Monitoring methotrexate in clinical samples from cancer patients during chemotherapy with a LSPR-based competitive sensor
Bibliographic record
Abstract
A competitive binding assay based on localized surface plasmon resonance (LSPR) of folic acid-functionalized gold nanoparticles (FA-AuNPs) and human dihydrofolate reductase enzyme (hDHFR) was developed to detect nanomolar to micromolar concentrations of the widely applied anti-cancer drug, methotrexate (MTX). By the nature of the competitive assay for MTX, the LSPR shift from specific binding between FA-AuNPs and the free enzyme was inversely proportional to the concentration of MTX. In addition, the dynamic range for MTX was tuned from 10(-11) to 10(-6) M by varying the concentration of hDHFR from 1 to 100 nM. Inter-day reproducibility and recovery of MTX spiked in phosphate buffer saline (PBS) were excellent. Potential interferents such as FA, trimethoprim (TMP) and 4-amino-4-deoxy-N-methylpteroic acid (DAMPA) did not occur in the concentration range of interest for MTX. Clinical samples of human serum from patients undergoing MTX chemotherapy were analyzed following a simple solid-phase extraction step to isolate MTX from the serum matrix, with a limit of detection of 155 nM. Validation of the LSPR method was carried out in comparison to Fluorescence Polarization Immunoassay (FPIA), a commonly used method in clinical settings, and LC-MS/MS, a reference technique. The results of the LSPR competitive assay compared well to FPIA and LC-MS/MS, with a slope of 2.4 and 1.1, respectively, for the correlation plots. The method established herein is intended for therapeutic drug monitoring (TDM) of MTX levels in patients undergoing chemotherapy to ensure safety and efficacy of the treatment.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".