High-Throughput Cytochrome P450 Inhibition Assays Using Laser Diode Thermal Desorption-Atmospheric Pressure Chemical Ionization-Tandem Mass Spectrometry
Bibliographic record
Abstract
This paper describes the development of a high-throughput method for the analysis of cytochrome P450 (CYP) inhibition assay incubation samples using laser diode thermal desorption interfaced with atmospheric pressure chemical ionization mass spectrometry (LDTD-APCI-MS). Data for the CYP isoforms 3A4, 2D6, 2C9, and 1A2 from competitive inhibition assays are shown. The potential for inhibition of the CYP isoforms was measured by monitoring the level of the metabolites 6beta-hydroxytestosterone (3A4), dextrorphan (2D6), 4'-hydroxydiclofenac (2C9), and acetaminophen (1A2) formed in the presence of drug candidates using an eight-point titration. The analytical method involves plating of the inhibition samples on specially designed 96-well plates with stainless steel bottoms, followed by direct analysis using the LDTD source. Validation of the LDTD-MS method was performed by testing for interferences, reproducibility, dynamic range, ion suppression, and the ability of the source to produce comparable results to previously validated LC-MS methods. IC50 values for each CYP isoform using 33 different test compounds showed excellent agreement between LDTD-APCI-MS and LC-MS methods and literature values where available. Assay analysis time using the LDTD-APCI source is reduced to less than 30 min for a single 96-well plate compared to greater than 10 h using the LC-MS method. The LDTD-APCI-MS and LC-MS methods and results are compared and limitations and future potential for LDTD-APCI-MS are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".