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Record W2180163584 · doi:10.3791/52424

Quantification of the Immunosuppressant Tacrolimus on Dried Blood Spots Using LC-MS/MS

2015· article· en· W2180163584 on OpenAlexfundno aff
Touraj Shokati, Nicholas Bodenberger, Holly Gadpaille, Björn Schniedewind, Alexander A. Vinks, Wenlei Jiang, Rita R. Alloway, Uwe Christians

Bibliographic record

VenueJournal of Visualized Experiments · 2015
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsnot available
FundersU.S. Public Health ServiceNational Institutes of HealthU.S. Food and Drug AdministrationHamilton Health Sciences Foundation
KeywordsChromatographyTacrolimusFormic acidDried blood spotTherapeutic drug monitoringWhole bloodChemistryProtein precipitationAnalyteLiquid chromatography–mass spectrometrySelected reaction monitoringMass spectrometryTandem mass spectrometryTransplantationPharmacokineticsPharmacologyMedicineSurgery

Abstract

fetched live from OpenAlex

The calcineurin inhibitor tacrolimus is the cornerstone of most immunosuppressive treatment protocols after solid organ transplantation in the United States. Tacrolimus is a narrow therapeutic index drug and as such requires therapeutic drug monitoring and dose adjustment based on its whole blood trough concentrations. To facilitate home therapeutic drug and adherence monitoring, the collection of dried blood spots is an attractive concept. After a finger stick, the patient collects a blood drop on filter paper at home. After the blood is dried, it is mailed to the analytical laboratory where tacrolimus is quantified using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) in combination with a simple manual protein precipitation step and online column extraction. For tacrolimus analysis, a 6-mm disc is punched from the saturated center of the blood spot. The blood spot is homogenized using a bullet blender and then proteins are precipitated with methanol/0.2 M ZnSO4 containing the internal standard D2,(13)C-tacrolimus. After vortexing and centrifugation, 100 µl of supernatant is injected into an online extraction column and washed with 5 ml/min of 0.1 formic acid/acetonitrile (7:3, v:v) for 1 min. Hereafter, the switching valve is activated and the analytes are back-flushed onto the analytical column (and separated using a 0.1% formic acid/acetonitrile gradient). Tacrolimus is quantified in the positive multi reaction mode (MRM) using a tandem mass spectrometer. The assay is linear from 1 to 50 ng/ml. Inter-assay variability (3.6%-6.1%) and accuracy (91.7%-101.6%) as assessed over 20 days meet acceptance criteria. Average extraction recovery is 95.5%. There are no relevant carry-over, matrix interferences and matrix effects. Tacrolimus is stable in dried blood spots at RT and at +4 °C for 1 week. Extracted samples in the autosampler are stable at +4 °C for at least 72 hr.

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 categoriesnone
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 score0.010
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

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

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 head0.098
GPT teacher head0.456
Teacher spread0.358 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations28
Published2015
Admission routes1
Has abstractyes

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