hence the relative enzyme activity, can be determined. Both IC50 and KI values can then be obtained by direct MS detection of the effect of inhibitors on relative activity. Inhibitor screening is demonstrated using reusable, sol-gel derived, monolithic capillary columns containing adenosine deaminase, directly interfaced to ESI-MS/MS. On-column enzyme activity was assessed by monitoring inosine and adenosine elution. It is shown that the method can be used for automated screening of the effects of compound mixtures on ADA activity and to determine the KI value of the known inhibitor, erythro-9-(2-hydroxy-3-nonyl)adenine, even when the compound is present within a mixture.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.226
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations60
Published2005
Admission routes2
Has abstractyes

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